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Development towards Vehicle Safety in Future Smart Traffic Systems
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Development towards Vehicle Safety in Future Smart Traffic Systems

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 8377

Special Issue Editors

Prof. Dr. Zhenfei Zhan

E-Mail Website
Guest Editor
School of Mechanotronic and Vehicle Engineering, Chongqing Jiaotong University, Chongqing, China
Interests: integrated automotive safety; vehicle virtual design and optimization; vehicle compliance strategy; intelligent transportation system
Dr. Yongjun Pan

E-Mail Website
Co-Guest Editor
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400030, China
Interests: multibody system dynamics; vehicle system dynamics; batteries; data-driven modeling and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Road traffic accidents are a major public challenge around the world. Due to the existence of many uncontrollable factors (poor and various driving environments, etc.), road traffic accidents will continue to exist in the future for a long time. Deep mining and evaluation of accident data, quantifying the risk of injury to traffic participants in dangerous scenarios, incorporating passive and active automotive safety technologies. Integrated collaborative protection technology to achieve the best protection for traffic participants inside and outside the automobile. These technologies include artificial intelligence (AI) and machine learning (ML); simulation and optimization for automotive safety applications, including reliability and system safety applications, such as reliability modeling, uncertainty quantification, failure prognostics, and system design optimization; predictive, prescriptive, or preventive maintenance; and big data analytics. Integrated automotive safety system development and verification technology systems to achieve multi-dimensional measurement of automotive safety.

Prof. Dr. Zhenfei Zhan
Dr. Yongjun Pan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. World Electric Vehicle Journal is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI/ML applications in vehicle safety
  • driver intent analytic/inference
  • driving risk assessment/prevention
  • advances in human modeling
  • smart mobility for road safety

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Published Papers (4 papers)

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Research

14 pages, 3752 KiB  
Article
A Comparative Study of Traffic Signal Control Based on Reinforcement Learning Algorithms
by Chen Ouyang, Zhenfei Zhan and Fengyao Lv
World Electr. Veh. J. 2024, 15(6), 246; https://doi.org/10.3390/wevj15060246 - 4 Jun 2024
Cited by 1 | Viewed by 1709
Abstract
In recent years, the increasing production and sales of automobiles have led to a notable rise in congestion on urban road traffic systems, particularly at ramps and intersections with traffic signals. Intelligent traffic signal control represents an effective means of addressing traffic congestion. [...] Read more.
In recent years, the increasing production and sales of automobiles have led to a notable rise in congestion on urban road traffic systems, particularly at ramps and intersections with traffic signals. Intelligent traffic signal control represents an effective means of addressing traffic congestion. Reinforcement learning methods have demonstrated considerable potential for addressing complex traffic signal control problems with multidimensional states and actions. In this research, the team propose Q-learning and Deep Q-Network (DQN) based signal control frameworks that use variable phase sequences and cycle times to adjust the order and the duration of signal phases to obtain a stable traffic signal control strategy. Experiments are simulated using the traffic simulator Simulation of Urban Mobility (SUMO) to test the average speed and the lane occupancy rate of vehicles entering the ramp to evaluate its safety performance and test the vehicle’s traveling time to assess its stability. The simulation results show that both reinforcement learning algorithms are able to control cars in dynamic traffic environments with higher average speed and lower lane occupancy rate than the no-control method and that the DQN control model improves the average speed by about 10% and reduces the lane occupancy rate by about 30% compared to the Q-learning control model, providing a higher safety performance. Full article
(This article belongs to the Special Issue Development towards Vehicle Safety in Future Smart Traffic Systems)
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20 pages, 2439 KiB  
Article
Distributed Intelligent Vehicle Path Tracking and Stability Cooperative Control
by Zhaoxue Deng, Yangrui Zhang and Shuen Zhao
World Electr. Veh. J. 2024, 15(3), 89; https://doi.org/10.3390/wevj15030089 - 28 Feb 2024
Cited by 2 | Viewed by 1638
Abstract
To enhance the path tracking capability and driving stability of intelligent vehicles, a controller is designed that synergizes active front wheel steering (AFS) and direct yaw moment (DYC), specifically tailored for distributed-drive electric vehicles. To address the challenge of determining the weight matrix [...] Read more.
To enhance the path tracking capability and driving stability of intelligent vehicles, a controller is designed that synergizes active front wheel steering (AFS) and direct yaw moment (DYC), specifically tailored for distributed-drive electric vehicles. To address the challenge of determining the weight matrix in the linear quadratic regulator (LQR) algorithm during the path tracking design for intelligent vehicles on conventional roads, a genetic algorithm (GA)-optimized LQR path tracking controller is introduced. The 2-degree-of-freedom vehicle dynamics error model and the desired path information are established. The genetic algorithm optimization strategy, utilizing the vehicle’s lateral error, heading error, and output front wheel steering angle as the objective functions, is employed to optimally determine the weight matrices Q and R. Subsequently, the optimal front wheel steering angle control (AFS) output of the vehicle is calculated. Under extreme operating conditions, to enhance vehicle dynamics stability, while ensuring effective path tracking, the active yaw moment is crafted using the sliding mode control with a hyperbolic tangent convergence law function. The control weights of the sliding mode surface related to the center-of-mass lateral declination are adjusted based on the theory of the center-of-mass lateral declination phase diagram, and the vehicle’s target yaw moment is calculated. Validation is conducted through Matlab/Simulink and Carsim co-simulation. The results demonstrate that the genetic algorithm-optimized LQR path tracking controller enhances vehicle tracking accuracy and exhibits improved robustness under conventional road conditions. In extreme working conditions, the designed path tracking and stability cooperative controller (AFS+DYC) is implemented to enhance the vehicle’s path tracking effect, while ensuring its driving stability. Full article
(This article belongs to the Special Issue Development towards Vehicle Safety in Future Smart Traffic Systems)
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24 pages, 9650 KiB  
Article
Vehicle Trajectory Prediction Based on Local Dynamic Graph Spatiotemporal–Long Short-Term Memory Model
by Juan Chen, Qinxuan Feng and Daiqian Fan
World Electr. Veh. J. 2024, 15(1), 28; https://doi.org/10.3390/wevj15010028 - 15 Jan 2024
Viewed by 1633
Abstract
Traffic congestion and frequent traffic accidents have become the main problems affecting urban traffic. The effective location prediction of vehicle trajectory can help alleviate traffic congestion, reduce the occurrence of traffic accidents, and optimize the urban traffic system. Vehicle trajectory is closely related [...] Read more.
Traffic congestion and frequent traffic accidents have become the main problems affecting urban traffic. The effective location prediction of vehicle trajectory can help alleviate traffic congestion, reduce the occurrence of traffic accidents, and optimize the urban traffic system. Vehicle trajectory is closely related to the surrounding Point of Interest (POI). POI can be considered as the spatial feature and can be fused with trajectory points to improve prediction accuracy. A Local Dynamic Graph Spatiotemporal–Long Short-Term Memory (LDGST-LSTM) was proposed in this paper to extract and fuse the POI knowledge and realize next location prediction. POI semantic information was learned by constructing the traffic knowledge graph, and spatial and temporal features were extracted by combining the Graph Attention Network (GAT) and temporal attention mechanism. The effectiveness of LDGST-LSTM was verified on two datasets, including Chengdu taxi trajectory data in August 2014 and October 2018. The accuracy and robustness of the proposed model were significantly improved compared with the benchmark models. The effects of major components in the proposed model were also evaluated through an ablation experiment. Moreover, the weights of POI that influence location prediction were visualized to improve the interpretability of the proposed model. Full article
(This article belongs to the Special Issue Development towards Vehicle Safety in Future Smart Traffic Systems)
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15 pages, 4627 KiB  
Article
Realistic Approach to Safety Verification of Electric Tricycle in Thailand
by Songwut Mongkonlerdmanee, Sthaphorn Wannapor, Pichest Boonyalai, Saharat Chanthanumataporn, Manus Dangchat and Saiprasit Koetniyom
World Electr. Veh. J. 2023, 14(7), 164; https://doi.org/10.3390/wevj14070164 - 23 Jun 2023
Viewed by 2204
Abstract
A Tuk-tuk, also known as a motorized tricycle, is a three-wheeled vehicle with wheels symmetrically arranged in the longitudinal driving direction. Compared to four-wheeled vehicles, tuk-tuks have less stability. Classical Tuk-tuks typically have a metal occupant compartment without doors, resulting in direct contact [...] Read more.
A Tuk-tuk, also known as a motorized tricycle, is a three-wheeled vehicle with wheels symmetrically arranged in the longitudinal driving direction. Compared to four-wheeled vehicles, tuk-tuks have less stability. Classical Tuk-tuks typically have a metal occupant compartment without doors, resulting in direct contact between occupants and the metal structure. In tropical countries with heavy rainfall, flooded roads are common. This study proposes technical requirements specific to electric Tuk-tuks, which are gaining popularity in Thailand. Experimental tests focused on braking performance, rollover stability, and electric safety prevention. The tests addressed four aspects: brake performance, parking capability, rollover stability, and electric isolation resistance during floods. These tests help manufacturers meet Thai safety standards. Results emphasize the importance of adhering to Tuk-tuk standards for vehicle performance and electric safety. Full article
(This article belongs to the Special Issue Development towards Vehicle Safety in Future Smart Traffic Systems)
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