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Intelligent Transportation Systems Magazine, IEEE
Journal Prestige (SJR): 0.816
Citation Impact (citeScore): 4
Number of Followers: 12  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 1939-1390
Published by IEEE Homepage  [228 journals]
  • TechRxiv: Share Your Preprint Research With the World!

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      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • IEEE Collabratec

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      Abstract: Advertisement.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • IEEE Foundation

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      Abstract: Advertisement.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • Cross-Border, Interoperable Cooperative Intelligent Transportation
           Systems: Could We Make Them Operational' [Editor’s Column]

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      Authors: Ljubo Vlacic;
      Pages: 3 - 4
      Abstract: Aimed at increasing people’s mobility, the Cooperative Intelligent Transportation Systems (C-ITS) paradigm postulates that all modes of transport, and their means, are interconnected and their operations synchronized in time. This principle is also imposed on C-ITS infrastructures as well as services provided by C-ITS solutions. All of these inevitably require interconnectivity and interoperability among all C-ITS hard and soft components, products, and services. In return, such operational conditions make C-ITS components interdependable.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • IEEE App

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      Pages: 4 - 4
      Abstract: Advertisement.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • Connected and Automated Driving Systems [President’s Message]

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      Authors: Cristina Olaverri-Monreal;
      Pages: 5 - 245
      Abstract: Cooperative, automated driving systems represent an opportunity to enhance mobility and increase safety. The highest levels of automation will make human intervention superfluous as automation will be in charge of driving subtasks. This will not only lead to a decrease in the rate of accidents caused by human errors but also to a reduction of air congestion and pollution. In addition, exemption from driving duties will give passengers the freedom to perform other tasks during their trip.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • Cross-Border Interoperability for Cooperative, Connected, and Automated
           Driving

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      Authors: José E. Naranjo;Felipe Jiménez;Rodrigo Castiñeira;Mauro Gil;Cristiano Premebida;Pedro Serra;Alberto Valejo;Fawzi Nashashibi;Conceição Magalhães;
      Pages: 6 - 19
      Abstract: The implementation on a massive scale of safe, highly automated driving is very difficult using only information from ego vehicles, which are subject to visual horizon limitations. For automated driving systems to become a reality, it is essential to provide them with two fundamental elements, among others: connectivity and cooperative services. Both elements are still at a very early stage of development in communications technology and in the organization and generation of support information. In addition, extra difficulties are presented, such as transnational barriers to accessing services and exchanging information with other vehicles and infrastructure. This article presents the implementation of a novel architecture to support the integration of cooperative intelligent transportation systems in automated driving, including the results of cross-border interoperability tests carried out in three cooperative, connected, and automated driving (CCAD) pilots that were deployed in three cities belonging to the Trans-European Atlantic Corridor—Madrid, Spain; Lisbon, Portugal; and Paris, France—under the framework of the European Regulation Study for Interoperability in the Adoption of Autonomous Driving in European Urban Nodes. These results show the performance of the CCAD architecture and have been analyzed to include a set of recommendations to ensure the successful deployment of CCAD driving at the European level.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • The Tornado Project: An Automated Driving Demonstration in Peri-Urban and
           Rural Areas

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      Authors: Vicente Milanés;David González;Francisco Navas;Imane Mahtout;Alexandre Armand;Clément Zinoune;Arunkumar Ramaswamy;Farid Bekka;Nievsabel Molina;Emmanuel Battesti;Yvon Kerdoncuff;Carlos Guindel;Jorge Beltrán;Irene Cortés;Alejandro Barrera;Fernando Garcia;
      Pages: 20 - 36
      Abstract: This article presents the results of a two-week robot taxi service demonstration in peri-urban and rural areas. A fully robotized Renault Zoe was available for general public use in Rambouillet, France. The driving zone included several complex scenarios, such as a narrow two-way road, a tunnel where the lanes reduced from two to one, and roundabouts, enabling an evaluation of the vehicle’s capabilities. The article describes the scientific and technical development of the car’s perception, navigation, and control to carry out the experiment. The results indicate that even though the vehicle was able to autonomously navigate suburban areas and the countryside, technical challenges remain that limit the car’s integration with the transport system.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • Discover Trip Purposes From Cellular Network Data With Topic Modeling

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      Authors: Xueliang Zhao;Zhishuai Li;Yu Zhang;Yisheng Lv;
      Pages: 37 - 46
      Abstract: The widespread use of mobile phones has generated a large amount of individual trajectory data. Such data can greatly help analyze and understand human daily travel behavior. In this paper, we use the topic modeling technique to infer trip purposes based on pseudonymized users’ trajectory data from cellular network and points of interest (POIs) from online map services. The adapted latent Dirichlet allocation method is used to model the trip generation process and then infer trip purposes behind the data. The experiments are performed on a data set of 27,732 trip records in Beijing on weekdays. Ten topics are discovered. This method can easily infer different trip purposes based on three trip attributes, i.e., trip departure time, stay duration, and POI categories for destinations, and most of the topics/trip purposes are explainable.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • Research on Vehicle Adaptive Real-Time Positioning Based on Binocular
           Vision

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      Authors: Su Zhou;Gang Zhang;Ran Yi;Zhengchun Xie;
      Pages: 47 - 59
      Abstract: The positioning of a self-driving car is the basis of vehicle navigation and decision planning. To solve the problem of localization of self-driving cars in scenarios where GPS signals are missing, this article proposes an adaptive real-time positioning method, which is based on the binocular vision simultaneous localization and mapping method. The front-end visual odometry module uses the gray threshold adaptive oriented FAST and rotated BRIEF feature point extraction method for feature matching and pose calculation. The back end performs optimization processing based on the camera pose and the loop closure detection information estimated by the front end. Before optimization, we developed a rough dynamic feature elimination method to increase the robustness and positioning accuracy. Thus, a globally consistent positioning trajectory was obtained. To verify the actual positioning effect of the positioning system, the algorithm was tested using the KITTI data set and an experimental platform was built to test it in outdoor scenarios. The experimental results showed that the loop closure optimization and dynamic point elimination methods improved the positioning accuracy. The loop closure optimization significantly improved the actual positioning accuracy, and the dynamic feature elimination method slightly improved the positioning accuracy in dynamic scenes.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • Road Boundary-Enhanced Automatic Background Filtering for Roadside Lidar
           Sensors

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      Authors: Jianqing Wu;Hao Xu;Renjuan Sun;Peizhi Zhuang;
      Pages: 60 - 72
      Abstract: The roadside-deployed lidar sensor provides a solution to obtain the real-time, high-resolution micro traffic data (HRMTD) of all road users in the mixed traffic situation (connected vehicles and unconnected vehicles both exist on the roads). Background filtering is a necessary and important step for the HRMTD collection to serve the connected vehicle. Without excluding the background points, the accuracy of Euclidean-based object clustering and tracking algorithms can be reduced. The widely used method-3D-density-statistic filtering (3D-DSF) for roadside lidar background filtering can effectively exclude the background points for free-flow conditions. However, the performance of 3D-DSF can be greatly influenced by congested traffic conditions. This article presents a revised 3D-DSF algorithm to automatically extract background points by involving the road-boundary information. This new method is named road-boundary-enhanced, 3D-density statistic filtering (3D-DSFRB). This algorithm involves the boundary of the historical trajectories of road users as the region of interest (ROI) to enhance the accuracy of background filtering. A revised grid-based method was developed for road-boundary ID. The 3D-DSF was only applied for the area outside of the ROI. Within the ROI, only ground surface was excluded. Case studies were conducted to evaluate the effectiveness of the 3D-DSFRB algorithm. The results showed that the 3D-DSFRB can filter background points for both free-flow conditions and congested traffic conditions. The time cost of the 3D-DSFRB was also reduced compared to the 3D-DSF. Compared to the state of the art, the 3D-DSFRB improved the accuracy of background filtering.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • Understanding and Predicting the Short-Term Passenger Flow of Station-Free
           Shared Bikes: A Spatiotemporal Deep Learning Approach

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      Authors: Ximing Chang;Ziyan Feng;Jianjun Wu;Huijun Sun;Guang Wang;Xu Bao;
      Pages: 73 - 85
      Abstract: Accurate passenger flow prediction of shared bikes provides useful information for operators to optimize the repository of bikes in different regions. However, it is very challenging as the usage of shared bikes is affected by many complex factors. In this article, we propose an end-to-end deep learning architecture, termed spatial-temporal fusion network (STFNet), to forecast short-term passenger flow in the new station-free bike-sharing system. The architecture utilizes the different neural network structures jointly to capture the complex nonlinear relationships of spatiotemporal dependencies and external factors from multiple data sources. Furthermore, the attention mechanism is introduced to improve the model’s interpretability and prediction ability. Based on two real-world data sets collected from Chinese cities, Beijing and Shenzhen, detailed spatiotemporal usage patterns of shared bikes are analyzed. Ablation studies are performed to test the effectiveness of different components on the whole framework. Experiment results show that the proposed STFNet can effectively capture the spatiotemporal correlations, and the predictions outperform state-of-art baselines in different predicting horizons.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • The Robustness and Prewarning for the Real-Time Service of Station-Based
           Bike-Sharing Systems Under Normal Operation

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      Authors: Wenbin Zhang;Zihao Tian;Lixin Tian;David Z.W. Wang;Yi Yao;
      Pages: 86 - 101
      Abstract: This article focuses on exploring the robustness for the real-time service of station-based bike-sharing (SBSS) systems under normal operation and calculates the more accurate station thresholds as an early warning for real-time service. We propose new robust indicators and strategies and complete the empirical analysis. The results showed that [0.19,0.82] is the new station threshold for the Nanjing SBSS system, which will reduce the number of stations with rebalancing demands down by 2.18% under the flow-type windows within one week. Therefore, the results will be better for operators to develop more cost-effective management strategies. Moreover, the indicators and methods are general and applicable to SBSSs in other cities as the Nanjing SBSS is a system with many sites, wide coverage, massive usage, and an uneven distribution of records in time and space.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • The Prediction of Urban Road Traffic Congestion by Using a Deep Stacked
           Long Short-Term Memory Network

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      Authors: Tong Wang;Azhar Hussain;Qi Sun;Shengbo Eben Li;Cao Jiahua;
      Pages: 102 - 120
      Abstract: Traffic congestion is an overwhelming problem faced by road travelers all over the world. A time-efficient and accurate prediction of upcoming traffic congestion can reduce this problem through enabling the proactive planning of routes. Recent research suggests that prediction accuracy requires the extraction of hidden features of the road network from the historical traffic data. In general, this data is either limited (with a longer sampling time) or not provided by providers. In urban areas, traffic lights, weather conditions, city events, accidents, and people’s habits significantly influence the traffic flow according to the structure of road network. Therefore, a mechanism is required to extract traffic data by scraping images from the route planners’ websites to predict traffic congestion. In this article, we devise such a method and introduce a fuzzy logic and stochastic estimation algorithm to detect congestion levels at the intersections of the road network. We then build a deep stacked long short-term memory network, in combination with online training, for the multipoint future prediction of congestion. We name the proposed model a fuzzy logic and deep learning-based traffic congestion predictor (FDLTCP) and compare the proposed predictor with the gated recurrent unit and stacked auto-encoders. Experimental evaluations demonstrate the effectiveness of FDLTCP, in terms of mean square error and other critical performance metrics, to perform future predictions.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • Resilience Characterization for Multilayer Infrastructure Networks

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      Authors: Mehmet Baran Ulak;Lalitha Madhavi Konila Sriram;Ayberk Kocatepe;Eren Erman Ozguven;Reza Arghandeh;
      Pages: 121 - 132
      Abstract: Catastrophic weather has significantly battered the U.S. Gulf Coast in recent years and exposed critical deficiencies in the resilience across communities and organizations. These deficiencies compel the devising of strategies to identify critical infrastructure components that require more attention with regard to building resilience. This article presents a holistic approach to assessing urban resilience by studying the coresilience of infrastructure networks. For this purpose, Tallahassee, Florida is used as a case study with a focus on both power and roadway networks and includes real-life disaster data from three extreme weather events that recently hit the study area. This article contributes to the coresilience concept through: 1) developing a geographical information system-based information-gathering approach to obtain an integrated infrastructure network and feed the causality models, 2) developing novel coresilience metrics to spatially identify and evaluate the high-risk locations, and 3) presenting a comprehensive case study and application of the developed approaches by using real-life data from three major storms that hit the study area.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • Lane-Based Traffic Arrival Pattern Estimation Using License Plate
           Recognition Data

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      Authors: Chengchuan An;Xiaoyu Guo;Rongrong Hong;Zhenbo Lu;Jingxin Xia;
      Pages: 133 - 144
      Abstract: Understanding the traffic arrival process and its patterns is of vital importance for delay and queue analysis at intersections. The installation of advance loop detectors to sense vehicle arrivals could be costly and biased. Utilizing sampled vehicle trajectory data to reconstruct the traffic arrival flow might suffer from small sample sizes. License plate recognition (LPR) data commonly available at intersections in cities in China are promising for overcoming such limitations. This study aims to estimate a lane-based traffic arrival pattern by using LPR data collected at downstream and upstream intersections. The proposed method develops a probability model with an assumption of a two-stage piecewise arrival process for upstream merge movements. Given observations of vehicle arrivals provided by matched cars and trucks in LPR data, the model estimates second-based mean arrival rates for each lane at the downstream intersection. The proposed method is validated using actual LPR data collected at two adjacent intersections in Kunshan City, China. The results demonstrate that the proposed method can describe the traffic arrival patterns of upstream merge movements with either two-stage or uniform arrival processes in different traffic scenarios. In addition, the proposed method is more robust and reliable than an average-based benchmark method in terms of revealing actual traffic arrival patterns under different match rates.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • Parallel Hierarchical Control-Based Efficiency Enhancement for Large-Scale
           Virtual Reality Traffic Simulation

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      Authors: Weizhi Qiu;Wei ShangGuan;Linguo Chai;BaiGen Cai;Junjie Chen;
      Pages: 145 - 162
      Abstract: Virtual reality (VR)-based traffic simulation plays an important role in the study of transportation systems, especially in the testing of connected and autonomous vehicles. However, a simulator that provides a large-scale traffic scene with high fidelity is still not available. To address this issue, the parallel hierarchical control method is proposed in this article, which presents a framework that enables the efficient simulation of a VR-based large-scale traffic scene. First, the original data of the basic traffic components are acquired through a modification and generation method. Then, based on the proposed method for spatial parallel slicing, the prepared data and simulation tasks are separated and distributed to subcontrollers considering the connected-vehicle environment. Meanwhile, a fidelity loss-based hierarchical control method is integrated to stratify the separate data into multiple levels. Finally, the experiments are carried out on a virtual driving platform that indicates that the proposed approach effectively ensures the fidelity of the visualized scenes and performs a better allocation of computational resources.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • An Offline Framework for the Diagnosis of Transfer Reliability Using
           Automatic Vehicle Location Data

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      Authors: Benedetto Barabino;Massimo Di Francesco;Giulio Maternini;Sara Mozzoni;
      Pages: 163 - 182
      Abstract: In public transit networks, transfers occur when more than one route is used to connect origins and destinations. Much research has been done to investigate the design and management of transfers at the tactical and operational levels. However, very few studies have investigated the monitoring phase to check a posteriori if transfers are well planned and/or delivered according to archived automatic vehicle location (AVL) data. This article covers this gap by proposing the first offline framework. This framework preprocesses AVL raw data, performs a diagnosis of transfer reliability over all bus stops and time periods, and discloses the most common sources of transfer unreliability. Easy-to-read control dashboards show the viability of this framework on real bus routes with approximately 145,000 AVL data records to make an accurate transfer analysis and possible service adjustments.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • Investigating the Longitudinal Impact of Cooperative Adaptive Cruise
           Control Vehicle Degradation Under Communication Interruption

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      Authors: Weijie Yu;Xuedong Hua;Wei Wang;
      Pages: 183 - 201
      Abstract: Cooperative adaptive cruise control (CACC), which is recognized as an extension of adaptive cruise control (ACC) through the addition of vehicle-to-vehicle (V2V) communication, is promoted for application in the near future. Nevertheless, V2V communication is unreliable due to the communication interruption caused by cyberattacks and information congestion. When communication interruption occurs, a CACC vehicle cannot receive the travel information of the preceding vehicle and automatically degrades to an ACC vehicle, leading to the instability of the CACC fleet. This research aims to investigate the longitudinal impact of CACC vehicle degradation under communication interruption. The realistic CACC model and ACC model are used for constructing simulation experiments with a fleet of 10 CACC vehicles. By simulating multiple driving, degradation, and communication-interruption scenarios, we investigate the impact of CACC degradation on fleet stability, energy consumption, and exhaust emissions. Finally, we conduct sensitivity analysis on the key factors of driving models and simulation scenarios. The result shows that CACC vehicle degradation, especially the successive degradation of adjacent vehicles under the acceleration scenario, would reduce average travel speed and increase energy consumption and exhaust emissions. The findings highlight some key scenarios for actively monitoring communication interruption and provide some prospective actions to reduce the impact of CACC degradation.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • A Bayesian Game-Based Train Protection Method Using Train-to-Train
           Communication

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      Authors: Wei Wu;Haifeng Song;Zixuan Zhang;Shiyao Zhang;Jochen Trinckauf;Hairong Dong;
      Pages: 202 - 213
      Abstract: Train-to-wayside communication cannot satisfy growing demands for safety and efficiency on high-speed railways. In this article, a novel train control system is presented based on train-to-train (T2T) communication and mobile edge computing (MEC). The T2T approach can shorten communication times and optimize the control performance. The scheme is described and compared to the traditional communication system, considering parameters that can result in poor service quality. Additionally, the application of MEC is introduced in a novel wireless access network. Then, a Bayesian game-based protection method is proposed to ensure operation safety and efficiency. Considering bit errors, packet losses, and broken connections, train and communication system cost functions are designed. The results show that the proposed strategy can reduce the impact of unreliable communication on the safe operation of trains.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • A Railway Turnout Closeness Monitoring Method Based on Switch Gap Images

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      Authors: Chao Li;Linhai Zhao;
      Pages: 214 - 229
      Abstract: The railway turnout system is a critical infrastructure that is responsible for steering trains. The closeness state of the turnout is directly related to the safety of the passing trains. To avoid derailment accidents caused by insufficient turnout closeness degrees, it is necessary to monitor the closeness degrees of the turnout. This article introduces a new condition-monitoring strategy that evaluates the turnout closeness degree by measuring the size of the switch gap. To implement this strategy, a detector based on image sensors is designed, and an automatic algorithm is proposed to measure the size of the switch gap based on the images acquired by the detector. The proposed algorithm directly processes the original images of the switch gap and can adaptively extract the region of interest (ROI) and the hysteresis threshold of the Canny operator for each image, which enables workers to avoid using a fixed ROI and hysteresis threshold with different images. Finally, the turnout closeness degree is calculated through a simple conversion of the size of the switch gap, measured by the proposed algorithm. Experiments based on data collected from the actual turnouts of a station show that the proposed turnout closeness monitoring method has high accuracy and robustness, which simplifies the turnout closeness maintenance process and improves its efficiency.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • Multiobjective Optimization on the Operation Speed Profile Design of an
           Urban Railway Train With a Hybrid Running Strategy

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      Authors: Qian Pu;Xiaomin Zhu;Runtong Zhang;Jian Liu;Dongbao Cai;Guanhua Fu;
      Pages: 230 - 243
      Abstract: In urban railway systems, a predefined speed profile helps train drivers and automatic train operation systems realize eco-driving without ignoring the requirements for punctuality and comfort. This study proposes a multiobjective optimization method for speed profiles of urban railway systems and provides a Pareto front in three dimensions with a hybrid running strategy. First, three popular running strategies are selected and combined. Second, a model of train behavior is constructed, and energy efficiency, running time, and comfort are used to evaluate the speed profiles. Third, a hybrid strategy multiobjective particle swarm optimization algorithm is proposed and utilized with train performance simulation to solve this problem. Finally, two running sections of Beijing subway line 8 are applied in a case study. The results verify the effectiveness of the proposed approach and imply that a single strategy should not be abandoned when comfort is a consideration.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • [PH.D. & M.PHIL. Theses' Abstracts]

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      Pages: 244 - 245
      Abstract: Presents summaries of recent PhD thesis from select researchers.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • The Autonomous Vehicles and Electronics Lab [ITS Research Lab]

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      Authors: Yisheng Lv;
      Pages: 246 - 250
      Abstract: The Korea Advanced Institute of Science and Technology (KAIST) was established in 1971 as a research-oriented university by the government of South Korea. KAIST currently leads the R&D of innovative technologies in South Korea with its 646 faculty members and 10,793 students, the majority of whom are graduate students. The five colleges at KAIST are equipped with state-of-the-art facilities, enabling KAIST-affiliated research groups to conduct cutting-edge research in almost all science and engineering fields, one of which is intelligent systems.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • ITS Society Conferences [Conference Reports]

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      Pages: 251 - 251
      Abstract: Presents information on ITS society conferences.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
  • [Calendar]

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      Pages: 252 - 252
      Abstract: Presents the ITS society calendar of upcoming events and meetings.
      PubDate: July-Aug. 2022
      Issue No: Vol. 14, No. 4 (2022)
       
 
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