Subjects -> ENGINEERING (Total: 2688 journals)
    - CHEMICAL ENGINEERING (229 journals)
    - CIVIL ENGINEERING (237 journals)
    - ELECTRICAL ENGINEERING (176 journals)
    - ENGINEERING (1325 journals)
    - ENGINEERING MECHANICS AND MATERIALS (452 journals)
    - HYDRAULIC ENGINEERING (56 journals)
    - INDUSTRIAL ENGINEERING (98 journals)
    - MECHANICAL ENGINEERING (115 journals)

ENGINEERING (1325 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 1205 Journals sorted by number of followers
Composite Structures     Hybrid Journal   (Followers: 244)
IEEE Spectrum     Full-text available via subscription   (Followers: 219)
Composites Part B : Engineering     Hybrid Journal   (Followers: 218)
ACS Nano     Hybrid Journal   (Followers: 181)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 173)
Composites Science and Technology     Hybrid Journal   (Followers: 150)
IEEE Geoscience and Remote Sensing Letters     Hybrid Journal   (Followers: 148)
IEEE Instrumentation & Measurement Magazine     Hybrid Journal   (Followers: 148)
IEEE Communications Magazine     Full-text available via subscription   (Followers: 139)
IEEE Engineering Management Review     Full-text available via subscription   (Followers: 117)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 112)
IEEE Transactions on Control Systems Technology     Hybrid Journal   (Followers: 111)
IEEE Transactions on Instrumentation and Measurement     Hybrid Journal   (Followers: 106)
IEEE Transactions on Signal Processing     Hybrid Journal   (Followers: 92)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 88)
IEEE Industry Applications Magazine     Full-text available via subscription   (Followers: 82)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 78)
IEEE Transactions on Engineering Management     Hybrid Journal   (Followers: 74)
Engineering Failure Analysis     Hybrid Journal   (Followers: 68)
IEEE Microwave Magazine     Full-text available via subscription   (Followers: 63)
IEEE Signal Processing Letters     Hybrid Journal   (Followers: 60)
IEEE Transactions on Reliability     Hybrid Journal   (Followers: 53)
Experimental Techniques     Hybrid Journal   (Followers: 51)
IET Radar, Sonar & Navigation     Open Access   (Followers: 50)
IEEE Transactions on Microwave Theory and Techniques     Hybrid Journal   (Followers: 49)
Control Engineering Practice     Hybrid Journal   (Followers: 46)
IEEE Journal of Selected Topics in Signal Processing     Hybrid Journal   (Followers: 43)
Biotechnology Progress     Hybrid Journal   (Followers: 42)
IEEE Potentials     Full-text available via subscription   (Followers: 42)
IEEE Journal on Selected Areas in Communications     Hybrid Journal   (Followers: 39)
Heat Transfer Engineering     Hybrid Journal   (Followers: 36)
IET Microwaves, Antennas & Propagation     Open Access   (Followers: 35)
International Journal for Numerical Methods in Engineering     Hybrid Journal   (Followers: 35)
IEEE Microwave and Wireless Components Letters     Hybrid Journal   (Followers: 35)
Digital Signal Processing     Hybrid Journal   (Followers: 34)
IEEE Transactions on Knowledge and Data Engineering     Hybrid Journal   (Followers: 31)
AIChE Journal     Hybrid Journal   (Followers: 31)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 31)
Computers & Geosciences     Hybrid Journal   (Followers: 30)
Flow, Turbulence and Combustion     Hybrid Journal   (Followers: 30)
Coastal Management     Hybrid Journal   (Followers: 29)
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 28)
GPS Solutions     Hybrid Journal   (Followers: 28)
Fluid Dynamics     Hybrid Journal   (Followers: 27)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 27)
Géotechnique     Hybrid Journal   (Followers: 27)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 27)
IEEE Transactions on Power Delivery     Hybrid Journal   (Followers: 26)
Applied Energy     Partially Free   (Followers: 26)
Advances in Engineering Software     Hybrid Journal   (Followers: 26)
IEEE Journal of Solid-State Circuits     Full-text available via subscription   (Followers: 24)
Corrosion Science     Hybrid Journal   (Followers: 23)
Engineering & Technology     Hybrid Journal   (Followers: 22)
IET Image Processing     Open Access   (Followers: 22)
Intermetallics     Hybrid Journal   (Followers: 21)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 21)
IEEE Transactions on Electronics Packaging Manufacturing     Hybrid Journal   (Followers: 21)
IET Signal Processing     Open Access   (Followers: 21)
IEEE Transactions on Circuits and Systems II: Express Briefs     Hybrid Journal   (Followers: 20)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 20)
Implementation Science     Open Access   (Followers: 20)
International Communications in Heat and Mass Transfer     Hybrid Journal   (Followers: 19)
Engineering Optimization     Hybrid Journal   (Followers: 19)
International Journal for Numerical Methods in Fluids     Hybrid Journal   (Followers: 19)
Electrophoresis     Hybrid Journal   (Followers: 18)
IET Circuits, Devices & Systems     Open Access   (Followers: 18)
IEEE/ACM Transactions on Computational Biology and Bioinformatics     Hybrid Journal   (Followers: 18)
IEEE Transactions on Intelligent Transportation Systems     Hybrid Journal   (Followers: 17)
Experiments in Fluids     Hybrid Journal   (Followers: 17)
Computational Geosciences     Hybrid Journal   (Followers: 17)
International Journal of Adhesion and Adhesives     Hybrid Journal   (Followers: 17)
Integration     Hybrid Journal   (Followers: 16)
IEEE Transactions on Energy Conversion     Hybrid Journal   (Followers: 16)
Engineering Geology     Hybrid Journal   (Followers: 16)
European Journal of Mass Spectrometry     Hybrid Journal   (Followers: 16)
Energy Conversion and Management     Hybrid Journal   (Followers: 15)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 15)
Coastal Engineering     Hybrid Journal   (Followers: 15)
IEEE Transactions on Magnetics     Hybrid Journal   (Followers: 14)
IEEE Journal of Biomedical and Health Informatics     Hybrid Journal   (Followers: 14)
IEEE Transactions on Automation Science and Engineering     Full-text available via subscription   (Followers: 13)
IEEE Transactions on Evolutionary Computation     Hybrid Journal   (Followers: 13)
Electromagnetics     Hybrid Journal   (Followers: 13)
Computers and Geotechnics     Hybrid Journal   (Followers: 12)
IEEE Transactions on Semiconductor Manufacturing     Hybrid Journal   (Followers: 12)
IET Renewable Power Generation     Open Access   (Followers: 12)
Human Factors in Ergonomics & Manufacturing     Hybrid Journal   (Followers: 12)
IEEE Transactions on Professional Communication     Hybrid Journal   (Followers: 11)
Biomedical Engineering     Hybrid Journal   (Followers: 11)
IEEE Transactions on Education     Hybrid Journal   (Followers: 11)
CIRP Annals - Manufacturing Technology     Hybrid Journal   (Followers: 11)
IEEE Journal of Oceanic Engineering     Hybrid Journal   (Followers: 11)
Heat Transfer - Asian Research     Hybrid Journal   (Followers: 10)
International Journal of Antennas and Propagation     Open Access   (Followers: 10)
Proceedings of the Institution of Civil Engineers - Geotechnical Engineering     Hybrid Journal   (Followers: 10)
IEEE Transactions on Nuclear Science     Hybrid Journal   (Followers: 10)
IEEE Transactions on Plasma Science     Hybrid Journal   (Followers: 10)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 9)
Fuel Cells Bulletin     Full-text available via subscription   (Followers: 9)
Computational Optimization and Applications     Hybrid Journal   (Followers: 9)
Annals of Science     Hybrid Journal   (Followers: 9)
European Journal of Engineering Education     Hybrid Journal   (Followers: 9)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 9)
Biomedical Microdevices     Hybrid Journal   (Followers: 8)
IEEE Technology and Society Magazine     Full-text available via subscription   (Followers: 8)
Fuel Cells     Hybrid Journal   (Followers: 8)
Adaptive Behavior     Hybrid Journal   (Followers: 8)
Proceedings of the Institution of Civil Engineers - Bridge Engineering     Hybrid Journal   (Followers: 8)
Energy Engineering     Full-text available via subscription   (Followers: 8)
IEEE Transactions on Advanced Packaging     Full-text available via subscription   (Followers: 8)
Clay Minerals     Hybrid Journal   (Followers: 8)
Continuum Mechanics and Thermodynamics     Hybrid Journal   (Followers: 8)
Applied Catalysis A: General     Hybrid Journal   (Followers: 7)
International Journal of Applied Ceramic Technology     Hybrid Journal   (Followers: 7)
Basin Research     Hybrid Journal   (Followers: 7)
Discrete Optimization     Full-text available via subscription   (Followers: 7)
Designs, Codes and Cryptography     Hybrid Journal   (Followers: 7)
IEEE Journal of Selected Topics in Quantum Electronics     Hybrid Journal   (Followers: 7)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
Biomicrofluidics     Open Access   (Followers: 7)
Geothermics     Hybrid Journal   (Followers: 7)
Fuel and Energy Abstracts     Full-text available via subscription   (Followers: 7)
IEEE Vehicular Technology Magazine     Full-text available via subscription   (Followers: 7)
Catalysis Communications     Hybrid Journal   (Followers: 7)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 7)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computing and Visualization in Science     Hybrid Journal   (Followers: 6)
Fusion Engineering and Design     Hybrid Journal   (Followers: 6)
Applied Clay Science     Hybrid Journal   (Followers: 6)
Composite Interfaces     Hybrid Journal   (Followers: 6)
Formal Methods in System Design     Hybrid Journal   (Followers: 6)
Acta Geotechnica     Hybrid Journal   (Followers: 6)
Advances in OptoElectronics     Open Access   (Followers: 6)
International Journal of Adaptive Control and Signal Processing     Hybrid Journal   (Followers: 5)
IEEE Transactions on Vehicular Technology     Hybrid Journal   (Followers: 5)
IET Science, Measurement & Technology     Open Access   (Followers: 5)
IEEE Transactions on Applied Superconductivity     Hybrid Journal   (Followers: 5)
International Journal of Architectural Computing     Full-text available via subscription   (Followers: 5)
Finite Fields and Their Applications     Full-text available via subscription   (Followers: 5)
Focus on Powder Coatings     Full-text available via subscription   (Followers: 5)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Proceedings of the Institution of Civil Engineers - Engineering Sustainability     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
Active and Passive Electronic Components     Open Access   (Followers: 5)
Proceedings of the Institution of Civil Engineers - Ground Improvement     Hybrid Journal   (Followers: 4)
Frontiers in Energy     Hybrid Journal   (Followers: 4)
Adsorption     Hybrid Journal   (Followers: 4)
Catalysis Today     Hybrid Journal   (Followers: 4)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 4)
Current Applied Physics     Full-text available via subscription   (Followers: 4)
Fluid Phase Equilibria     Hybrid Journal   (Followers: 4)
Graphs and Combinatorics     Hybrid Journal   (Followers: 4)
Filtration & Separation     Full-text available via subscription   (Followers: 4)
Annals of Pure and Applied Logic     Open Access   (Followers: 4)
Grass and Forage Science     Hybrid Journal   (Followers: 4)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 4)
Informatik-Spektrum     Hybrid Journal   (Followers: 3)
Engineering Computations     Hybrid Journal   (Followers: 3)
European Journal of Combinatorics     Full-text available via subscription   (Followers: 3)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 3)
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 3)
Concurrent Engineering     Hybrid Journal   (Followers: 3)
Focus on Pigments     Full-text available via subscription   (Followers: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Frontiers of Environmental Science & Engineering     Hybrid Journal   (Followers: 3)
Fuzzy Sets and Systems     Hybrid Journal   (Followers: 3)
Catalysis Letters     Hybrid Journal   (Followers: 3)
IET Generation, Transmission & Distribution     Open Access   (Followers: 2)
Historical Records of Australian Science     Hybrid Journal   (Followers: 2)
IET Optoelectronics     Open Access   (Followers: 2)
Assembly Automation     Hybrid Journal   (Followers: 2)
International Journal of Abrasive Technology     Hybrid Journal   (Followers: 2)
Aerobiologia     Hybrid Journal   (Followers: 2)
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 2)
Comptes Rendus : Mécanique     Open Access   (Followers: 2)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
IEEE Latin America Transactions     Full-text available via subscription   (Followers: 2)
Communications in Numerical Methods in Engineering     Hybrid Journal   (Followers: 2)
ESAIM: Control Optimisation and Calculus of Variations     Open Access   (Followers: 2)
Focus on Surfactants     Full-text available via subscription   (Followers: 2)
Engineering Analysis with Boundary Elements     Hybrid Journal   (Followers: 2)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 1)
Foundations of Science     Hybrid Journal   (Followers: 1)
Forschung     Hybrid Journal   (Followers: 1)
European Journal of Lipid Science and Technology     Hybrid Journal   (Followers: 1)
Antarctic Science     Hybrid Journal   (Followers: 1)
Épités - Épitészettudomány     Full-text available via subscription   (Followers: 1)
Dyes and Pigments     Hybrid Journal   (Followers: 1)
Bautechnik     Hybrid Journal   (Followers: 1)
Biointerphases     Open Access   (Followers: 1)
Designed Monomers and Polymers     Open Access   (Followers: 1)
Color Research & Application     Hybrid Journal   (Followers: 1)
Abstract and Applied Analysis     Open Access   (Followers: 1)
Focus on Catalysts     Full-text available via subscription  
ESAIM: Proceedings     Open Access  
Environmetrics     Hybrid Journal  
COMBINATORICA     Hybrid Journal  
Chinese Science Bulletin     Open Access  
Calphad     Hybrid Journal  
Boundary Value Problems     Open Access  

        1 2 3 4 5 6 7 | Last

Similar Journals
Journal Cover
IEEE Transactions on Intelligent Transportation Systems
Journal Prestige (SJR): 1.175
Citation Impact (citeScore): 5
Number of Followers: 17  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1524-9050
Published by IEEE Homepage  [228 journals]
  • IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY

    • Free pre-print version: Loading...

      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • IEEE Intelligent Transportation Systems Society Information

    • Free pre-print version: Loading...

      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Scanning the Issue

    • Free pre-print version: Loading...

      Authors: Azim Eskandarian;
      Pages: 3892 - 3903
      Abstract: How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A
           Survey

    • Free pre-print version: Loading...

      Authors: Jiexia Ye;Juanjuan Zhao;Kejiang Ye;Chengzhong Xu;
      Pages: 3904 - 3924
      Abstract: In recent years, various deep learning architectures have been proposed to solve complex challenges (e.g. spatial dependency, temporal dependency) in traffic domain, which have achieved satisfactory performance. These architectures are composed of multiple deep learning techniques in order to tackle various challenges in traffic tasks. Traditionally, convolution neural networks (CNNs) are utilized to model spatial dependency by decomposing the traffic network as grids. However, many traffic networks are graph-structured in nature. In order to utilize such spatial information fully, it’s more appropriate to formulate traffic networks as graphs mathematically. Recently, various novel deep learning techniques have been developed to process graph data, called graph neural networks (GNNs). More and more works combine GNNs with other deep learning techniques to construct an architecture dealing with various challenges in a complex traffic task, where GNNs are responsible for extracting spatial correlations in traffic network. These graph-based architectures have achieved state-of-the-art performance. To provide a comprehensive and clear picture of such emerging trend, this survey carefully examines various graph-based deep learning architectures in many traffic applications. We first give guidelines to formulate a traffic problem based on graph and construct graphs from various kinds of traffic datasets. Then we decompose these graph-based architectures to discuss their shared deep learning techniques, clarifying the utilization of each technique in traffic tasks. What’s more, we summarize some common traffic challenges and the corresponding graph-based deep learning solutions to each challenge. Finally, we provide benchmark datasets, open source codes and future research directions in this rapidly growing field.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Comparison and Evaluation of Algorithms for LiDAR-Based Contour Estimation
           in Integrated Vehicle Safety

    • Free pre-print version: Loading...

      Authors: David Michael Mothershed;Robert Lugner;Shahabaz Afraj;Gerald Joy Sequeira;Kilian Schneider;Thomas Brandmeier;Valentin Soloiu;
      Pages: 3925 - 3942
      Abstract: Many nations and organizations are committing to achieving the goal of ‘Vision Zero’ which aims to bring the number of road deaths close to zero by the year 2050. The core of the strategy is a safe transportation system with optimized vehicles and transportation routes. The industry continues to develop integrated safety systems to make vehicles safer, smarter, and more capable in safety-critical scenarios. Passive safety systems are now focusing on pre-crash deployment of restraint systems to better protect vehicle passengers. Current commonly used bounding box methods for the shape estimation of potential crash partners lack the fidelity required for edge case collision detection and advanced crash modeling of future pre-crash technologies. This has led to the development of novel algorithms for vehicle contour estimation in literature. With this work, we present a framework for assessing and comparing different contour estimation algorithms, including a simple bounding box, oriented bounding box, polynomial fit estimation, complemented convex hull, and three-arc fit. Tests on simulated virtual and experimental LiDAR measurements of a simplified vehicle contour have been conducted to determine performance at varying relative angles and distances. It has been concluded that the convex hull and the three-arc methods are the best performing of the studied algorithms, with each having different strengths. The three-arc algorithm offers higher accuracy estimations at low relative angles and near-mid distances, whereas the convex hull method requires low computation time and can provide accurate estimations even at extreme relative angles and distances.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Deep Learning for Visual Tracking: A Comprehensive Survey

    • Free pre-print version: Loading...

      Authors: Seyed Mojtaba Marvasti-Zadeh;Li Cheng;Hossein Ghanei-Yakhdan;Shohreh Kasaei;
      Pages: 3943 - 3968
      Abstract: Visual target tracking is one of the most sought-after yet challenging research topics in computer vision. Given the ill-posed nature of the problem and its popularity in a broad range of real-world scenarios, a number of large-scale benchmark datasets have been established, on which considerable methods have been developed and demonstrated with significant progress in recent years – predominantly by recent deep learning (DL)-based methods. This survey aims to systematically investigate the current DL-based visual tracking methods, benchmark datasets, and evaluation metrics. It also extensively evaluates and analyzes the leading visual tracking methods. First, the fundamental characteristics, primary motivations, and contributions of DL-based methods are summarized from nine key aspects of: network architecture, network exploitation, network training for visual tracking, network objective, network output, exploitation of correlation filter advantages, aerial-view tracking, long-term tracking, and online tracking. Second, popular visual tracking benchmarks and their respective properties are compared, and their evaluation metrics are summarized. Third, the state-of-the-art DL-based methods are comprehensively examined on a set of well-established benchmarks of OTB2013, OTB2015, VOT2018, LaSOT, UAV123, UAVDT, and VisDrone2019. Finally, by conducting critical analyses of these state-of-the-art trackers quantitatively and qualitatively, their pros and cons under various common scenarios are investigated. It may serve as a gentle use guide for practitioners to weigh when and under what conditions to choose which method(s). It also facilitates a discussion on ongoing issues and sheds light on promising research directions.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Detecting the Demand Changes of Bike Sharing: A Bayesian Hierarchical
           Approach

    • Free pre-print version: Loading...

      Authors: Xian Chen;Hai Jiang;
      Pages: 3969 - 3984
      Abstract: Detecting the changes of bike sharing demands, which are evoked by event disturbances, is critical to evaluating the event impacts on a bike sharing system. Although there has been a proliferation of studies that investigate the problem of demand change detection, most, if not all, of them focus exclusively on the short-term changes lasting for hours or few days. In this research, we propose to detect the abrupt, substantial and persistent changes in the changing regularity of daily demands. We develop a Bayesian hierarchical model, where the upper layer captures the state sequence using a Dirichlet process, and the lower layer captures the state-specific changing regularity of daily demands using linear regression. We estimate the parameters of our model based on the Markov Chain Monte Carlo method. We conduct numerical experiments using the publicly available bike sharing records collected in New York. Results show that our model identifies the demand changes evoked by three bike sharing system expansions and significantly improves the average log marginal likelihood per region.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • An Optimal Battery Charging Algorithm in Electric Vehicle-Assisted Battery
           Swapping Environments

    • Free pre-print version: Loading...

      Authors: Haneul Ko;Sangheon Pack;Victor C. M. Leung;
      Pages: 3985 - 3994
      Abstract: In battery swapping environments, electric vehicles (EVs) can play roles as battery providers as well as consumers. In this paper, we propose an optimal battery charging algorithm (OBCA) where a battery swapping station (BSS) charges batteries in its storage with the consideration of the profile of the electricity price and the arrival rates of EVs. To maximize the net profit of BSS while maintaining the battery changing probability above a certain level (i.e., maintaining high quality of service (QoS) of BSS), we formulate a constraint Markov decision process (CMDP) problem and the optimal charging schedule for batteries in BSS is obtained by a linear programming (LP). Evaluation results demonstrate that OBCA with the optimal policy can improve the net profit of BSS up to 418% compared to an electric price-aware scheme while maintaining high QoS of BSS.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Solar Wireless Sensor Nodes for Condition Monitoring of Freight Trains

    • Free pre-print version: Loading...

      Authors: Stefano Cii;Gisella Tomasini;Maria Laura Bacci;Davide Tarsitano;
      Pages: 3995 - 4007
      Abstract: The objective of this work is to present the design and testing of a Wireless Sensor Network, powered by solar energy, to be installed on freight trains with the purpose of performing on-board monitoring operations. A complete Wireless Sensor Network requires a certain number of Wireless Sensor Nodes, installed in significant points of a vehicle, provided with sensors and capable of elaborating raw data, transmitting them via wireless network as synthesis information to an on-board control unit. The on-board control unit periodically communicates the data gathered from different sensors to a ground central control unit through the Internet. Each Wireless Sensor Node needs to be powered independently. To achieve this purpose a small solar panel was used to provide the Wireless Sensor Node with the necessary amount of energy. Integrated circuits were designed for power management, acquisition, elaboration and wireless transmission of data and analyzed in terms of performances and energy consumption. The communication protocol between the Wireless Sensor Node and the control unit was first laboratory-tested and finally the whole system was installed on a real wagon, and on-field tests were conducted for a period of almost one year.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Fail-Safe Multi-Modal Localization Framework Using Heterogeneous
           Map-Matching Sources

    • Free pre-print version: Loading...

      Authors: Soomok Lee;Seung-Woo Seo;
      Pages: 4008 - 4020
      Abstract: A highly accurate and robust real-time localization process is crucial for autonomous driving applications. Numerous methods for localization have been proposed, which combine various kinds of input, such as data from environmental sensors, inertial measurement units (IMU), and the Global Positioning System (GPS). Because reliance on a single environmental sensor is a vulnerable approach, the use of multiple environmental sensors is a better alternative. However, the fusion methods from previous studies have not adequately compensated for the drawbacks due to the lack of sensor diversity nor have the methods considered the fail-safe issue. In this paper, we propose a multi-modal fusion-based localization framework that uses multiple map matching sources. The framework contains two independent map matching sources and integrates them in a stochastic situational analysis model. By applying a probabilistic model, the more reliable map matching between the multiple sources is determined and the system stability is verified via a fail-safe action. A number of experiments with autonomous vehicles within actual driving environments have shown that combining multiple map matching sources yield more robust results than the use of a single map matching.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Effect of Routing Constraints on Learning Efficiency of Destination
           Recommender Systems in Mobility-on-Demand Services

    • Free pre-print version: Loading...

      Authors: Gyugeun Yoon;Joseph Y. J. Chow;Assel Dmitriyeva;Daniel Fay;
      Pages: 4021 - 4036
      Abstract: With Mobility-as-a-Service platforms moving toward vertical service expansion, we propose a destination recommender system for Mobility-on-Demand (MOD) services that explicitly considers dynamic vehicle routing constraints as a form of a “physical internet search engine”. It incorporates a routing algorithm to build vehicle routes and an upper confidence bound based algorithm for a generalized linear contextual bandit algorithm to identify alternatives which are acceptable to passengers. As a contextual bandit algorithm, the added context from the routing subproblem makes it unclear how effective learning is under such circumstances. We propose a new simulation experimental framework to evaluate the impact of adding the routing constraints to the destination recommender algorithm. The proposed algorithm is first tested on a 7 by 7 grid network and performs better than benchmarks that include random alternatives, selecting the highest rating, or selecting the destination with the smallest vehicle routing cost increase. The RecoMOD algorithm also reduces average increases in vehicle travel costs compared to using random or highest rating recommendation. Its application to Manhattan dataset with ratings for 1,012 destinations reveals that a higher customer arrival rate and faster vehicle speeds lead to better acceptance rates. While these two results sound contradictory, they provide important managerial insights for MOD operators.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • CoDriver ETA: Combine Driver Information in Estimated Time of Arrival by
           Driving Style Learning Auxiliary Task

    • Free pre-print version: Loading...

      Authors: Yiwen Sun;Kun Fu;Zheng Wang;Donghua Zhou;Kailun Wu;Jieping Ye;Changshui Zhang;
      Pages: 4037 - 4048
      Abstract: Estimated time of arrival (ETA) is one of the most important services in intelligent transportation systems (ITS). Precise ETA ensures proper travel scheduling of passengers as well as guarantees efficient decision-making on ride-hailing platforms, which are used by an explosively growing number of people in the past few years. Recently, machine learning-based methods have been widely adopted to solve this time estimation problem and become state-of-the-art. However, they do not well explore the personalization information, as many drivers are short of personalized data and do not have sufficient trajectory data in real applications. This data sparsity problem prevents existing methods from obtaining higher prediction accuracy. In this article, we propose a novel deep learning method to solve this problem. We introduce an auxiliary task to learn an embedding of the personalized driving information under multi-task learning framework. In this task, we discriminatively learn the embedding of driving preference that preserves the historical statistics of driving speed. For this purpose, we adapt the triplet network from face recognition to learn the embedding by constructing triplets in the feature space. This simultaneously learned embedding can effectively boost the prediction accuracy of the travel time. We evaluate our method on two large-scale real-world datasets from Didi Chuxing platform. The extensive experimental results on billions of historical vehicle travel data demonstrate that the proposed method outperforms state-of-the-art algorithms.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Efficient Lane-Level Map Building via Vehicle-Based Crowdsourcing

    • Free pre-print version: Loading...

      Authors: Jiangang Shu;Songlei Wang;Xiaohua Jia;Weizhe Zhang;Ruitao Xie;Hejiao Huang;
      Pages: 4049 - 4062
      Abstract: By providing rich context of lane information on roads, lane-level maps play a vital role in intelligent transportation systems. Since Global Positioning Systems (GPS) have been widely applied to vehicles, vehicle-based crowdsourcing offers an economical way to the lane-level map building by collecting and analyzing the GPS trajectories of vehicles. However, existing works cannot directly extract lane-level road information from raw and interleaved crowdsourcing trajectories, and moreover they are time-consuming and inaccurate. In this article, we propose a lane-level map building scheme, which can directly extract lane-level road information from raw crowdsourcing GPS trajectories with both efficiency and accuracy improvement. Consider the global similarity between trajectories, we design an efficient trajectory segmentation and clustering algorithm based on improved discrete Fréchet distance and entropy theory, which can directly and accurately deal with the interleaved and messy trajectories. To improve the efficiency, we employ the Least Square Estimate (LSE) to constrain Gaussian Mixture Model (GMM) and design an efficient and accurate lane-level road information extraction algorithm. Comprehensive comparative experiments and performance evaluation on a real-world trajectory dataset show that the proposed scheme outperforms the state-of-the-art works in terms of both efficiency and accuracy.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • On-Line Train Speed Profile Generation of High-Speed Railway With
           Energy-Saving: A Model Predictive Control Method

    • Free pre-print version: Loading...

      Authors: Weifeng Zhong;Shukai Li;Hongze Xu;Wenjing Zhang;
      Pages: 4063 - 4074
      Abstract: By considering dynamic operational conditions in high-speed railway, this paper focuses on the on-line generation problem of train speed profile with energy-saving. This problem is formulated via the model predictive control framework in a moving-horizon manner, such that the real-time running conditions (e.g., temporary speed restrictions) can be involved in the on-line scheduling process of the train. At each control step, a hybrid scheme combining the energy-efficient and time-optimal train control strategies is proposed to ensure the feasibility of the optimal train control problem within the prediction horizon. The optimal control problem in the horizon is transformed into a multi-phase optimal control problem, which is then solved efficiently on-line using the pseudospectral method. By repeatedly solving the train control problem at each step, the energy-efficient train speed trajectory for the whole trip involving dynamic operational conditions can be obtained on-line. In addition, a delay recovery process is designed to re-schedule the train operation if the delay time during the trip exceeds a given threshold value. Finally, numerical examples using data for a real high-speed railway line are given to demonstrate the effectiveness and robustness of the proposed approach.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Timetable Recovery After Disturbances in Metro Operations: An Exact and
           Efficient Solution

    • Free pre-print version: Loading...

      Authors: Konstantinos Gkiotsalitis;Oded Cats;
      Pages: 4075 - 4085
      Abstract: This study proposes an exact model for timetable recovery after disturbances in the context of high-frequency public transport services. The objective of our model is the minimization of the deviation between the actual headway and the respective planned value. The resulting mathematical program for the rescheduling problem is nonlinear and non-smooth; thus, it cannot be solved to optimality. To rectify this, we reformulate the model using slack variables. The reformulated model can be solved to global optimality in real-time with quadratic programming. We apply the model to real data from the red metro line in Washington D.C. in a series of experiments. In our experiments, we investigate how many upstream trips should be rescheduled to respond to a service disturbance. Our findings demonstrate an improvement potential of service regularity of up to 30% if we reschedule the five upstream trips of a disturbed train.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Graph Neural Network for Robust Public Transit Demand Prediction

    • Free pre-print version: Loading...

      Authors: Can Li;Lei Bai;Wei Liu;Lina Yao;S Travis Waller;
      Pages: 4086 - 4098
      Abstract: Understanding and forecasting mobility patterns and travel demand are fundamental and critical to efficient transport infrastructure planning and service operation. However, most existing studies focused on deterministic demand estimation/prediction/analytics. Differently, this study provides confidence interval based demand forecasting, which can help transport planning and operation authorities to better accommodate demand uncertainty/variability. The proposed Origin-Destination (OD) demand prediction approach well captures and utilizes the correlations among spatial and temporal information. In particular, the proposed Probabilistic Graph Convolution Model (PGCM) consists of two components: (i) a prediction module based on Graph Convolution Network and combined with the gated mechanism to predict OD demand by utilizing spatio-temporal relations; (ii) a Bayesian-based approximation module to measure the confidence interval of demand prediction by evaluating the graph-based model uncertainty. We use a large-scale real-world public transit dataset from the Greater Sydney area to test and evaluate the proposed approach. The experimental results demonstrate that the proposed method is capable of capturing the spatial-temporal correlations for more robust demand prediction against several established tools in the literature.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • SA-YOLOv3: An Efficient and Accurate Object Detector Using Self-Attention
           Mechanism for Autonomous Driving

    • Free pre-print version: Loading...

      Authors: Daxin Tian;Chunmian Lin;Jianshan Zhou;Xuting Duan;Yue Cao;Dezong Zhao;Dongpu Cao;
      Pages: 4099 - 4110
      Abstract: Object detection is becoming increasingly significant for autonomous-driving system. However, poor accuracy or low inference performance limits current object detectors in applying to autonomous driving. In this work, a fast and accurate object detector termed as SA-YOLOv3, is proposed by introducing dilated convolution and self-attention module (SAM) into the architecture of YOLOv3. Furthermore, loss function based on GIoU and focal loss is reconstructed to further optimize detection performance. With an input size of $512times 512$ , our proposed SA-YOLOv3 improves YOLOv3 by 2.58 mAP and 2.63 mAP on KITTI and BDD100K benchmarks, with real-time inference (more than 40 FPS). When compared with other state-of-the-art detectors, it reports better trade-off in terms of detection accuracy and speed, indicating the suitability for autonomous-driving application. To our best knowledge, it is the first method that incorporates YOLOv3 with attention mechanism, and we expect this work would guide for autonomous-driving research in the future.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Multi-Commodity Traffic Signal Control and Routing With Connected Vehicles

    • Free pre-print version: Loading...

      Authors: Felipe De Souza;Rodrigo Castelan Carlson;Eduardo Rauh Müller;Konstantinos Ampountolas;
      Pages: 4111 - 4121
      Abstract: A real-time traffic management policy that integrates traffic signal control and multi-commodity routing of connected vehicles in networks with multiple destinations is developed. The proposed policy is based on a multi-commodity formulation of the store-and-forward model and assumes all vehicles are able to exchange information with the infrastructure. Vehicles share information about their current location and final destination. Based on this information, the strategy determines both optimized signal timings at every intersection and vehicle-specific routing information at every link of the network. The control actions, i.e., signal times and routing information, are updated at every cycle and delivered by a finite horizon optimal control problem cast into a rolling horizon framework. The underlying optimization problem is convex, and thus the method is suitable for real-time operation in large networks. The method is validated via a micro-simulation study in networks with up to twenty intersections and, in all simulations, outperforms a real-time traffic-responsive signal control strategy that is based on a single-commodity store-and-forward model. The scalable computation effort for increasing network sizes and prediction horizon confirms the computational efficiency of the method.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Interdependence in Vehicle-Pedestrian Encounters and its Implications for
           Vehicle Automation

    • Free pre-print version: Loading...

      Authors: Joshua E. Domeyer;John D. Lee;Heishiro Toyoda;Bruce Mehler;Bryan Reimer;
      Pages: 4122 - 4134
      Abstract: Communication among road users smooths interactions, improves efficiency, and mitigates risk. Eye contact and waving may be the most salient of this communication, but more often road users use their movement or position as implicit signals. Vehicle automation may disrupt these signals by introducing unfamiliar or unclear interactions that may not align with other road user expectations. This creates a need to evaluate how effectively vehicle automation communicates and how this affects safety and efficiency. Vehicle automation with effective communication can enter the roadway ecology more naturally and facilitate acceptance across society. We modeled the outcomes of vehicle-pedestrian encounters where drivers yielded for pedestrians. Models of the initial conditions revealed that drivers and pedestrians jointly contribute to safety and efficiency; however, the initial conditions were generally poor predictors, suggesting that dynamic interaction may be an important determinant of those outcomes. An interdependence model of wait times revealed that driver and pedestrian influence on one another varied across traffic control devices. During the nonintersection encounters, pedestrian wait time depended on their own speed and distance when entering the encounter, indicating that they may linger away from the road and choose the encounter conditions. The stop sign encounters showed a division of influence, with pedestrian and driver initial conditions influencing their own wait time. The unprotected encounters showed negotiation, with pedestrian initial conditions influencing the driver wait time. We demonstrate the need and methods to understand interdependence between road users and the implications for vehicle automation communication.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Coordinated Cognitive Risk Control for Bridging Vehicular Radar and
           Communication Systems

    • Free pre-print version: Loading...

      Authors: Shuo Feng;Simon Haykin;
      Pages: 4135 - 4150
      Abstract: As an essential part of the emerging Internet of Things, connected and autonomous vehicles (CAVs) have the potential to reshape future transportation systems and change the commute style in people’s everyday life. Among many vehicular on-board devices, radar system and vehicle-to-vehicle (V2V) communication system are two important pillars for the realization of CAVs. In this paper, the concept of coordinated CRC (C-CRC) is proposed to serve as a cognitive mediator for bridging vehicular radar and communication systems. By establishing a mutual-assistance relationship, C-CRC provides a new safety mechanism that allows one system to learn from and react to the risks that the other system has encountered. Simulation results have shown that the proposed method has desirable performance in face of motion perturbation and/or jamming attack under various scenarios.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Feature Calibration Network for Occluded Pedestrian Detection

    • Free pre-print version: Loading...

      Authors: Tianliang Zhang;Qixiang Ye;Baochang Zhang;Jianzhuang Liu;Xiaopeng Zhang;Qi Tian;
      Pages: 4151 - 4163
      Abstract: Pedestrian detection in the wild remains a challenging problem especially for scenes containing serious occlusion. In this paper, we propose a novel feature learning method in the deep learning framework, referred to as Feature Calibration Network (FC-Net), to adaptively detect pedestrians under various occlusions. FC-Net is based on the observation that the visible parts of pedestrians are selective and decisive for detection, and is implemented as a self-paced feature learning framework with a self-activation (SA) module and a feature calibration (FC) module. In a new self-activated manner, FC-Net learns features which highlight the visible parts and suppress the occluded parts of pedestrians. The SA module estimates pedestrian activation maps by reusing classifier weights, without any additional parameter involved, therefore resulting in an extremely parsimony model to reinforce the semantics of features, while the FC module calibrates the convolutional features for adaptive pedestrian representation in both pixel-wise and region-based ways. Experiments on CityPersons and Caltech datasets demonstrate that FC-Net improves detection performance on occluded pedestrians up to 10% while maintaining excellent performance on non-occluded instances.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Crowdsourcing-Based Road Surface Evaluation and Indexing

    • Free pre-print version: Loading...

      Authors: Yousef-Awwad Daraghmi;Tsung-Hsiang Wu;Tsì-Uí İk;
      Pages: 4164 - 4175
      Abstract: Continuous monitoring of road surface quality is necessary to maintain high functionality of the entire road networks and allow users to perceive road roughness. Although several fundamental methods have been used to evaluate road roughness by employing smart probe cars, the accuracy of these methods is affected by factors, such as vehicle speed, sensor positions, and vehicle suspension systems. Also, the long-term application of these methods to the entire road network is also limited due to the high cost. Further, the widely used roughness indices do not reflect road users’ perception about road roughness as these indices are also affected by the accuracy of the roughness evaluation methods. Therefore, we propose a crowdsourcing based road roughness evaluation model which uses power spectral density accompanied with blind source separation technique to eliminate the vehicle effects. We also propose a road surface ranking based on majority voting algorithm for comparing roads based on their surface quality. Finally, the road surface roughness index is derived to widen the range of quality scale and capture roughness at fine granularity. The models are tested by real world experiments on different roads in Taiwan. The results show that the proposed model can accurately measure the roughness of roads, rank these roads and index them accordingly in a way that shows tiny differences in the road surface quality.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Cooperative Adaptive Cruise Control With Unconnected Vehicle in the Loop

    • Free pre-print version: Loading...

      Authors: Zheng Chen;Byungkyu Brian Park;
      Pages: 4176 - 4186
      Abstract: To improve the usability of cooperative adaptive cruise control (CACC) in the mixed traffic, a CACC algorithm with unconnected vehicle in the loop (CACCu) is proposed. Unlike the traditional CACC that requires a connected preceding vehicle or otherwise degrades to adaptive cruise control (ACC), CACCu aims to closely follow an unconnected preceding vehicle utilizing the information from the further (connected) preceding vehicle. Moreover, CACCu can robustly maintain string stability given various behaviors of unconnected preceding vehicles, without requiring identification process or extra information on the unconnected vehicles. For the sake of simplicity, this paper starts with CACCu in the three-vehicle sandwich scenario (i.e., one unconnected vehicle is in between of two connected vehicles), but derivatively, this control design is extended and evaluated in multiple-unconnected-vehicle cases. It is proven that by attaching a filter of “virtual preceding vehicle” to the original feedforward filter, the CACCu vehicle can stay string-stable at a gap significantly shorter than that required by ACC, given almost all kinds of car-following behaviors of the unconnected vehicle. At last, the favorable properties of CACCu are validated in high-fidelity simulations using real vehicle trajectory data and a physics-based vehicle dynamics model. The results show that CACCu outperforms existing ACC and acceleration-based connected cruise control (CCC) in string stability, ride comfort, safety maintenance, and fuel consumption.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Multi-Model-Based Local Path Planning Methodology for Autonomous Driving:
           An Integrated Framework

    • Free pre-print version: Loading...

      Authors: Zhiqiang Jian;Shitao Chen;Songyi Zhang;Yu Chen;Nanning Zheng;
      Pages: 4187 - 4200
      Abstract: Autonomous driving systems (ADSs) need to be able to respond quickly to changes in the dynamic traffic scenario. However, regardless of the changes occurring in traffic scenes, the current local path planning frameworks of ADSs are based on the fixed frequency re-planning path (i.e., running their planning algorithms repeatedly). This planning method makes it difficult to provide a reasonable traveling path, agility, and comfort for driverless vehicles in changing traffic scenarios. Therefore, this article performs an in-depth analysis of the problems of traditional planning frameworks which use a fixed frequency to replan the path and proposes a novel path planning framework that is universal based on multiple-models. The proposed framework divides the planning process into several layers, each of which has different functions. With this framework, the ADS can adaptively adjust the planning process according to the changes in traffic scenes and then provide different path planning algorithms to ensure its safety and flexibility in the process of driving. Moreover, the problems caused by the traditional planning framework can be solved. This framework has been applied to the autonomous vehicle “Pioneer”, which won first place in the 2019 China Intelligent Vehicle Future Challenge (IVFC). The effectiveness and rationality of the integrated framework of local path planning proposed in this article were verified by a large number of tests in real-world traffic scenarios.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Spherical Formulation of Geometric Motion Segmentation Constraints in
           Fisheye Cameras

    • Free pre-print version: Loading...

      Authors: Letizia Mariotti;Ciarán Eising;
      Pages: 4201 - 4211
      Abstract: We introduce a visual motion segmentation method employing spherical geometry for fisheye cameras and automated driving. Three commonly used geometric constraints in pin-hole imagery (the positive height, positive depth and epipolar constraints) are reformulated to spherical coordinates, making them invariant to specific camera configurations as long as the camera calibration is known. A fourth constraint, known as the anti-parallel constraint, is added to resolve motion-parallax ambiguity, to support the detection of moving objects undergoing parallel or near-parallel motion with respect to the host vehicle. A final constraint constraint is described, known as the spherical three-view constraint, is described though not employed in our proposed algorithm. Results are presented and analyzed that demonstrate that the proposal is an effective motion segmentation approach for direct employment on fisheye imagery.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Roundabout Crossing With Interval Occupancy and Virtual Instances of Road
           Users

    • Free pre-print version: Loading...

      Authors: Stefano Masi;Philippe Xu;Philippe Bonnifait;
      Pages: 4212 - 4224
      Abstract: Although autonomous vehicle technology has evolved significantly in recent years, the navigation of self-driving vehicles in complex scenarios is still an open issue. One of the major challenges in these conditions is safe navigation on roads open to public traffic. The main issue is the interaction of the autonomous vehicle with regular traffic, as the behaviors and intentions of human-driven vehicles are hard to predict and understand. In this paper we propose a strategy to allow an autonomous vehicle to safely cross a multi-lane roundabout. Our approach uses a High-Definition (HD) map to predict at lane level the future situation, harnessing the concept of virtual instances of road users, which is a key concept in anticipating the situation in a roundabout that can be represented by a navigation graph with loops. This paper presents a methodology that uses intervals representing road occupancy by vehicles, with the road being widened to reflect uncertainties in localization. Our method safely avoids collisions and guarantees that no priority constraints are violated during the insertion maneuver. Moreover, the method does not provide an overly cautious insertion policy, i.e., an autonomous vehicle does not wait for a long time before the insertion. The performance of our strategy was evaluated using the SUMO simulation framework. To better evaluate the complexity of the simulation scenario, a highly interactive vehicle flow was generated using real dynamic traffic data from the INTERACTION dataset. We report real tests carried out with an experimental self-driving vehicle on a test circuit. Our results show that this approach is easy to integrate into an embedded system and that it allows roundabouts to be crossed with a high level of safety.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Longitudinal Platoon Control of Connected Vehicles: Analysis and
           Verification

    • Free pre-print version: Loading...

      Authors: Yongfu Li;Zhenyu Zhong;Yu Song;Qi Sun;Hao Sun;Simon Hu;Yibing Wang;
      Pages: 4225 - 4235
      Abstract: This paper proposes a longitudinal platoon controller for connected vehicles (CVs) by considering the information of multiple preceding vehicles and the car-following interactions between CVs. The stability of the proposed controller is analyzed using the Routh criterion. For the verification, we develop an integrated platoon control framework for CVs in a V2V/V2I communication environment. The proposed framework consists of two main components: simulation platform and experimental platform. In particular, the simulation platform is developed based on the TransModeler software, and the experimental platform is designed using the self-developed V2X devices. Finally, a scenario of platoon forming is taken as an example and is conducted in simulation platform and experimental platform, respectively. Results demonstrate the effectiveness of the proposed controller with respect to the trajectory and velocity profiles.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • A New Approach Based on Predictive Maintenance Using the Fuzzy Classifier
           in Pantograph-Catenary Systems

    • Free pre-print version: Loading...

      Authors: Gulsah Karaduman;Erhan Akin;
      Pages: 4236 - 4246
      Abstract: Faults in railway and pantograph and catenary systems significantly endanger transport safety. Since periodic maintenance or maintenance at the time of fault will be costly, predictive maintenance methods are recommended to prevent faults in these systems. Performing predictive maintenance requires obtaining data from the railway and recording and using this data appropriately. The platform used in this study, allows data to be recorded from every device that can be connected to the internet. This recorded data are easily accessible. For this reason, this study proposes a new predictive maintenance method using the fuzzy classifier in railway systems. A simulation is performed using an internet of things platform. The data are recorded instantly on the proposed platform. Two modules, a camera and a temperature sensor, to be placed on either side of a rail line are simulated. Correlation is applied to the pantograph images obtained with the camera, and vector features are obtained from the images. In this way, a correlation coefficient for each image is calculated and gives information about the health of the pantograph. Data consisting of correlation coefficients and temperature values from modules is transmitted as input to a fuzzy classifier. The fuzzy classifier provides results about the health status of the pantograph. The results are evaluated by the ROC analysis method. When the results of the simulation are examined, it is shown that the proposed method produces effective and accurate results.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Exploring Travel Pattern Variability of Public Transport Users Through
           Smart Card Data: Role of Gender and Age

    • Free pre-print version: Loading...

      Authors: Shasha Liu;Toshiyuki Yamamoto;Enjian Yao;Toshiyuki Nakamura;
      Pages: 4247 - 4256
      Abstract: A better understanding of travel pattern variability is important for public transport (PT) authorities to improve passenger experience and service provision. Although many studies have examined the travel pattern variability of PT users, these studies are often limited to a short analysis period or to only one dimension of travel behavior. In addition, there is limited knowledge of how the demographic characteristics of PT users are associated with their travel pattern variability. To address these limitations, we develop a novel measure that simultaneously considers multiple dimensions of travel behavior to quantify the intrapersonal variability in weekly PT usage. Moreover, we examine interpersonal variability by identifying clusters of users who share similar weekly profiles. Based on smart card transaction data for 52 weeks and an anonymous cardholder database (including age and gender) from Shizuoka, Japan, we analyze the intrapersonal and interpersonal variability in weekly PT usage as well as the role of gender and age in travel pattern variability. The results indicate that gender and age play an important role in the travel pattern variability of PT users. Female users exhibit higher intrapersonal variability than their male counterparts. Weekly patterns are the most diverse for users aged 70 or over, followed by the users aged 65–69. Regarding interpersonal variability, we identify five clusters of users, each characterized by a distinct weekly profile and associated with certain age and gender.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Vision Based Detection of Driver Cell Phone Usage and Food Consumption

    • Free pre-print version: Loading...

      Authors: Benjamin Wagner;Franz Taffner;Sezer Karaca;Lukas Karge;
      Pages: 4257 - 4266
      Abstract: Distracted driving is a problem which yearly causes a large amount of road traffic crashes with high rates of fatalities and injured persons. Recently, car manufacturers started to integrate driver monitoring systems to detect visual distraction. This paper proposes a method to extend such systems by driver posture classification to detect driver cell phone usage and food consumption. Such an extension can be beneficial since systems that focus on the detection of visual distraction mainly rely on head pose and gaze information. Thus, distraction caused by cell phone usage or food consumption can not be detected by these systems when the driver is looking to the road ahead. To robustly detect those types of manual and cognitive distraction, different deep learning models were trained and evaluated based on a new image dataset which was captured by two infrared cameras to ensure that a large range of head angles can be covered by the system. Separate Convolutional Neural Networks (CNNs) were trained and evaluated for the dataset of the left and the right camera to optimize the classification accuracy. The trained CNNs revealed a competitive test accuracy of 92.88% and 90.36% for the left and the right camera, respectively. In inference mode, the models achieve a frame rate of 44Hz and 28Hz for the left and the right camera, respectively. The combination of the classification output of both networks revealed a test accuracy of 92.54%.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • A Statistical Approach Toward Channel Modeling With Application to
           Large-Scale Censored Data

    • Free pre-print version: Loading...

      Authors: Ehsan Emad Marvasti;S M Osman Gani;Yaser P. Fallah;
      Pages: 4267 - 4278
      Abstract: Connected vehicle safety and efficiency applications rely on vehicular wireless networks for information sharing. To study the performance of these networks, and numerous protocols designed for them, it is essential to be able to model the channel behavior. However, due to the complexity of the vehicular environment, such modeling has to rely on data collected from large scale field tests. A majority of data collection campaigns with a large number of vehicles utilize vehicular on-board units (transceivers) to collect data in the form of received signal strength (RSS) values. Such datasets are limited in information since only the RSS values of correctly received packets are recorded, leading to datasets that can be considered as censored; especially where packet losses are high. Therefore, deriving channel models that accurately represent the channel behavior at distances with considerable loss of packets is a challenge. In this paper, we propose Bernoulli Mapping Estimation, a novel approach for the estimation of distributions from censored samples. Additionally, we provide a general method and formulation for modeling the communication channel in vehicular environments, considering receiver characteristics, imperfections, and data sparsity. The proposed methods were applied to datasets collected from highway and intersection environments; and the derived models are shown to be accurate representations of the datasets. It is also shown that even with only a small fraction of the RSS values, the framework is able to produce fading models that are similar in characteristics to field data.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Development of a Robust Cooperative Adaptive Cruise Control With Dynamic
           Topology

    • Free pre-print version: Loading...

      Authors: Lian Cui;Zheng Chen;Aobo Wang;Jia Hu;Byungkyu Brian Park;
      Pages: 4279 - 4290
      Abstract: This research proposed a robust Cooperative Adaptive Cruise Control (CACC) to overcome the shortcoming of the existing CACC controllers in dealing with unexpected events, such as malware attack, phishing attack, and DNS tunneling attack, where perception and data received via communication contradict with the reality. The proposed controller combines the advantage of two information flow topologies – All-Predecessor Following (APF) and Predecessor-Leader Following (PLF) control methods – to improve the capability of CACC platoons. The string stability of the proposed CACC controller was proven. The robustness to time delay switch (TDS) attacks was assessed using simulation. The normal cruise was simulated to show the capability of the proposed controller in a wide range of speeds. TDS attacks, which encompassed four different unexpected events, were tested to verify the robustness of the proposed CACC. Results confirmed that the proposed CACC controller is string stable and robust against these TDS attacks without crashing or causing jerks. It also showed that the proposed CACC controller outperformed a state-of-the-art CACC controller.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Road Intersection Optimization Considering Spatial-Temporal Interactions
           Among Turning Movement Spillovers

    • Free pre-print version: Loading...

      Authors: Hongsheng Qi;Zhengbing He;
      Pages: 4291 - 4304
      Abstract: Road intersections play an important role in the operation of an urban road transportation system. However, channelized segment (C-segment) spillovers frequently occur during peak hours, making the vehicle queue on a lane spatially and temporally interact with the queue on another lane. Intersection traffic efficiency is thus largely degraded. Unfortunately, most of the existing models cannot capture the spatial-temporal queue interactions and thus are incapable of developing efficient intersection optimization plans. To fill this gap, the paper proposes a highly efficient approach traffic flow model to capture C-segment spillovers. An optimization problem that takes C-segment spillovers into account is formulated as a nonlinear programming model, and it is solved by using a Markov chain Monte Carlo method. The proposed model and solution are tested in several scenarios with various intersection settings, and the effectiveness of the proposed models is demonstrated. This study is beneficial to understanding C-segment spillovers and the models can be applied to improve traffic efficiency at recurrently-congested intersections.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Impacts of Connected Automated Vehicles on Freeway Traffic Patterns at
           Different Penetration Levels

    • Free pre-print version: Loading...

      Authors: Sergei S. Avedisov;Gaurav Bansal;Gábor Orosz;
      Pages: 4305 - 4318
      Abstract: In this paper we investigate the effects of connected automated vehicles on traffic patterns. We first experimentally study traffic patterns using two connected human-driven vehicles, which are equipped with vehicle-to-vehicle (V2V) communication, and a connected automated vehicle, which is able to respond to V2V information and control its longitudinal motion. Our experimental results indicate the long-range feedback may benefit traffic flow and that car-following models with delay are able to replicate the experimental results. The data fitted models are used in simulations for a 100-car network to study traffic dynamics with partial penetration of connected automated vehicles.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • An Emergency Hierarchical Guidance Control Strategy for Autonomous
           Vehicles

    • Free pre-print version: Loading...

      Authors: Faïza Khelladi;Mohamed Boudali;Rodolfo Orjuela;Mario Cassaro;Michel Basset;Clément Roos;
      Pages: 4319 - 4330
      Abstract: This paper introduces a vehicle guidance control architecture capable of autonomously resolving emergency situations due to a steering system failure. This situation requires a safe stop in the emergency lane by means of differential braking. The proposed approach is based on a three-level hierarchical architecture composed, from the highest to the lowest, by a reference generation, a guidance control, and a control allocation level. The reference generation function computes the trajectory and the speed profile to be tracked by the vehicle according to the active mode of operation: normal or emergency. Switching mode information are received by the fault detection and isolation (FDI) supervisor. The guidance control function generates the steering angle and the braking/accelerating wheels’ torques commands based on longitudinal and lateral tracking errors. At the lowest level of hierarchy, the control allocation function dynamically redistributes the control commands to the available set of actuators, according to FDI information. For instance, in the proposed study, promoting differential braking in case of a steering system failure, guaranteeing acceptable tracking performance both in longitudinal and lateral directions. Simulation results prove the efficacy of the proposed approach.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Coordinated Time-Varying Low Gain Feedback Control of High-Speed Trains
           Under a Delayed Communication Network

    • Free pre-print version: Loading...

      Authors: Weiqi Bai;Hairong Dong;Zixuan Zhang;Yidong Li;
      Pages: 4331 - 4341
      Abstract: The coordinated control problem for a multiple high-speed train (HST) system subject to unknown communication delays is systematically investigated in this paper. Taking into consideration the inertial lag of the servo motor, a third-order nonlinear control model is constructed to capture the dynamics of a train in real-world operations. By virtue of the backstepping linearization technique, the coordinated control of trains is formulated as a stabilization problem for a linear multiple-input multiple-output system with an unknown input delay. Distributed control laws with a time-varying low gain parameter are designed, besides solving the stabilization problem, to guarantee a fast convergency rate during the train status adjustment process. Numerical examples are provided to illustrate that the time-varying low gain parameter design achieves better control performance compared with the traditional constant low gain feedback design in terms of the convergency rate and the system overshot, and that the proposed control method is effective in train tracking distance adjustment.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Vehicle Rebalancing With Charging Scheduling in One-Way Car-Sharing
           Systems

    • Free pre-print version: Loading...

      Authors: Ge Guo;Tao Xu;
      Pages: 4342 - 4351
      Abstract: This paper studies the problem of coordinated rebalancing and charging scheduling for mobility-on-demand systems with electric vehicles. A joint framework consisting of multi-server M/M/s queueing and fluid model is proposed to solve the problem, in which the former is used to deal with charging scheduling of vehicles, while the latter describes the dynamics of vehicles and users in the system. A fluid policy is presented to minimize the total number of in-transit empty vehicles under static equilibrium, yielding the optimal assignment by nonlinear programming. To cope with dynamically varying traffic conditions, we further develop a two-stage real-time policy for charging and rebalancing scheduling, where rebalancing assignment is periodically adjusted and a time-weighted averaging method is proposed to predict the future travel demand. Also, the amount of vehicles to be deployed in each charging station is given to minimize the customer waiting time for charging. The effectiveness of the proposed method is verified via simulations and experiments.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Handover Count Based MAP Estimation of Velocity With Prior Distribution
           Approximated via NGSIM Data-Set

    • Free pre-print version: Loading...

      Authors: Ravi Tiwari;Siddharth Deshmukh;
      Pages: 4352 - 4361
      Abstract: In this paper, we propose a maximum-a-posteriori probability (MAP) based velocity estimation technique in which the prior distribution is defined by current location of the user. Motivation of this work is to improve accuracy of the existing velocity estimation techniques which are either solely based on cellular network measurements or location specific information. Our objective is to exploit both cellular measurements and location information in Bayesian sense; thus, to jointly address the critical applications of mobility management in Heterogeneous-Networks (HetNets), and intelligent transportation system. Here we assume that the Next Generation Simulation (NGSIM) data set for velocity is available at the current location and can be utilized to approximate the prior distribution. Additional information in form of prior distribution function is then exploited to improve the minimum variance unbiased (MVU) estimate of velocity which is based on handover count measurements. Since MVU estimate is a random variable, we first formulate its density function parameterized over the actual velocity. Next, we follow Bayesian approach to accommodate both prior distribution and parametric density function in deriving posterior density function of velocity. Finally, we derive expression of the MAP estimator considering various standard distribution functions which best fit to the density function obtained from NGSIM data set. In order to quantify the quality of estimate, we derive its variance and the corresponding Cramer-Rao-bound (CRB) on the minimum error variance. Numerical results demonstrate that the proposed estimator which incorporates NGSIM data set is asymptotically efficient and outperforms other classical handover count based estimation techniques.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • A Robust In-Motion Optimization-Based Alignment for SINS/GPS Integration

    • Free pre-print version: Loading...

      Authors: Xiang Xu;Yifan Sun;Yiqing Yao;Tao Zhang;
      Pages: 4362 - 4372
      Abstract: Initial alignment is of vital importance to the inertial navigation system, and the in-motion initial alignment is a critical stage for the moving vehicles. In this paper, an in-motion alignment method for SINS/GPS integration is investigated. Using the velocity, which is obtained from GPS, the observation and the reference vectors are constructed. Different from previous methods, a robust attitude determination method is devised to finish the in-motion alignment process when there are outliers contained in the GPS outputs. To implement the robust attitude determination method, the error of the magnitudes of the observation and reference vectors is calculated, and this error is used to detect the outliers. However, the bias of the inertial sensors, the expectation and variance of the calculated error of the magnitude is time-varying, so that the detected accuracy of the outliers degrades. To address this defect, the new normalized error of the magnitude is constructed by a robust parameter identification method, then the expectation and variance of the new normalized error are constant. Using the new normalized error, the initial attitude is obtained by the robust optimization-based alignment (ROBA) method. The simulation and field tests are designed to validate the performance of the proposed method, the alignment results show that the proposed method can detect the outliers accurately, and it gets the similar alignment accuracy with the current popular method when there are no outliers in the auxiliary velocity of GPS.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Distributed H∞ Controller Design and Robustness Analysis for Vehicle
           Platooning Under Random Packet Drop

    • Free pre-print version: Loading...

      Authors: Kaushik Halder;Umberto Montanaro;Shilp Dixit;Mehrdad Dianati;Alexandros Mouzakitis;Saber Fallah;
      Pages: 4373 - 4386
      Abstract: This paper presents the design of a robust distributed state-feedback controller in the discrete-time domain for homogeneous vehicle platoons with undirected topologies, whose dynamics are subjected to external disturbances and under random single packet drop scenario. A linear matrix inequality (LMI) approach is used for devising the control gains such that a bounded $H_{infty }$ norm is guaranteed. Furthermore, a lower bound of the robustness measure, denoted as $gamma $ gain, is derived analytically for two platoon communication topologies, i.e., the bidirectional predecessor following (BPF) and the bidirectional predecessor leader following (BPLF). It is shown that the $gamma $ gain is highly affected by the communication topology and drastically reduces when the information of the leader is sent to all followers. Finally, numerical results demonstrate the ability of the proposed methodology to impose the platoon control objective for the BPF and BPLF topology under random single packet drop.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • User Community Identification Through Fine-Grained Mobility Records for
           Smart City Applications

    • Free pre-print version: Loading...

      Authors: Danielle L. Ferreira;Bruno Astuto A. Nunes;Carlos Alberto Vieira Campos;Katia Obraczka;
      Pages: 4387 - 4401
      Abstract: Motivated by Smart City applications and services, this article presents a novel approach to identifying user communities in communication networks. We define user communities as groups of users that share common mobility features over spatio-temporal scales of arbitrary length, such as time spent in certain locales, mobility speed, and time between consecutive movements. We describe our user community identification framework in detail including how mobility features can be extracted from real mobility traces (as examples of unlabelled data) and synthetic mobility records (as examples of labeled data). We present results obtained when using our approach in four distinct mobility scenarios represented by both unlabeled and labeled datasets. We also introduce a new validation methodology that uses image-based similarity metrics in order to assess the quality of identified communities. Our results show that the proposed approach significantly increases similarity between users within the same community as well as dissimilarity between users in different communities. We also demonstrate that the proposed user community identification approach yields significant increase in contact time amongst users belonging to the same community when compared to the average contact time when not considering community structures.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • High-Speed Train Platoon Dynamic Interval Optimization Based on Resilience
           Adjustment Strategy

    • Free pre-print version: Loading...

      Authors: Wei Shangguan;Rui Luo;Hongyu Song;Jing Sun;
      Pages: 4402 - 4414
      Abstract: Resilience adjustment refers to the generation of a control strategy by evaluating the interaction between related factors. Tracking intervals of the high-speed train platoon change dynamically, which directly influences the operation safety and efficiency, and constrains the train operation trajectories. In China, the tracking interval is getting shorter. To ensure safety and improve efficiency, we research a dynamic interval resilience adjustment strategy based on the moving block system. Firstly, the optimal offline operation strategy is obtained by solving the multi-objective optimization model with the improved gravitational search algorithm (I-GSA). The resilience adjustment mechanism is developed to evaluate the tracking interval and choose the appropriate driving strategy to adjust operation states based on the resilience tracking interval model. Then, we study the relation between operation strategy and departure interval, and a seeker optimization algorithm (SOA) is used to obtain the optimal departure intervals and driving strategies. Simulations are conducted based on the sections between Chibi North station and Changsha South station in Wuhan-Guangzhou high-speed railway. The results indicate that the total operation time decreased by 191s and the operation safety can be ensured at any time.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • A Two-Layer Potential-Field-Driven Model Predictive Shared Control Towards
           Driver-Automation Cooperation

    • Free pre-print version: Loading...

      Authors: Mingjun Li;Haotian Cao;Guofa Li;Song Zhao;Xiaolin Song;Yimin Chen;Dongpu Cao;
      Pages: 4415 - 4431
      Abstract: This paper proposes a novel driver-automation shared control based on a potential-field-driven model predictive controller (PF-MPC) and a two-layer fuzzy strategy (TLFS) to address driver-automation conflicts and control authority allocation issues. The PF-MPC approach based on the driver-vehicle model is introduced to deal with obstacles avoidance and driver-automation conflicts. The potential field is constructed to evaluate the driving risk by considering the driving environment and vehicle states, meanwhile, it is also involved in the optimized objective in the PF-MPC controller for obstacle avoidance. The tuning weight is designed to adjust the trade-off between the motion planning-related cost and driver-related cost to reduce driver-automation conflicts. To further alleviate the conflict and control authority allocation between the human driver and PF-MPC controller, the TLFS for shared control is designed based on the evaluation of the driving risk level and conflict situation, and the values of the tuning weight and cooperative coefficient are determined using the fuzzy control method. Moreover, comparative studies are conducted to verify the driving safety and conflict management performance of the proposed shared control method on a straight road and a curvy road. The results show that the proposed shared control method can help drivers avoid obstacles safely and alleviate the driver-automation conflicts in different driving conditions.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Parallel Complement Network for Real-Time Semantic Segmentation of Road
           Scenes

    • Free pre-print version: Loading...

      Authors: Qingxuan Lv;Xin Sun;Changrui Chen;Junyu Dong;Huiyu Zhou;
      Pages: 4432 - 4444
      Abstract: Real-time semantic segmentation is in intense demand for the application of autonomous driving. Most of the semantic segmentation models tend to use large feature maps and complex structures to enhance the representation power for high accuracy. However, these inefficient designs increase the amount of computational costs, which hinders the model to be applied on autonomous driving. In this paper, we propose a lightweight real-time segmentation model, named Parallel Complement Network (PCNet), to address the challenging task with fewer parameters. A Parallel Complement layer is introduced to generate complementary features with a large receptive field. It provides the ability to overcome the problem of similar feature encoding among different classes, and further produces discriminative representations. With the inverted residual structure, we design a Parallel Complement block to construct the proposed PCNet. Extensive experiments are carried out on challenging road scene datasets, i.e., CityScapes and CamVid, to make comparison against several state-of-the-art real-time segmentation models. The results show that our model has promising performance. Specifically, PCNet* achieves 72.9% Mean IoU on CityScapes using only 1.5M parameters and reaches 79.1 FPS with $1024times 2048$ resolution images on GTX 2080Ti. Moreover, our proposed system achieves the best accuracy when being trained from scratch.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Introspective Failure Prediction for Autonomous Driving Using Late Fusion
           of State and Camera Information

    • Free pre-print version: Loading...

      Authors: Christopher B. Kuhn;Markus Hofbauer;Goran Petrovic;Eckehard Steinbach;
      Pages: 4445 - 4459
      Abstract: We present an introspective failure prediction approach for autonomous vehicles. In autonomous driving, complex or unknown scenarios can cause a disengagement of the self-driving system. Disengagements can be triggered either by automatic safety measures or by human intervention. We propose to use recorded disengagement sequences from test drives as training data to learn to predict future failures. The system then learns introspectively from its own previous mistakes. In order to predict failures as early as possible, we propose a machine learning approach where sequences of sensor data are classified as either failure or success. The car itself is treated as a black box. Our method combines two sensor modalities that contain different types of information. An image-based model learns to detect generally challenging situations such as crowded intersections accurately multiple seconds in advance. A state data based model allows to detect fast changes immediately before a failure, such as sudden braking or swerving. The outcome of the individual models is fused by averaging the individual failure probabilities. We evaluate our approach on a data set provided by the BMW Group containing 14 hours of autonomous driving. The proposed late fusion approach allows for predicting failures at an accuracy of more than 85% seven seconds in advance, at a false positive rate of 20%. The proposed method outperforms state-of-the-art failure prediction by more than 15% while being a flexible framework that allows for straightforward addition of further sensor modalities.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Taxi-Passenger’s Destination Prediction via GPS Embedding and
           Attention-Based BiLSTM Model

    • Free pre-print version: Loading...

      Authors: Chengwu Liao;Chao Chen;Chaocan Xiang;Hongyu Huang;Hong Xie;Songtao Guo;
      Pages: 4460 - 4473
      Abstract: The prediction of taxi-passenger’s destination with the partial GPS trajectory left by moving taxis is an important yet challenging research issue. The high uncertainty of human mobility and limited clue provided by the unfinished trajectory are two major barriers to developing effective predictors. In general, such a prediction task is often converted to the identification one among given candidate destinations. Hence, how to extract the discriminative knowledge from the partial trajectory becomes crucial. It is well-recognized that the sequence of visited locations by the taxi has inherent relationship with the heading destination. Inspired by the idea, we propose a novel approach that jointly combines the GPS embedding and attention-based BiLSTM techniques for the prediction of passenger’s destination. Specifically, we propose two GPS embedding methods to encode the geographic proximity and multi-scale spatiality of GPS points into embedding vectors, so as to reveal the spatial context of visited locations in the urban space. After converting GPS trajectories into embedding sequences, we further establish an attention-based dual BiLSTMs neural network to model the relationship between the heading destination and the bidirectional sequential context of visited locations. Meanwhile, the discriminative capability of visited locations in determining the destination can be captured by the attention mechanism. In addition, the OT (origin and time) information is aggregated into the neural network as auxiliary features. Stepping closer to smarter passenger services, rather than telling destinations in terms of drop-off clusters, our proposed model outputs the destinations in terms of historical passengers’ destination clusters. Finally, we evaluate the system performance based on two real large-scale datasets. Results show the superior performance of our proposed model.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Robust Control of Arrivals Into a Queuing Network

    • Free pre-print version: Loading...

      Authors: Sandeep Badrinath;Hamsa Balakrishnan;
      Pages: 4474 - 4489
      Abstract: Queuing networks have been widely-used to model congestion in transportation systems. Due to their interconnected nature, delays in a queuing network can propagate as customers traverse through the network; similarly, downstream resources can be underutilized due to poor control policies. This paper considers the regulation of arrivals into a queuing network in order to maintain a desired level of occupancy (queue length) in the system. The dynamics of the queuing network is represented by a fluid-flow model, which is then used to develop a robust controller for tracking the desired queue length. The controller is based on a sliding mode control approach, with predictor-based feedback to account for propagation delays. For a single queue, we determine sufficient conditions for tracking the queue length, and bounds on the tracking error. We also present an analysis of the tracking performance for queues in tandem. We demonstrate our approach for the example of airport surface congestion control. The proposed robust control framework is based on a queuing network model of the airport, and is used to tactically manage aircraft departures in order to reduce congestion on the airport tarmac.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Cooperative Ramp Merging Design and Field Implementation: A Digital Twin
           Approach Based on Vehicle-to-Cloud Communication

    • Free pre-print version: Loading...

      Authors: Xishun Liao;Ziran Wang;Xuanpeng Zhao;Kyungtae Han;Prashant Tiwari;Matthew J. Barth;Guoyuan Wu;
      Pages: 4490 - 4500
      Abstract: Ramp merging is considered as one of the most difficult driving scenarios due to the chaotic nature in both longitudinal and lateral driver behaviors (namely lack of effective coordination) in the merging area. In this study, we have designed a cooperative ramp merging system for connected vehicles, allowing merging vehicles to cooperate with others prior to arriving at the merging zone. Different from most of the existing studies that utilize dedicated short-range communication, we adopt a Digital Twin approach based on vehicle-to-cloud communication. On-board devices upload the data to the cloud server through the 4G/LTE cellular network. The server creates Digital Twins of vehicles and drivers whose parameters are synchronized in real time with their counterparts in the physical world, processes the data with the proposed models in the digital world, and sends advisory information back to the vehicles and drivers in the physical world. A real-world field implementation has been conducted in Riverside, California, with three passenger vehicles. The results show the proposed system addresses the issues of safety and environmental sustainability with an acceptable communication delay, compared to the baseline scenario where no advisory information is provided during the merging process.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Traffic Dynamics at Intersections Subject to Random Misperception

    • Free pre-print version: Loading...

      Authors: Volker Berkhahn;Marcel Kleiber;Johannes Langner;Chris Timmermann;Stefan Weber;
      Pages: 4501 - 4511
      Abstract: Traffic accidents cause harm to the society. Future technology in autonomous vehicles is expected to eliminate the human factor as one important cause of failure. However, in the near future, autonomous vehicles and human drivers will coexist and downside risk still needs to be tolerated in exchange for mobility. Unsignalized intersections are particularly prone to accidents, as lots of potential conflicts between traffic participants occur. Motorists need to anticipate these on the basis of their perception of the environment and react accordingly. Yet, perceptional errors affect human drivers, and it is important to understand their impact on traffic safety and traffic efficiency. We develop a microscopic model of traffic dynamics at single-lane unsignalized intersections subject to random misperception that may cause accidents. Perceptional errors can be modeled by stochastic processes, e.g., Ornstein-Uhlenbeck processes. We present suitable simulation techniques and characterize the behavior of the traffic system in various case studies. We discuss the impact of errors and safety margins on traffic flow, the number of accidents, and the number of collided vehicles. In terms of perception errors, we consider both homogeneous and heterogeneous traffic participants, reflecting the coexistence of human drivers and autonomous vehicles. The model captures the real-world tradeoff between safety and efficiency for potential future traffic systems.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • A Multi-Agent Driving Simulation Approach for Evaluating the Safety
           Benefits of Connected Vehicles

    • Free pre-print version: Loading...

      Authors: Jieun Ko;Jiyong Jang;Cheol Oh;
      Pages: 4512 - 4524
      Abstract: The in-vehicle warning information provided in the CVs environment allow the driver to respond rapidly to upcoming hazardous situations. The main purpose of this study is to evaluate the safety benefits due to the provision of warning information by analyzing vehicle interactions that are defined as the behavior change of the subject vehicle and the preceding vehicle. A notable feature of this study is the use of a multi-agent driving simulation (MADS) method to analyze the vehicle interaction with various vehicle pairs that are composed of the connected vehicle (CV) capable of receiving warning information and the regular vehicle (RV) that does not receive warning information. A total of four scenarios representing different vehicle pairs, which include CV-RV, RV-CV, CV-CV, and RV-RV, are evaluated in this study. The proposed analysis consists of four parts: the characteristics of subject vehicle maneuvering, variation of relative speed, evasive maneuvering by lane change, and overall crash potential. As an example, the result of analyzing crash potential index (CPI) showed that the greatest safety benefits were obtained with the CV-CV case among aforementioned four vehicle pairs. Approximately 45% of CPI reduction was achievable with the CV-CV case, compared to the RV-RV case. In addition to the CPI, useful findings obtained by investigating safety indicators for each vehicle pair are discussed in terms of safety benefits. The results of this study are expected to be used for both deriving valuable policies and developing more effective in-vehicle warning technologies to fully exploit the benefits of CVs.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Synchronized Optimization for Service Scheduling, Train Parking and
           Routing at High-Speed Rail Maintenance Depot

    • Free pre-print version: Loading...

      Authors: Jiaxi Wang;Yinan Zhao;Manfred Gronalt;Boliang Lin;Jianping Wu;
      Pages: 4525 - 4540
      Abstract: To meet the operational requirements and to ensure safe operations, train units of a passenger railway operator require inspection and/or cleaning at the maintenance depots after running a specified mileage or time period. Accordingly, these inspection and cleaning activities are called depot services. In this paper, we propose an integer linear programming (ILP) model for the integrated optimization problem of depot service scheduling, train parking, and train routing. Both short trains (8 marshaling) and long trains (16 marshaling) are considered in the ILP model which is a combinational optimization problem in the dead-end tracks scenario. Moreover, a real-world case study of the Shanghai South Depot, China is carried out to further examine the effectiveness and efficiency of the proposed methodology. Computational results indicate that our method is able to generate optimized shunting plans for the linearized INLP within reasonable solution time for real-life applications, outperforming the manual method in terms of the solution quality and the computation speed.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Strategic Information Perturbation for an Online In-Vehicle Coordinated
           Routing Mechanism for Connected Vehicles Under Mixed-Strategy Congestion
           Game

    • Free pre-print version: Loading...

      Authors: Stephen Spana;Lili Du;Yafeng Yin;
      Pages: 4541 - 4555
      Abstract: The increased market penetration of route guidance tools–relaying real-time traffic information to drivers–can have damaging effects on transportation networks, including traffic congestion oscillation resulting from the overreaction phenomenon and the inability to control system performance. To address these issues, this study leverages V2I communication capabilities to integrate strategic real-time traffic information perturbation into an online, in-vehicle coordinated routing mechanism for connected vehicles using a mixed-strategy congestion game (CRM-M-IP). Under the CRM-M-IP, the routing decisions of all vehicles are coordinated to prevent overreaction. Additionally, the routing decisions for all vehicles are based on strategically perturbed traffic information (a convex combination between average and marginal link travel times), to ensure that the selfish route choices made by users also help improve system performance. We prove that low information perturbation levels can lead to high system performance gains with correspondingly low individual user optimality losses. From numerical experiments conducted on the Sioux Falls network, we observe that the CRM-M-IP leads to a system performance improvement greater than 3%, and average individual travel time reduction up to 3.5% as compared to the case with no perturbation. Moreover, we find that the average individual user optimality loss resulting from information perturbation is less than 2%. However, we find that when perturbation is high, some users can experience losses approaching 30%—illustrating the need to not over-perturb to ensure compliance of drivers.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Optimal Trajectory Planning and Robust Tracking Using Vehicle Model
           Inversion

    • Free pre-print version: Loading...

      Authors: Stéphane Victor;Jean-Baptiste Receveur;Pierre Melchior;Patrick Lanusse;
      Pages: 4556 - 4569
      Abstract: This article deals with the issue of tracking a reference optimal trajectory for an autonomous nonlinear vehicle model by designing both lateral and longitudinal robust feedback control and a suited feedforward control. In previous works, a strategy based on a human-driver field of view was used to plan an optimal trajectory reference. The optimization has been made using a Genetic Algorithm (GA), and the obtained trajectory has been injected into a Potential Field (PF) so as to be reactive to unforeseen events by using a point mass model. Here, the previously developed GA-PF planification process is integrated in a new complete global planning and tracking method and applied to a validation nonlinear vehicle model. This control tracking method is developed in two strategies: a bicycle model is used as model inversion for feedforward design and a robust control is designed as feedback control in order to take the vehicle (mass and velocity) and road (slope and adherence) parameter variations into account. A lateral nonlinear control and a longitudinal robust control are designed. Realistic autonomous car simulation results are provided on an overtaking scenario and a round-about scenario.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • End-to-End Trainable One-Stage Parking Slot Detection Integrating Global
           and Local Information

    • Free pre-print version: Loading...

      Authors: Jae Kyu Suhr;Ho Gi Jung;
      Pages: 4570 - 4582
      Abstract: This paper proposes an end-to-end trainable one-stage parking slot detection method for around view monitor (AVM) images. The proposed method simultaneously acquires global information (entrance, type, and occupancy of parking slot) and local information (location and orientation of junction) by using a convolutional neural network (CNN), and integrates them to detect parking slots with their properties. This method divides an AVM image into a grid and performs a CNN-based feature extraction. For each cell of the grid, the global and local information of the parking slot is obtained by applying convolution filters to the extracted feature map. Final detection results are produced by integrating the global and local information of the parking slot through non-maximum suppression (NMS). Since the proposed method obtains most of the information of the parking slot using a fully convolutional network without a region proposal stage, it is an end-to-end trainable one-stage detector. In experiments, this method was quantitatively evaluated using the public dataset (PS2.0) and outperforms previous methods by showing both recall and precision of 99.77%, type classification accuracy of 100%, and occupancy classification accuracy of 99.31% while processing 60 frames per second.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • A Bayesian Game Based Approach for Associating the Nodes to the Gateway in
           LoRa Network

    • Free pre-print version: Loading...

      Authors: Preti Kumari;Hari Prabhat Gupta;Tanima Dutta;
      Pages: 4583 - 4592
      Abstract: Various wireless local area network technologies are developed for Internet of Things of which Long-Range Wide Area Network is preferred for low power long range communication. In the LoRa network, multiple LoRa nodes can simultaneously communicate with a LoRa gateway which causes the interference problem. This article presents an approach for estimating the association time duration between each LoRa node and the LoRa gateways for transmitting the data of end users to the LoRa gateways with high packet delivery ratio. The approach uses a Beta distribution based reputation model for estimating the association time duration between each LoRa node and LoRa gateways and Bayesian Game strategy which accommodates unknown private information of the LoRa nodes. The approach is validated by simulating the LoRa network using network simulator-3. We also demonstrate an on-campus traffic monitoring system to detect the reckless driving action and estimate the vehicle speed using sensors embedded nodes deployed along both sides of the road.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Accelerated Map Matching for GPS Trajectories

    • Free pre-print version: Loading...

      Authors: Marko Dogramadzi;Aftab Khan;
      Pages: 4593 - 4602
      Abstract: The processing and analysis of large-scale journey trajectory data is becoming increasingly important as vehicles become ever more prevalent and interconnected. Mapping these trajectories onto a road network is a complex task, largely due to the inevitable measurement error generated by GPS sensors. Past approaches have had varying degrees of success, but achieving high accuracy has come at the expense of performance, memory usage, or both.In this paper, we solve these issues by proposing a map matching algorithm based on Hidden Markov Models (HMM). The proposed method is shown to be more efficient when compared against a traditional HMM based map matching method, whilst maintaining high accuracy and eschewing any requirements for CPU-intensive and memory-expensive pre-processing. The proposed algorithm offers a method for significantly accelerating transition-probability calculations using instances of high data-availability, which have previously been a large bottleneck in map matching algorithm performance. It is shown that this can be accomplished with the application of road-network segmentation combined with a spatially-aware heuristic. Experiments are performed using two different datasets, with over 9 hours of GPS samples. We show that the proposed framework is able to offer a reduction in run-time of over 90% with no significant effect on the algorithm’s accuracy when compared against the traditional HMM approach.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • A Train Positioning Method Based-On Vision and Millimeter-Wave Radar Data
           Fusion

    • Free pre-print version: Loading...

      Authors: Zhangyu Wang;Guizhen Yu;Bin Zhou;Pengcheng Wang;Xinkai Wu;
      Pages: 4603 - 4613
      Abstract: Accurate train positioning is crucial for train safety. In this paper, we propose a train positioning method which fuses vison and millimeter-wave radar data. The proposed method contains two parts: loop closure detection (LCD) and radar-based odometry. The loop closure detection part fuses the convolutional neural network (CNN) features and the line features to achieve accurate key location detection. The radar-based odometry part proposes a train speed measurement algorithm using millimeter-wave radar, and combines the results of loop closure detection to further realize train positioning. Experiments conducted on the Hong Kong metro Tsuen Wan line show that our proposed loop closure detection can achieve an efficient key location detection with 98.57% precision and 99.37% recall; the speed detection method fulfills the ETCS requirements; and the relative error of the proposed train positioning method is 0.45%. Besides, the proposed method has been applied on the Hong Kong Metro TSUEN WAN line.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Public Curb Parking Demand Estimation With POI Distribution

    • Free pre-print version: Loading...

      Authors: Yiwen Nie;Wei Yang;Zhi Chen;Nanxue Lu;Liusheng Huang;Huan Huang;
      Pages: 4614 - 4624
      Abstract: With the increasing quantity of private cars, curb parking has evolved into an important approach to mitigate parking pressure in urban cities. While some efforts have been made for the demand analysis of point-of-interest (POI) and pattern analysis of human mobility, which may indirectly reflect the parking situation in urban area, there is a lack of comprehensive models for the parking demand, so as to make a prediction for the road sections without parking lots. In this paper, by focusing on curb parking and designing a systemic framework, named Curb Parking Demand Estimation (CPDE), we model the public parking demand in urban area, w.r.t. parking durations and regional characteristics. Specifically, we use taxi destinations and the distribution of POIs to quantitatively analyze the regional characteristics, designing corresponding features, and propose a K-means-based Least Square (KLS) method to relate parking characteristics, namely, the temporal parking durations and the corresponding demands, with these features. In this way, we effectively avoid the geographical sparsity of road parking sections and can finely estimate parking durations and demands for newly developed districts without parking data. Moreover, we give a strategy, named Parking Types Estimation (PTE), which projects estimated parking durations and demands onto Gaussian Mixture Model (GMM) to accurately measure the distribution of demands over different parking durations for a road section. At last, we conduct experiments on a real-world curb parking dataset in Hefei, a provincial city in China. This dataset contains parking orders of 2016 over the urban area of Hefei. The experimental results validate the effectiveness of our methods, and show that our framework outperforms the state-of-the-art baseline schemes.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Trajectory Tracking and Integrated Chassis Control for Obstacle Avoidance
           With Minimum Jerk

    • Free pre-print version: Loading...

      Authors: Amrik Singh Phuman Singh;Osamu Nishihara;
      Pages: 4625 - 4641
      Abstract: Intelligent vehicles are expected to perform emergency lane change maneuvers to avoid a collision. During this aggressive maneuver, a high jerk may occur, which reduces the comfort level and may be harmful to vehicle occupants. The present paper addresses autonomous collision avoidance in the context of minimum jerk. First, the desired trajectory described by the desired path and desired velocity profile is generated using quintic polynomials. These quintic polynomials are derived using the Euler-Lagrange equations for the functional defined as the time integral of the squared resultant jerk. The generation of the trajectory depends on the essential parameters, which are the initial longitudinal vehicle velocity, the desired final lateral position, and the tire-road friction coefficient. As a result of nondimensionalization and algebraic manipulations, the collision avoidance problem reduces to a nondimensionalized equation that is an implicit equation in one unknown, which is the lane change aspect ratio, and an input capturing the essential parameters. The plot of the lane change aspect ratio with respect to the input yields a curve that indicates the last point at which a collision can be avoided. The sliding mode control method is used to translate the trajectory tracking errors into the reference values of the total longitudinal force and the centers of percussion lateral accelerations that are the inputs to the tire force distributor. This distributor allocates these reference values to the tires by reducing the tire force usage. Numerical simulations at different initial longitudinal vehicle velocities demonstrate the effectiveness of the controller in avoiding the obstacle and keeping the vehicle motion stable.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Harmonious Lane Changing via Deep Reinforcement Learning

    • Free pre-print version: Loading...

      Authors: Guan Wang;Jianming Hu;Zhiheng Li;Li Li;
      Pages: 4642 - 4650
      Abstract: In this paper, we study how to learn a harmonious deep reinforcement learning (DRL) based lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) communication support. The basic framework of this paper can be viewed as a multi-agent reinforcement learning in which different agents will exchange their strategies after each round of learning to reach a zero-sum game state. Unlike cooperation driving, harmonious driving only relies on individual vehicles’ limited sensing results to balance overall and individual efficiency. Specifically, we propose a well-designed reward that combines individual efficiency with overall efficiency for harmony, instead of only emphasizing individual interests like competitive strategy. Testing results show that competitive strategy often leads to selfish lane change behaviors, anarchy of crowd, and thus the degeneration of traffic efficiency. In contrast, the proposed harmonious strategy can promote traffic efficiency in both free flow and traffic jam than the competitive strategy. This interesting finding indicates that we should take care of the reward setting for reinforcement learning-based AI robots (e.g., automated vehicles) design, when the utilities of these robots are not strictly in alignment.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Estimating the Potential for Shared Autonomous Scooters

    • Free pre-print version: Loading...

      Authors: Dániel Kondor;Xiaohu Zhang;Malika Meghjani;Paolo Santi;Jinhua Zhao;Carlo Ratti;
      Pages: 4651 - 4662
      Abstract: Recent technological developments have shown significant potential for transforming urban mobility. Considering first- and last-mile travel and short trips, the rapid adoption of dockless bike-share systems showed the possibility of disruptive change, while simultaneously presenting new challenges, such as fleet management or the use of public spaces. In this paper, we evaluate the operational characteristics of a new class of shared vehicles that are being actively developed in the industry: scooters with self-repositioning capabilities. We do this by adapting the methodology of shareability networks to a large-scale dataset of dockless bike-share usage, giving us estimates of ideal fleet size under varying assumptions of fleet operations. We show that the availability of self-repositioning capabilities can help achieve up to 10 times higher utilization of vehicles than possible in current bike-share systems. We show that actual benefits will highly depend on the availability of dedicated infrastructure, a key issue for scooter and bicycle use. Based on our results, we envision that technological advances can present an opportunity to rethink urban infrastructures and how transportation can be effectively organized in cities.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • The Problem of Electric Vehicle Charging: State-of-the-Art and an
           Innovative Solution

    • Free pre-print version: Loading...

      Authors: Omar N. Nezamuddin;Clayton L. Nicholas;Euzeli Cipriano dos Santos;
      Pages: 4663 - 4673
      Abstract: This paper presents a comprehensive literature review with focus on the difficulties electric vehicles (EVs) face to charge the battery while on a trip, and proposes a solution without the need of an expensive change in infrastructure. The proposed method charges EVs while en route from another vehicle, which will be referred to as vehicle-to-vehicle recharging (VVR). The aim of this system is to bring an innovative way for EVs to charge their battery without getting off route on a highway. The electric vehicle can request such a service from a designated charger vehicle on demand and receive electric power wirelessly while en route. The vehicles that provide energy (charger vehicles) through wireless power transfer (WPT) only need to be semi-autonomous in order to engage/disengage during a trip. The state-of-the-art is divided into three subsections relevant to the proposed system and where most of the innovations to reduce the burden of charging EVs can be found: (1) infrastructure changes, (2) device level innovations, and (3) autonomous vehicles. The infrastructure changes highlight some of the proposed systems that aim to help EVs become a convenient solution to the public. Device level innovations covers some of the literature on technology that addresses EVs in terms of WPT. And finally, the autonomous vehicle subsection covers the importance of such technology in terms of safety and reliability, that could be implemented on the VVR system. Furthermore, modeling, analysis, and simulation is presented to validate the feasibility of the proposed system.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Traffic Estimation and Prediction via Online Variational Bayesian Subspace
           Filtering

    • Free pre-print version: Loading...

      Authors: Charul Paliwal;Uttkarsha Bhatt;Pravesh Biyani;Ketan Rajawat;
      Pages: 4674 - 4684
      Abstract: With the increased proliferation of smart devices, the transit passengers of today expect a higher quality of service in the form of real-time traffic updates, accurate expected time-of-arrival (ETA) predictions. Providing these services requires public transit agencies and private transportation players to maintain full situational awareness of the city-wide traffic. However, most such agencies and companies are resource-constrained and do not have access to city-wide traffic data. The availability of sparsely sampled and outlier-corrupted traffic data renders the resulting traffic maps patchy and unreliable and necessitates the use of sophisticated real-time traffic interpolation and prediction algorithms. Moreover, since the traffic data is measured and collected in a sequential manner, the estimations must also be generated online. Thankfully, the traffic matrices are spatially and temporally structured, allowing the use of time-series and matrix/tensor completion algorithms. This work puts forth a generative model for the traffic density and subsequently uses a variational Bayesian formalism to learn the parameters of the model. Specifically, we consider low-rank traffic matrices whose subspace evolves according to a state-space model with possible sparse outliers. Unlike most matrix/tensor completion algorithms, the proposed model is equipped with automatic relevance determination priors that allow it to learn the parameters in an entirely data-driven manner. A forward-backward algorithm is proposed that enables the updates to be carried out at low-complexity. Simulations carried out on real traffic speed data demonstrate that the proposed algorithm better predicts the future traffic densities as compared to the state-of-the-art matrix/tensor completion algorithms.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Pothole 3D Reconstruction With a Novel Imaging System and Structure From
           Motion Techniques

    • Free pre-print version: Loading...

      Authors: Adeel Ahmed;Moeez Ashfaque;Muhammad Uzair Ulhaq;Senthan Mathavan;Khurram Kamal;Mujib Rahman;
      Pages: 4685 - 4694
      Abstract: Machine vision based evaluation systems are receiving increased attention, day by day, for automated quality inspection of roads. Industrial pavement scanners consist of laser scanners and are very expensive, hence inaccessible for everyone. The proposed work presents a simple and novel approach for 3D reconstruction of potholes for an automated inspection and road surface evaluation. The technique utilizes a Structure from Motion based 3D reconstruction algorithm, along with laser triangulation, to generate 3D point clouds of potholes. Alongside, a novel low-cost system, consisting of a single camera and a laser pointer, is also proposed. Keypoint matching techniques are employed, with the 5-point algorithm, on successive image frames to generate a point cloud. However, this point cloud is not metric yet, without scale information. The scale ambiguity is solved by making use of the laser pointer, and using the principle of triangulation. The laser spot is also detected in the same image sequence that is used for point-cloud building, cutting down the image capturing and processing overhead. The system has been benchmarked on artificial indentations with known dimensions, proving the robustness of the measurement scheme and hardware. Static and dynamic tests have been performed. The mean depth errors for measurement made by the imager statically and at dynamic speeds of 10 km/hr, 15 km/hr, and 20 km/hr are 5.3%, 7.9%, 14.4%, and 26.6%, whereas for perimeter the errors are 5.2%, 6.83 %, 11.8%, and 27.8%. The proposed, low-cost technique shows promising results in generating 3D point clouds for potholes.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Spatio-Temporal Knowledge Transfer for Urban Crowd Flow Prediction via
           Deep Attentive Adaptation Networks

    • Free pre-print version: Loading...

      Authors: Senzhang Wang;Hao Miao;Jiyue Li;Jiannong Cao;
      Pages: 4695 - 4705
      Abstract: Accurately predicting the urban spatio-temporal data is critically important to various urban computing tasks for smart city related applications such as crowd flow prediction and traffic congestion prediction. Existing models especially deep learning based approaches require a large volume of training data, whose performance may degrade remarkably when the data is scarce. Recent works try to transfer knowledge from the intra-city or cross-city multi-modal spatio-temporal data. However, the careful design of what to transfer and how between the multi-modal spatio-temporal data needs to be determined in advance. There still lacks an end-to-end solution that can automatically capture the common cross-domain knowledge. In this paper, we propose a Deep Attentive Adaptation Network model named ST-DAAN to transfer cross-domain Spatio-Temporal knowledge for urban crowd flow prediction. ST-DAAN first maps the raw spatio-temporal data of source domain and target domain to a common embedding space. Then domain adaptation is adopted on several domain-specific layers through adding a domain discrepancy penalty to explicitly match the mean embeddings of the two domain distributions. Considering the complex spatial correlation in many urban spatio-temporal data, a global attention mechanism is also designed to enable the model to capture broader spatial dependencies. Using urban crowd flow prediction as a demonstration, we conduct experiments on five real-world large datasets over both intra- and cross-city transfer learning. The results demonstrate that ST-DAAN outperforms state-of-the-art methods by a large margin.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Computationally Efficient Dynamic Traffic Optimization of Railway Systems

    • Free pre-print version: Loading...

      Authors: Robin Vujanic;Andrew J. Hill;
      Pages: 4706 - 4719
      Abstract: In this paper, we investigate dynamic traffic optimization in railway systems, i.e., the behavior of these systems through time when their movements are dictated by solutions to optimization models with finite horizons. As interactions between trains are not considered beyond the limits of finite horizons, the danger of leading the system into a deadlock arises. In this paper we present new procedures to establish finite prediction horizons that are formally guaranteed to operate the system in a way that is compatible with the physical constraints of the network while avoiding deadlocking and minimizing computations. The key to this result is the notion of recursive feasibility. This paper introduces conditions sufficient to attain it. We then discuss several important ramifications of recursive feasibility that enable efficient computations. We examine the possibility of decomposing the underlying optimization models into smaller models with shorter horizons, or into models that only consider subsets of all trains. We also discuss warm starting and anytime approaches. We finally perform numerical experiments verifying these results on models that include a real-world railway system used for freight transport. On harder instances, some of our approaches outperform solving the same models as monolithic MILPs by more than two order of magnitude in terms of median computation times, while also achieving better worst–case optimality gaps.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • An Efficient Data Acquisition System for Large Numbers of Various Vehicle
           Terminals

    • Free pre-print version: Loading...

      Authors: Shiting Wen;Yunjun Gao;Detian Zhang;Jinqiu Yang;Qing Li;
      Pages: 4720 - 4725
      Abstract: The continuing development of intelligent transportation terminals and the massive generated traffic data have placed tremendous pressure on traffic data acquisition. However, most of existing intelligent transportation systems and applications rely on well-defined data, while few studies focus on how to collect live traffic data from various vehicle terminals in a large number. To solve this problem, we propose an efficient and non-blocking data acquisition system in this paper, which can retrieve traffic data based on different priority or QoS requirements from a large number of various terminals in real-time, so that the application layer can easily access certain type of traffic data it needs. Extensive experiment and simulation results prove the efficiency, reliability, and scalability of our proposed system. Besides, two real applications based on the proposed system are introduced in the paper.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Graph-Enabled Intelligent Vehicular Network Data Processing

    • Free pre-print version: Loading...

      Authors: Zhigao Zheng;Ali Kashif Bashir;
      Pages: 4726 - 4735
      Abstract: Intelligent vehicular network (IVN) is the underlying support for the connected vehicles and smart city, but there are several challenges for IVN data processing due to the dynamic structure of the vehicular network. Graph processing, as one of the essential machine learning and big data processing paradigm, which provide a set of big data processing scheme, is well-designed to processing the connected data. In this paper, we discussed the research challenges of IVN data processing and motivated us to address these challenges by using graph processing technologies. We explored the characteristics of the widely used graph algorithms and graph processing frameworks on GPU. Furthermore, we proposed several graph-based optimization technologies for IVN data processing. The experimental results show the graph processing technologies on GPU can archive excellent performance on IVN data.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Technologies for Risk Mitigation and Support of Impaired Drivers

    • Free pre-print version: Loading...

      Authors: Christer Ahlström;Frederik Diederichs;Daniel Teichmann;
      Pages: 4736 - 4738
      Abstract: This editorial serves as an extended introduction to the Special Issue on Technologies for Risk Mitigation and Support of Impaired Drivers. It gives the context to recent advances in assisted and automated driving and the new challenges that arise when modern technology meets human users. The Special Issue focuses on the development of robust sensors and detection algorithms for driver state monitoring of fatigue, stress, and inattention, and on the development of personalized multimodal, user-oriented, and adaptive information, warning, actuation, and handover strategies. A summary of more recent developments serves as a motivation for each article that follows.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Predicting Driver’s Transition Time to a Secondary Task Given an
           in-Vehicle Alert

    • Free pre-print version: Loading...

      Authors: Steven Hwang;Ashis G. Banerjee;Linda Ng Boyle;
      Pages: 4739 - 4745
      Abstract: The goal of this study is to provide a framework, using hidden semi-Markov models, for modeling a driver’s response time after an alert is provided in manual driving. Given the plethora of alerts and warning within a vehicle, there is a need to understand when a driver will respond after an alert is provided. Data from a previous driving simulator study, where drivers were interacting with an in-vehicle information system (IVIS) were used for model training. The final data set included 16 participants, with 288 task initiations. The proposed model could predict a driver’s response time accurately using only a small portion of the available data, and had a mean absolute error of 0.51 seconds with 84% of predictions within an absolute error of 1 second. This framework has applicability in mitigating the risk of transitions in driver distraction. This includes transitions from the road to the secondary task and back to the road.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Automation Aftereffects: The Influence of Automation Duration, Test Track
           and Timings

    • Free pre-print version: Loading...

      Authors: Linda Pipkorn;Trent Victor;Marco Dozza;Emma Tivesten;
      Pages: 4746 - 4757
      Abstract: Automation aftereffects (i.e., degraded manual driving performance, delayed responses, and more aggressive avoidance maneuvers) have been found in driving simulator studies. In addition, longer automation duration seems to result in more severe aftereffects, compared to shorter duration. The extent to which these findings generalize to real-world driving is currently unknown. The present study investigated how automation duration affects drivers’ take-over response quality and driving performance in a road-work zone. Seventeen participants followed a lead vehicle on test track. They encountered the road-work zone four times: two times while driving manually, and after a short and a long automation duration. The take-over request was issued before the lead vehicle changed lane to reveal the road-work zone. After both short and long automation durations, all drivers deactivated automation well ahead of the road-work zone. Compared to manual, drivers started their steering maneuvers earlier or at similar times after automation (independently of duration), and none of the drivers crashed. However, slight increases in vehicle speed and accelerations were observed after exposure to automation. In sum, the present study did not observe as large automation aftereffects on the test track as previously found in driving simulator studies. The extent to which these results are a consequence of a more realistic test environment, or due to the duration between the timings for the take-over request and the conflict appearance, is still unknown.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Real-Time Adaptation of Driving Time and Rest Periods in Automated
           Long-Haul Trucking: Development of a System Based on Biomathematical
           Modelling, Fatigue and Relaxation Monitoring

    • Free pre-print version: Loading...

      Authors: Christer Ahlström;Wessel van Leeuwen;Stas Krupenia;Herman Jansson;Svitlana Finér;Anna Anund;Göran Kecklund;
      Pages: 4758 - 4766
      Abstract: Hours of service regulations govern the working hours of commercial motor vehicle drivers, but these regulations may become more flexible as highly automated vehicles have the potential to afford periods of in-cab rest or even sleep while the vehicle is moving. A prerequisite is robust continuous monitoring of when the driver is resting (to account for reduced time on task) or sleeping (to account for the reduced physiological drive to sleep). The overall aims of this paper are to raise a discussion of whether it is possible to obtain successful rest during automated driving, and to present initial work on a hypothetical data driven algorithm aimed to estimate if it is possible to gain driving time after resting under fully automated driving. The presented algorithm consists of four central components, a heart rate-based relaxation detection algorithm, a camera-based sleep detection algorithm, a fatigue modelling component taking time awake, time of day and time on task into account, and a component that estimates gained driving time. Real-time assessment of driver fitness is complicated, especially when it comes to the recuperative value of in-cab sleep and rest, as it depends on sleep quality, time of day, homeostatic sleep pressure and on the activities that are carried out while resting. The monotony that characterizes for long-haul truck driving is clearly interrupted for a while, but the long-term consequences of extended driving times, including user acceptance of the key stakeholders, requires further research.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Unobtrusive Measurement of Physiological Features Under Simulated and Real
           Driving Conditions

    • Free pre-print version: Loading...

      Authors: Lennart Leicht;Marian Walter;Marcel Mathissen;Christoph Hoog Antink;Daniel Teichmann;Steffen Leonhardt;
      Pages: 4767 - 4777
      Abstract: Objective: For driver state estimation, physiological features might be promising input parameters. As cable-bound sensing of these parameters is impractical for ubiquitous monitoring, the measurement certainly has to be based on unobtrusive and contact-free technologies. In this work, unobtrusive methods for heart rate (HR) and respiration rate (RR) monitoring, including a hybrid imaging approach, are evaluated under simulated and real driving conditions. Methods: The feasability of unobtrusive methods was tested by comparing measurements from unobtrusive sensors to reference sensors. Under laboratory conditions, magnetic induction and photoplethysmography, both integrated into the seat belt, and hybrid imaging, combining visual and thermal imaging, were evaluated for RR sensing. In real driving, creating an urban and a rural scenario, sensing of RR by hybrid imaging and sensing of HR by a seat-integrated capacitive ECG were evaluated. Results: Under laboratory conditions, a reliable RR detection was possibly using all three sensor technologies. In real-world driving, a reliable HR and RR detection was possible during the rural scenario. In the urban scenario, only the RR detection was feasible. Due to motion artifacts, the capacitive ECG was disturbed and the HR detection impaired. Conclusion: The evaluated unobtrusive measurement systems can monitor physiological parameters during e.g. long-time driving on highways, but may not yet be feasible for monitoring during agile inner-city driving situations, due to motion artifacts. Therefore, future work should focus on artifact reduction. Significance: Physiological features might be used as input parameters for driver state estimation systems. This work presents unobtrusive sensing methods for these parameters.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Towards a Context-Dependent Multi-Buffer Driver Distraction Detection
           Algorithm

    • Free pre-print version: Loading...

      Authors: Christer Ahlström;George Georgoulas;Katja Kircher;
      Pages: 4778 - 4790
      Abstract: This paper presents initial work on a context-dependent driver distraction detection algorithm called AttenD2.0, which extends the original AttenD algorithm with elements from the Minimum Required Attention (MiRA) theory. Central to the original AttenD algorithm is a time buffer which keeps track of how often and for how long the driver looks away from the forward roadway. When the driver looks away the buffer is depleted and when looking back the buffer fills up. If the buffer runs empty the driver is classified as distracted. AttenD2.0 extends this concept by adding multiple buffers, thus integrating situation dependence and visual time-sharing behaviour in a transparent manner. Also, the increment and decrement of the buffers are now controlled by both static requirements (e.g. the presence of an on-ramp increases the need to monitor the sides and the mirrors) as well as dynamic requirements (e.g., reduced speed lowers the need to monitor the speedometer). The algorithm description is generic, but a real-time implementation with concrete values for different parameters is showcased in a driving simulator experiment with 16 bus drivers, where AttenD2.0 was used to ensure that drivers are attentive before taking back control after an automated bus stop docking and depot procedure. The scalability of AttenD2.0 relative to available data sources and the level of vehicle automation is demonstrated. Future work includes expanding the concept to real-world environments by automatically integrating situational information from the vehicles environmental sensing and from digital maps.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • A Multi-Stage, Multi-Feature Machine Learning Approach to Detect Driver
           Sleepiness in Naturalistic Road Driving Conditions

    • Free pre-print version: Loading...

      Authors: Bram Bakker;Bartosz Zabłocki;Angela Baker;Vanessa Riethmeister;Bernd Marx;Girish Iyer;Anna Anund;Christer Ahlström;
      Pages: 4791 - 4800
      Abstract: Driver fatigue is a contributing factor in about 20% of all fatal road crashes worldwide. Countermeasures are urgently needed and one of the most promising and currently available approaches for that are in-vehicle systems for driver fatigue detection. The main objective of this paper is to present a video-based driver sleepiness detection system set up as a two-stage model with (1) a generic deep feature extraction module combined with (2) a personalised sleepiness detection module. The approach was designed and evaluated using data from 13 drivers, collected during naturalistic driving conditions on a motorway in Sweden. Each driver performed one 90-minute driving session during daytime (low sleepiness condition) and one session during night-time (high sleepiness condition). The sleepiness detection model outputs a continuous output representing the Karolinska Sleepiness Scale (KSS) scale from 1–9 or a binary decision as alert (defined as KSS 1–6) or sleepy (defined as KSS 7–9). Continuous output modelling resulted in a mean absolute error (MAE) of 0.54 KSS units. Binary classification of alert or sleepy showed an accuracy of 92% (sensitivity = 91.7%, specificity = 92.3%, F1 score = 90.4%). Without personalisation, the corresponding accuracy was 72%, while a standard fatigue detection PERCLOS-based baseline method reached an accuracy of 68% on the same dataset. The developed real-time sleepiness detection model can be used in the management of sleepiness/fatigue by detecting precursors of severe fatigue, and ultimately reduce sleepiness-related road crashes by alerting drivers before high levels of fatigue are reached.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Detecting Driver Sleepiness Using Consumer Wearable Devices in Manual and
           Partial Automated Real-Road Driving

    • Free pre-print version: Loading...

      Authors: Ke Lu;Johan Karlsson;Anna Sjörs Dahlman;Bengt Arne Sjöqvist;Stefan Candefjord;
      Pages: 4801 - 4810
      Abstract: Driver sleepiness constitutes a well-known traffic safety risk. With the introduction of automated driving systems, the chance of getting sleepy and even falling asleep at wheel could increase further. Conventional sleepiness detection methods based on driving performance and behavior may not be available under automated driving. Methods based on physiological measurements such as heart rate variability (HRV) becomes a potential solution under automated driving. However, with reduced task load, HRV could potentially be affected by automated driving. Therefore, it is essential to investigate the influence of automated driving on the relation between HRV and sleepiness. Data from real-road driving experiments with 43 participants were used in this study. Each driver finished four trials with manual and partial automated driving under normal and sleep-deprived condition. Heart rate was monitored by consumer wearable chest bands. Subjective sleepiness based on Karolinska sleepiness scale was reported at five min interval as ground truth. Reduced heart rate and increased overall variability were found in association with severe sleepy episodes. A binary classifier based on the AdaBoost method was developed to classify alert and sleepy episodes. The results indicate that partial automated driving has small impact on the relationship between HRV and sleepiness. The classifier using HRV features reached area under curve (AUC) = 0.76 and it was improved to AUC = 0.88 when adding driving time and day/night information. The results show that commercial wearable heart rate monitor has the potential to become a useful tool to assess driver sleepiness under manual and partial automated driving.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Physiological Measures of Risk Perception in Highly Automated Driving

    • Free pre-print version: Loading...

      Authors: Jaume R. Perello-March;Christopher G. Burns;Stewart A. Birrell;Roger Woodman;Mark T. Elliott;
      Pages: 4811 - 4822
      Abstract: Highly automated driving will likely result in drivers being out-of-the-loop during specific scenarios and engaging in a wide range of non-driving related tasks. Manifesting in lower levels of risk perception to emerging events, and thus affect drivers’ availability to take-over manual control in safety-critical scenarios. In this empirical research, we measured drivers’ (N = 20) risk perception with cardiac and skin conductance indicators through a series of high-fidelity, simulated highly automated driving scenarios. By manipulating the presence of surrounding traffic and changing driving conditions as long-term risk modulators, and including a driving hazard event as a short-term risk modulator, we hypothesised that an increase in risk perception would induce greater physiological arousal. Our results demonstrate that heart rate variability features are superior at capturing arousal variations from these long-term, low to moderate risk scenarios. In contrast, skin conductance responses are more sensitive to rapidly evolving situations associated with moderate to high risk. Based on this research, future driver state monitoring systems should adopt multiple physiological measures to capture changes in the long and short term, modulation of risk perception. This will enable enhanced perception of driver readiness and improved availability to safely deal with take-over events when requested by an automated vehicle.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Investigation of Three Potential Stress Inducement Tasks During On-Road
           Driving

    • Free pre-print version: Loading...

      Authors: Marcel Mathissen;Nikica Hennes;Fabian Faller;Steffen Leonhardt;Daniel Teichmann;
      Pages: 4823 - 4832
      Abstract: A driving study was performed to induce stress with 24 participants performing different inducement tasks (n-back task, Sing-a-Song Stress Test and noise exposure). Both performance-based measures (Tactile Detection Response Task) as well as subjective measures were recorded to assess the driver state. Subjective ratings indicate that stress was most successfully induced on a group level with the n-back task with high inter-individual variation. The average response times doubled during the n-back task for a simultaneously performed tactile detection response task compared to baseline response times. Sympathetic nervous activation resulting in the increase of heart rate, respiration rate and decrease in heart rate variability (RMSSD) was found as a physiological reaction on stress-inducing secondary tasks. The most prominent physiological responses were found during the modified Sing-a-Song Stress Test. Subjective ratings on the perceived stress level and physiological response rarely correlated. This study provided reference data for driver state algorithm development in the EU-funded project ADAS&ME.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Effects of Dynamic Visual Stimuli on the Development of Carsickness in
           Real Driving

    • Free pre-print version: Loading...

      Authors: Dominique Bohrmann;Anna Bruder;Klaus Bengler;
      Pages: 4833 - 4842
      Abstract: Whereas autonomous vehicles are expected to provide several advantages, the current scenarios envisioned for self-driving vehicles are expected to increase the incidence of motion sickness. This study investigates the effects of dynamic visual stimuli on the development of carsickness under two different view conditions. A prototypical light-emitting diode (LED) feedback system visualizing longitudinal driving dynamics in the passenger’s peripheral visual field was installed in the rear of a modified serial vehicle. A real driving experiment was conducted on the test track of a major car manufacturer. Subjective motion sickness ratings were recorded. It was hypothesized that carsickness can be mitigated with the information from the visual feedback system. Subjective motion sickness scores tended to be lower with the LED feedback system while there was no substantial interaction effect with the view condition. Although the results indicate potential benefits of the LED feedback system for the mitigation of motion sickness, further development of the system and its functionalities and the inclusion of psychophysiological measures to objectively quantify motion sickness is necessary to confirm these findings.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Improving Driver Performance and Experience in Assisted and Automated
           Driving With Visual Cues in the Steering Wheel

    • Free pre-print version: Loading...

      Authors: Frederik Diederichs;Arun Muthumani;Alexander Feierle;Melanie Galle;Lesley-Ann Mathis;Valeria Bopp-Bertenbreiter;Harald Widlroither;Klaus Bengler;
      Pages: 4843 - 4852
      Abstract: In automated driving it is important to ensure drivers’ awareness of the currently active level of automation and to support transitions between those levels. This is possible with $a$ suitable human-machine interface (HMI). In this driving simulator study, two visual HMI concepts (Concept $A$ and $B$ ) were compared with $a$ baseline for informing drivers about three modes: manual driving, assisted driving, and automated driving. The HMIs, consisting of LED strips on the steering wheel that differed in luminance, color, and pattern, provided continuous information about the active mode and announced transitions. The assisted mode was conveyed in Concept $A$ using $a$ combination of amber and blue LEDs, while in Concept $B$ only amber LEDs were used. During automated driving Concept $A$ displayed blue LEDs and Concept B, turquoise. Both concepts were compared to $a$ baseline HMI, with no LEDs. Thirty-eight drivers with driving licence were trained and participated. Objective measures (hands-on-wheel time, takeover time, and visual attention) are reported. Self-reported measures (mode awareness, trust, user experience, and user acceptance) from $a$ previous publication are briefly repeated in this context (Muthumani et al.). Concept- $A$ showed 200 ms faster hands-on-wheel times than the baseline, while in Concept $B$ several outliers were observed that prevented significance. The visual HMIs with LEDs did not influence the eyes-on-road time in any of the automation levels. Participants preferred Concept B, with more prominent differentiation between the automation levels, over Concept A.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Augmented Reality Head-Up Display: A Visual Support During Malfunctions in
           Partially Automated Driving'

    • Free pre-print version: Loading...

      Authors: Alexander Feierle;Fabian Schlichtherle;Klaus Bengler;
      Pages: 4853 - 4865
      Abstract: Malfunctions are a major challenge in partially automated driving. During such malfunctions, the driver must be able to adequately take over vehicle guidance without being requested to intervene. This may be particularly difficult in urban areas due to their high complexity and information density. Augmented Reality Head-Up Displays (ARHUDs) may have the potential to support the driver during the monitoring task by providing driving-related information at its required location in the primary field of view. The effects of an ARHUD compared to a Baseline concept in case of malfunctions were investigated in a driving simulation experiment with 52 participants. In a partially automated urban drive, participants experienced a longitudinal and a lateral malfunction in permuted order. The concepts–ARHUD or Baseline– were presented as a between-subject factor. The results showed significantly shorter take-over times when using the ARHUD, resulting in fewer crashes. For those who were able to avoid the crash, no differences in the take-over quality between both concepts were found. There was one difference in visual attention: the attention ratio on the instrument cluster was lower for the ARHUD. In addition, the ARHUD revealed a significantly higher trust and usability rating. However, there were no differences in acceptance and subjective workload between the two concepts. The results showed that the ARHUD has more potential to prevent crashes in the event of malfunctions compared to the Baseline. Nevertheless, the high number of crashes, regardless of the concept, showed the importance of a fallback level for partially automated urban driving.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Adaptive User Experience in the Car—Levels of Adaptivity and
           Adaptive HMI Design

    • Free pre-print version: Loading...

      Authors: Lena Rittger;Doreen Engelhardt;Robert Schwartz;
      Pages: 4866 - 4876
      Abstract: Advancements in driver state detection and artificial intelligence allow for more and more user-centred and individual experiences. Intelligence and adaptivity in the vehicle context address the three main goals: Increasing safety, usability and empathy in vehicle systems. Adaptivity of systems can be evaluated by considering the technical system features, user-interface-related features and the actual user experience of the adaptive system. We provide an overview of classifications for adaptive systems including the Levels of Adaptive Sensitive Responses (LASR). The levels differentiate the input that a system considers in its operations to adapt to user groups or to the individual user. Along with that, we propose User Experience (UX) design guidelines applicable to the different levels. In an online survey, we varied LASR and one of the UX design guidelines, namely transparency. The within-subjects study showed that both, the levels and the variation of transparency, influenced the perception of intelligence, transparency and intuitive design. However, a significant proportion of users did not understand the difference between the two LASR versions, indicating that users build mental models of systems that imply more personal data usage than the system actually employs. The LASR framework allowed this differentiation to be revealed in system performance and user perception. More research is necessary to elaborate the correlation between levels of adaptivity, UX design, specific UX design guidelines and user experience measures.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
  • Using Glance Behaviour to Inform the Design of Adaptive HMI for Partially
           Automated Vehicles

    • Free pre-print version: Loading...

      Authors: Arun Ulahannan;Simon Thompson;Paul Jennings;Stewart Birrell;
      Pages: 4877 - 4892
      Abstract: Partially automated vehicles present a large range of information to the driver in order to keep them in-the-loop and engaged with monitoring the vehicle’s actions. However, existing research shows that this causes cognitive overload and disengagement from the monitoring task. Adaptive Human Machine Interfaces (HMIs) are an emerging technology that might address this problem, by prioritising the information presented. To date, research aiming to define the driver’s glance fixation behaviour in a partially automated vehicle to contribute towards an adaptive interface is scarce. This study used a unique three-day longitudinal driving simulator study design to explore which information drivers in a partially automated vehicle require. Twenty-seven participants experienced nine partially automated driving simulations over three consecutive days. Nine information types, developed from standards, previous studies and industry collaboration, were displayed as discrete icons and presented on a surrogate in-vehicle display. Unique to the literature, this study showed that the recorded eye-tracking data demonstrated that usage of the information types changed with longitudinal driving simulator use. This study provides three key contributions: first, the longitudinal study design suggest that single exposure HMI evaluations may be limited in their assessment. Secondly, this study has methodologically shortlisted a list of nine information types that can be used in future studies to represent future partially automated vehicle interfaces. Finally, this is one of the first studies to characterise glance behaviour for partially automated vehicles. With this knowledge, this study contributes important design recommendations for the development of adaptive interfaces.
      PubDate: May 2022
      Issue No: Vol. 23, No. 5 (2022)
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 34.231.147.28
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-