 Subjects -> ELECTRONICS (Total: 207 journals)
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- 26th IEEE International Conference on Intelligent Transportation Systems
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PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- IEEE Proceedings
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PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- TechRxiv: Share Your Preprint Research With the World!
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PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- IEEE Intelligent Transportation Systems Magazine Is Firmly on Its
Trajectory and Smoothly Sailing Ahead [Editor’s Column]-
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Authors:
Ljubo Vlacic;
Pages: 3 - 271
Abstract: One of the most treasured gifts we can bestow to someone is the gift of our knowledge and our dedication to share it with young fellows, peers, and the Society at large. The gift of our knowledge is a selfless act, one that is given without expectation of reciprocation and is given for the betterment and growth of IEEE Intelligent Transportation Systems Magazine’s (ITSM’s) readership, the IEEE Intelligent Transportation Systems Society (ITSS), and the technical disciplines it addresses. We share our knowledge by way of researching into unknown phenomena, by writing papers and reports on the findings we have achieved through research and/or professional practice, by undertaking projects on challenging issues, and by reviewing the quality of each other’s work. We meet at ITSS conferences to share our wisdom, discuss our findings, and explore synergies among them. Our dedication to knowledge sharing is ITSM’s primary existential pillar. All of us together, manuscript authors, reviewers, editors, and readers, are the source of ITSM’s strength.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- Extending the Bonds of the IEEE Intelligent Transportation Systems Society
in Latin America [President’s Message]-
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Authors:
Cristina Olaverri-Monreal;
Pages: 4 - 5
Abstract: This past November, one of the IEEE Intelligent Transportation Systems Society’s (ITSS’s) most successful events in recent years took place: the ITSS Latin America Networking and Research Event. The Universidad Piloto de Colombia in Bogotá provided its facilities to this end. The rector and dean of the Universidad Piloto de Colombia as well as representatives of its International Relations Office highlighted the importance of establishing relationships worldwide to advance the field of smart mobility and transportation. Representatives of the Ministry of Transport together with Transmilenio also contributed with an overview of the measures currently taken to address traffic-related challenges in Bogotá and to move toward more efficient and sustainable transport and mobility.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- IEEE App
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Pages: 5 - 5
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- Identifying License Plates in Distorted Vehicle Images: Detecting
Distorted Vehicle Licence Plates Using a Novel Preprocessing Methods With
Hybrid Feature Descriptors-
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Authors:
Meeras Salman Al-Shemarry;Yan Li;Shahab Abdulla;
Pages: 6 - 25
Abstract: Intelligent transportation systems (ITSs) play a key role in many people’s lives with different aspects. Most of the ITS applications include a system for detecting license plates (LPs) in transportation vehicles. A new framework for such detection systems is introduced in this article. It includes a novel technique for preprocessing, extraction, and detection stages to identify LPs from distorted vehicle images. An efficient preprocessing method is developed in this study. An enhanced, contrast-limited adaptive histogram equalization method for filtering the unwanted LP features is proposed. At the features-extraction stage, strong hybrid features from extended local binary patterns with a median robust pattern descriptor and speeded-up robust feature descriptor are applied to extract complicated features from LP areas. Those hybrid features can enhance the useful information, and the detection system performed well under difficult scenarios. In the detection stage, the trained model by an extreme learning machine (ELM) classifier, with mean-shift algorithm, is used as a detector to decide output results. Performances of the proposed framework were compared with other classifiers using true- and false-positive rates. The system’s performance improvements by the proposed method were also compared with our previous methods and the existing detection methods in the literature. The experiments on an English car LP’s database and other language vehicle images showed that the proposed method made significant improvements to the accuracy and runtime speed for the detection system under difficult image conditions.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- A Two-Stage Optimization Algorithm of the Train Traction Energy
Consumption in Urban Rail Transit-
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Authors:
Lihe Guan;
Pages: 26 - 40
Abstract: The optimization of the train traction energy consumption in urban rail transit is a nonlinear optimization problem, and its solution is a very difficult task. This article focuses on the optimization of train traction energy consumption on the line with multiple stations. First, a single-objective nonlinear energy-saving optimization model is established for a given line and total travel time. Second, a two-stage optimization algorithm is proposed to solve this model. In the first stage, for a given interval and travel time, an optimization model of the traction energy consumption is established by using the distance discretization method. A dichotomous iterative algorithm based on energy consumption is proposed to search the optimal traction energy consumption. By calling this algorithm repeatedly, the energy–time curve of each interval can be obtained. In the second stage, according to these energy–time curves, the total travel time of a train on the line is allocated to each interval one by one in the form of time slices. And finally the optimal travel time and optimal traction energy consumption of the train on each interval are obtained. This method does not need to set the train operation mode sequence in advance but adaptively selects the energy-saving operation mode according to the line constraints and the train parameters. The up-direction from Yizhuang to Tongjinan of the Beijing Metro Yizhuang line in China is selected as the test section. The experimental results demonstrate the effectiveness and computational efficiency of our proposed methods and algorithms. Moreover, the dichotomous iterative algorithm runs so fast that it can be used in real-time driver advisory systems or automatic train operation systems.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- X-CAR: An Experimental Vehicle Platform for Connected Autonomy Research
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Authors:
Goodarz Mehr;Prasenjit Ghorai;Ce Zhang;Anshul Nayak;Darshit Patel;Shathushan Sivashangaran;Azim Eskandarian;
Pages: 41 - 57
Abstract: Autonomous vehicles (AVs) promise a future with safer, cleaner, more efficient, and more reliable transportation. However, the current approach to autonomy has focused on building small, disparate intelligences that are closed off to the rest of the world. Vehicle connectivity has been proposed as a solution, relying on a vision of the future where a mix of connected autonomous and human–driven vehicles populate the road. Developed by the U.S. Department of Transportation Federal Highway Administration (FHWA) as a reusable, extensible platform for controlling connected autonomous vehicles (CAVs), the CARMA Platform, is one of the technologies enabling this connected future. Nevertheless, the adoption of CARMA has been slow, with one contributing factor being the limited, expensive, and relatively old vehicle configurations that are officially supported. To alleviate this problem, we propose the eXperimental vehicle platform for Connected Autonomy Research (X-CAR). By implementing the CARMA Platform on more-affordable, high-quality hardware, X-CAR aims to increase the versatility of the FHWA platform and facilitate its adoption for research and development into connected driving automation.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- Electric Fences for Dockless Bike-Sharing Systems: An Electric
Fence-Planning Framework for a Dockless Bike-Sharing System Based on a
Land Parcel Subdivision and Regional Coverage Maximization-
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Authors:
Xiaoying Shi;Ziyi Liang;Dewen Seng;
Pages: 58 - 69
Abstract: Dockless bike sharing (DBS) is convenient for bike users because it can solve their last-mile problems. However, dockless bikes that are disorderly parked in public spaces can disturb normal traffic. In planning electric fences for DBS systems, the maximum coverage location problem (MCLP) identifies locations suitable for electric fences. However, the MCLP model ignores the regional boundaries of bike use and causes substantial errors in coverage assessments. In this article, we present a novel framework for planning electric fences, accounting for both trip distribution and area shapes. We first introduce a dynamic land parcel subdivision algorithm, which can reasonably divide the city into fine-scale regions concerning the distributions of spatial parking demands. Then, we formulate the electric fence-planning problem as a regional coverage maximization problem. We develop an accelerated maximum coverage model with complementary coverage (MCMCC) to locate electric fence areas, which can improve solution accuracy and reliability. The DBS data of Shenzhen, China, are used to validate the framework. The study findings indicate that MCMCC produces more robust and accurate results than the MCLP model because the planning solutions are not affected by changes in the shapes and sizes of the regions. The framework can also automatically and accurately determine electric fence capacities and locations.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- A Survey of the Social Internet of Vehicles: Secure Data Issues,
Solutions, and Federated Learning-
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Authors:
Ling Xing;Pengcheng Zhao;Jianping Gao;Honghai Wu;Huahong Ma;
Pages: 70 - 84
Abstract: The Social Internet of Vehicles (SIoV) is constructed so that the vehicle, as an entity, generates social awareness, which leads to the social behavior of the vehicle. The SIoV has multidimensional, heterogeneous, massive, real-time dynamic data, so its data security is a major concern. The structure of the SIoV and the characteristics of security primitives are studied in this review, which defines the social community by the vehicle. After this, the attack model is elaborated on, further refining the source, target, and capabilities of the attack. On this basis, typical data security solutions are compared and analyzed. In this survey, the focus is on the collaborative application of federated learning in SIoV data security protection. Ultimately, the current challenges and possible future research directions in this field are discussed.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- A New Cellular Vehicle-to-Everything Application: Daytime Visibility
Detection and Prewarning on Expressways-
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Authors:
Changlong Zhang;Fan Li;Jian Ou;Pengcheng Xie;Weitian Sheng;
Pages: 85 - 98
Abstract: Driving on an expressway is dangerous on a foggy day due to drivers’ inability to obtain visibility information in time. In this research, a new daytime visibility-detection method based on pixel contrast implemented in a prewarning system is proposed. The new visibility-detection method considers various interference factors on low-visibility foggy days and is easy to implement. In addition, a new convenient and safe camera-calibration method is proposed. The prewarning system is based on the low-latency cellular vehicle-to-everything (C-V2X) communication technology; it enables drivers to obtain low-visibility information in real time. Our experiment results prove that the output visibility-distance information obtained from the proposed method is more stable, smooth, and robust than that of other methods. Currently, the proposed C-V2X application has been implemented on a real expressway, and its actual operation shows a high detection accuracy and stable performance.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- Cooperative Control of Two-Vehicle Transportation Based on Time-Varying
Distance Between Vehicles-
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Authors:
Yan Wang;Hongliang Wang;Dawei Pi;Xiaowang Sun;Xianhui Wang;
Pages: 99 - 114
Abstract: For two-vehicle cooperative transportation control, the typical methods are to keep the relative heading angle of the two vehicles constant and to realize cooperative transportation by using the characteristics of mecanum wheels. These methods do not apply to the requirements of the highway scene for the trafficability and robustness of two-vehicle transportation. To solve these problems, a cooperative control method based on a time-varying distance model was proposed. First, a multiaxis all-wheel-steering kinematic model was constructed based on the proportional steering principle. The reference rotation angles of each wheel were output by the model predictive control (MPC), which could realize trajectory tracking, with the current vehicle state and longitudinal speed prediction sequence as inputs. Second, the leader–follower strategy was adopted to build a dynamic reference distance of the path-between-vehicles model based on trajectory tracking deviation, vehicle states, and road information. On this basis, a two-vehicle cooperative prediction equation was established, and it output the longitudinal speed of the follower through the MPC. Finally, Simulink/TruckSim cosimulation was used to verify the effectiveness of the cooperative control method, which provides a new control idea for improving the efficiency of bulk transportation. The effectiveness and robustness of the collaborative control strategy were verified by Simulink/TruckSim cosimulation and a real vehicle experiment, which provides a new control idea for improving the efficiency of large cargo transportation.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- Data Analytics to Support a Smart Fleet Management Strategy
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Authors:
Laura Pozueco;Nishu Gupta;Xabiel G. Pañeda;Víctor Corcoba;David Melendi;Roberto García;Abel Rionda;
Pages: 115 - 127
Abstract: The correct application of efficient and safe driving techniques plays an important role for professional drivers. Monitoring and analyzing driving data can promote changes in the sector in terms of the better use of vehicles, reduction in energy consumption, and improved on-road safety. However, the results in driving performance can vary considerably among different fleets that have received the same training in efficient and safe driving. The aim of this article is to perform an in-depth analysis of the driving performance of professional drivers during their working day, taking into account the influence of fleet management decisions. For this, we have selected four urban public transport companies with clear differences in terms of the employees scheduled and rostered drivers to bus lines. The driving behavior of 745 drivers has been evaluated over a period of 10 months, considering performance in terms of efficient and safe driving through the use of driving patterns. A total of 6,517,983.995 km of real-time driving data retrieved from vehicles every 1.5 s has been analyzed. The results show significant differences in the evolution and acquisition of new driving habits. In addition, significant observations from this article provide valuable information for fleet managers and allow them take advantage of data provided by the adoption of intelligent transportation systems.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- Uncertainty Region Discovery and Model Refinement for Domain Adaptation in
Road Detection-
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Authors:
Rui Zhang;Yan Tian;Dongsheng Liu;
Pages: 128 - 136
Abstract: Road detection is an important task in intelligent transportation systems. In regard to the domain adaptation, although self-training methods generate pseudo-labels for retraining the model, redundancy and noise in pseudo-labels lead to limited improvement. We propose that necessary annotations are required to effectively handle this challenge. First, we introduce the classifier discrepancy to discover and annotate uncertainty regions in the target domain. Then, we also design a recurrent teacher–student module to consider both prior knowledge and correction signals, avoiding the risk of suboptimal solution entrapment. Experiments on public data sets show that our approach is competitive with state-of-the-art approaches.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- Toward an Online Decision Support System to Improve Collision Risk
Assessment at Sea-
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Authors:
Luman Zhao;Sai Rana Thattavelil Sunilkumar;Baiheng Wu;Guoyuan Li;Houxiang Zhang;
Pages: 137 - 148
Abstract: Increasing ship speeds and unpredictable environmental perturbations raise the difficulty of situation awareness and collision avoidance—for instance, when maneuvering in narrow and busy surroundings. Providing decision support for these operations automatically and timely is, thus, of great concern in terms of ship safety. To achieve it, we developed an online decision support system (DSS) to provide navigators with intuitive and reliable solutions, such as for collision risk and the point of collision, in real time. More specifically, the collision risk for each target ship (TS) is colored coded, and the ship domain is marked by a heat map, which can remind the navigators of the surrounding situations intuitively. Afterward, a comparative case study with and without decision support was conducted to verify the effectiveness of the DSS during the busy water navigation process around a high-fidelity simulator. Experiment results show that the collision risk can be significantly reduced with the help of the proposed DSS. It has the potential to be used onboard as a complementary service in the near future.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- Model Predictive Control-Based Multivariable Controller for Traffic Flows
in Automated Freeway Systems-
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Authors:
Hongguang Pan;Xinyu Yu;Lei Gao;Yongfu Li;Wei Hua;
Pages: 149 - 161
Abstract: Uncertainties and disturbances in traffic flows will result in performance degradation and even the critical instability of automated freeway systems. To handle these issues, a multivariable model predictive controller is proposed in this article, taking the speed and density of traffic flows as the control objectives and accounting for the uncertainty caused by ramps. Specifically, a state-space macroscopic traffic flow model is established based on a macroscopic traffic flow model. Then, a multivariable model predictive controller is developed based on the state-space model, and a sequence quadratic program algorithm is designed to find the optimal inputs. Finally, extensive simulations and comparisons are conducted. The results from the simulations verify that the proposed controller can make the density and speed of traffic flows approach a desired value more quickly and without chattering. In addition, the controller can effectively avoid traffic congestion and has better stability in the presence of disturbances.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- A Survey on Market-Inspired Intersection Control Methods for Connected
Vehicles-
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Authors:
Christina Iliopoulou;Konstantinos Kepaptsoglou;Eleni I. Vlahogianni;
Pages: 162 - 176
Abstract: Recent advances in wireless communication technology allow for the cooperative coordination of vehicles and infrastructures under vehicle-to-everything (V2X) communication protocols. V2X communication protocols are shaping a new reality for intersection control, allowing for individual driver preferences to be taken into account, such as heterogeneity in terms of value of time and willingness to pay to reduce individual delay. In this context, different economic instruments have been proposed for intersection control under connected vehicles, including various types of auctions, direct payments, and credit schemes. This study offers a comprehensive review on market-inspired control approaches under connected vehicles for both signalized and unsignalized intersections, focusing on auction-based and direct transaction schemes among drivers as incentive mechanisms for aligning user and system objectives. A structured way of analyzing the existing literature is introduced, underlining relevant methodological and practical issues. The key aspects of market-based control schemes are outlined, including priority allocation, types of agents employed, incentive compatibility, solution approaches, and validation. Emerging challenges for the implementation of such approaches and future research directions are then identified and discussed.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- Ethics Dilemmas and Autonomous Vehicles: Ethics Preference Modeling and
Implementation of Personal Ethics Setting for Autonomous Vehicles in
Dilemmas-
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Authors:
Yutian Wang;Xuepeng Hu;Lingfang Yang;Zhi Huang;
Pages: 177 - 189
Abstract: Autonomous vehicles (AVs) inevitably confront dilemmas in which collisions cannot be avoided, and collision avoidance involves ethical issues due to harm distribution among traffic participants. Regarding how AVs should respond to dilemmas, ethical divergence is the main barrier that exists universally in human society. To overcome this barrier, the rational ethical propensity, i.e., the inclination of evasive steering (IES), of Chinese respondents in two typical dilemmas was collected via an online survey to construct AVs’ ethical model. Respondents were partitioned into two subgroups with relative ethical preferences to address the main ethical divergence based on their IES. The analytical IES ethical model, describing relationships among the respondents’ ethical intention and sensitive factors, was proposed. Three IES models for the two partitioned subgroups and the total respondents were fitted. Personal ethics setting (PES) was implemented with the subgroups’ models as the boundaries of operation space. PES-based trajectory planning experiments were conducted, and simulation results showed that AVs make evasive decisions in line with their ethical preference.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review
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Authors:
Hongguang Lyu;Zeyuan Shao;Tao Cheng;Yong Yin;Xiaowei Gao;
Pages: 190 - 216
Abstract: Sea-surface object detection is critical for navigation safety of autonomous ships. Electro-optical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The article also proposes the direction of future research for sea-surface object detection based on EO sensors.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- A Spatiotemporal Hybrid Model for Airspace Complexity Prediction
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Authors:
Wenbo Du;Biyue Li;Jun Chen;Yisheng Lv;Yumeng Li;
Pages: 217 - 224
Abstract: Airspace complexity is a key indicator that reflects the safety of airspace operations in air traffic management systems. Furthermore, to achieve efficient air traffic control, it is necessary to accurately predict the airspace complexity. In this article, we propose a novel spatiotemporal hybrid deep learning model for airspace complexity prediction to efficiently capture spatial correlations as well as temporal dependencies pertaining to the airspace complexity data. Specifically, we apply convolutional networks to discover the short-term temporal patterns and skip long short-term memory networks to model the long-term temporal patterns of airspace complexity data. Furthermore, it is observed that the graph attention network in our proposed model, which emphasizes capturing the spatial correlations of the airspace sectors, can significantly improve the prediction accuracy. Extensive experiments are conducted on the real data of six airspace sectors in Southwest China. The experimental results show that our spatiotemporal deep learning approach is superior to state-of-the-art methods.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- Lane Changing in a Vehicle-to-Everything Environment: Research on a
Vehicle Lane-Changing Model in the Tunnel Area by Considering the
Influence of Brightness and Noise Under a Vehicle-to-Everything
Environment-
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Authors:
Hongzhuan Zhao;Hao Wu;Ningning Lu;Xin Zhan;Enyong Xu;Quan Yuan;
Pages: 225 - 237
Abstract: As there are many factors affecting vehicle lane changes in a tunnel, which leads to the unstable state of vehicles during lane changes and an increase of collision events, a new vehicle lane-changing model is proposed by considering the influence of typical factors such as noise and brightness in a tunnel under a vehicle-to-everything (V2X) environment. First, V2X-based technology enables real-time access to a target vehicle surrounding information characteristics in a tunnel, establishing a lane-changing decision model to quantify the willingness of vehicles to change lanes. Second, considering safety as a prerequisite for a lane change, establishing a vehicle safety lane-change-distance model and a minimum safe-distance model was introduced for comparison to evaluate the safety of lane changing. On this basis, considering the noise and brightness effects of a tunnel, the relationship between brightness, noise, and response time of human-driven vehicles, hybrid driving vehicles, and autonomous vehicles is quantitatively analyzed, and then a new vehicle lane-changing model in a tunnel is established. The results of the research show that brightness in a tunnel has a more significant effect on the driver than noise. At the same time, autonomous driving, as well as hybrid driving, has better stability and comfort with less change in velocity, acceleration, and other states during lane changing in a tunnel compared to manned driving, which proves the reasonableness of the model and helps to provide a model basis for research of real vehicle lane changing under a V2X environment.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- A Map-Matching Algorithm With Extraction of Multi-Group Information for
Low-Frequency Data-
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Authors:
Jie Fang;Xiongwei Wu;Dianchao Lin;Mengyun Xu;Huahua Wu;Xuesong Wu;Ting Bi;
Pages: 238 - 250
Abstract: The growing use of probe vehicles generates a huge number of global navigation satellite systems (GNSS) data. Limited by satellite positioning technology, further improving the accuracy of map matching (MM) is challenging work, especially for low-frequency trajectories. When matching a trajectory, the ego vehicle’s spatial-temporal information of the present trip is most useful with the least amount of data. In addition, there is a large number of other data, e.g., other vehicles’ state and past prediction results, but it is hard to extract useful information for matching maps and inferring paths. Most of the MM studies have used only the ego vehicle’s data and ignored other vehicles’ data. Based on those, this article designs a new MM method to make full use of “big data.” We first sort all the data into four groups according to their spatial and temporal distance from the present matching probe, which allows us to sort for their usefulness. Then we design three different methods to extract valuable information (scores) from them: a score for speed and bearing, one for historical usage, and another for traffic state using a spectral graph Markov neural network. Finally, we use a modified top-K shortest-path method to search the candidate paths within an ellipse region and then use the fused score to infer the path (projected location). We test the proposed method against baseline algorithms using a real-world dataset in China. The results show that all scoring methods can enhance MM accuracy. Furthermore, our method outperforms the others, especially when the GNSS probing frequency is ≤ 0.01 Hz.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- Cyber Mobility Mirror for Enabling Cooperative Driving Automation in Mixed
Traffic: A Co-Simulation Platform-
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Authors:
Zhengwei Bai;Guoyuan Wu;Xuewei Qi;Yongkang Liu;Kentaro Oguchi;Matthew J. Barth;
Pages: 251 - 265
Abstract: Endowed with automation and connectivity, connected and automated vehicles (CAVs) will be a revolutionary promoter for cooperative driving automation (CDA). Nevertheless, CAVs need high-fidelity perception information on their surroundings, which is available but costly to collect from various onboard sensors, as well as vehicle-to-everything communications. Therefore, authentic perception (AP) information based on high-fidelity sensors via a cost-effective platform is crucial for enabling CDA-related research, e.g., cooperative decision making or control. Most state-of-the-art traffic simulation studies for CAVs rely on the situation-awareness information by directly calling on intrinsic attributes of the objects, which impedes the reliability and fidelity of the assessment of CDA algorithms. In this study, a cyber mobility mirror (CMM) co-simulation platform is designed for enabling CDA by providing AP information. The CMM co-simulation platform can emulate the real world with a high-fidelity sensor perception system and the cyberworld with a real-time rebuilding system acting as a “mirror” of the real-world environment. Concretely, the real-world simulator is mainly in charge of simulating the traffic environment, the sensors, and the AP process. The mirror-world simulator is responsible for representing objects and providing their information as intrinsic attributes of the simulator to support the development and evaluation of CDA algorithms. To illustrate the functionality of the proposed co-simulation platform, a roadside lidar-based vehicle perception system for enabling CDA is prototyped as a study case. Specific traffic environments and CDA tasks are designed for experiments whose results are demonstrated and analyzed to show the performance of the platform.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- Celebrating Petros Ioannou’s Great Achievements [Its People]
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Authors:
Ljubo Vlacic;
Pages: 266 - 266
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- An Introduction to the Connected and Autonomous Transportation Systems
Laboratory [Its Research Lab]-
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Authors:
Yisheng Lv;
Pages: 267 - 271
Abstract: With the rapid developments of emerging technologies (e.g., intelligent vehicles, shared mobility, sensing, and communication) in transportation engineering, unprecedented research opportunities have risen at an increasingly rapid pace, ranging from element mechanism and behavior (e.g., infrastructure sensing, intelligent vehicle control, and individual traveler behavior) to systems modeling and management (e.g., the interdependence of infrastructure systems, resilient systems design, and associated equity and public health issues). Grasping these opportunities is crucial to the leadership of a transportation program at a top university. As shown in Figure 1, our research focuses on the forefront multiscale problems of smart mobility by integrating general fundamental theories and methodologies with cutting-edge interdisciplinary technology developments. Our research approaches integrate computer modeling and simulation with real-world field tests and demonstrations, bridging upstream knowledge generation and downstream technology transfer.
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- The 2017-2020 ITSM Outstanding Paper Awards and The 2017-2020 ITSM
Outstanding Reviewer Awards [Society News]-
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Authors:
Ljubo Vlacic;
Pages: 272 - 274
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- IEEE Feedback
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Pages: 274 - 274
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- IEEE Access
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Pages: 275 - 275
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
- [Calendar]
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Authors:
Martin Lauer;
Pages: 276 - 276
PubDate:
March-April 2023
Issue No: Vol. 15, No. 2 (2023)
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