Advances in Intelligent Connected Vehicles and Intelligent Transportation Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 4014

Special Issue Editors


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Guest Editor
1. School of Transportation, Southeast University, Nanjing 211189, China
2. Engineering College, Tibet University, Tibet 850000, China
Interests: intelligent scheduling for public transit (analysis, modeling and simulation); traffic information system (data platform system design, highway traffic operation)
Special Issues, Collections and Topics in MDPI journals
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA
Interests: connected and automated vehicle-highway systems; traffic operations and control; mobile traffic sensor modeling

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Guest Editor
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA
Interests: connected automated vehicle; intelligent transport system; car following and lane changing model; intelligent infrastructure

Special Issue Information

Dear Colleagues,

The technologies of Intelligent Connected Vehicles and Intelligent Transportation Systems, including the Internet of Vehicles and the Cooperative Vehicle Infrastructure System, have promoted the deep integration of smart cars, intelligent transportation, and mobile technology, which will have a profound impact on travel and driving patterns and provide potential economic and social benefits. The recent progress of Intelligent and Connected Transportation Systems benefits from advanced communication protocols, artificial intelligence (AI), and big data. However, there are still several open challenges in the domain of control, management, and analysis for the Intelligent and Connected Transportation Systems.

This Special Issue seeks contributions that present innovative control and analysis methods based on the new-generation intelligent transportation system, as well as contributions focusing on recent advances in urban traffic planning, data mining, and vehicle engineering that solve the most relevant challenges faced by current and future intelligent transportation systems.

Dr. Jian Zhang
Dr. Yang Cheng
Dr. Tianyi Chen
Guest Editors

Manuscript Submission Information

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Keywords

  • internet of vehicles
  • ITS
  • intelligent and connected vehicles
  • cooperative vehicle infrastructure system
  • traffic control and management

Published Papers (2 papers)

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Research

20 pages, 5263 KiB  
Article
Energy-Efficient Driving for Adaptive Traffic Signal Control Environment via Explainable Reinforcement Learning
by Xia Jiang, Jian Zhang and Bo Wang
Appl. Sci. 2022, 12(11), 5380; https://doi.org/10.3390/app12115380 - 26 May 2022
Cited by 7 | Viewed by 1507
Abstract
Energy-efficient driving systems can effectively reduce energy consumption during vehicle operation. Most of the existing studies focus on the driving strategies in a fixed signal timing environment, whereas the standardized Signal Phase and Timing (SPaT) data can help the vehicle make the optimal [...] Read more.
Energy-efficient driving systems can effectively reduce energy consumption during vehicle operation. Most of the existing studies focus on the driving strategies in a fixed signal timing environment, whereas the standardized Signal Phase and Timing (SPaT) data can help the vehicle make the optimal decisions. However, with the development of artificial intelligence and communication techniques, the conventional fixed timing methods are gradually replaced by adaptive traffic signal control (ATSC) approaches. The previous studies utilized SPaT information that cannot be applied directly in the environment with ATSC. Thus, a framework is proposed to implement energy-efficient driving in the ATSC environment, while the ATSC is realized by the value-based reinforcement learning algorithm. After giving the optimal control model, the framework draws upon the Markov Decision Process (MDP) to make an approximation to the optimal control problem. The state sharing mechanism allows the vehicle to obtain the state information of the traffic signal agents. The reward function in MDP considers energy consumption, traffic mobility, and driving comfort. With the support of traffic simulation software SUMO, the vehicle agent is trained by Proximal Policy Optimization (PPO) algorithm, which enables the vehicle to select actions from continuous action space. The simulation results show that the energy consumption of the controlled vehicle can be reduced by 31.73%~45.90% with a different extent of mobility sacrifice compared with the manual driving model. Besides, we developed a module based on SHapley Additive exPlanations (SHAP) to explain the decision process in each timestep of the vehicle. That can make the strategy more reliable and credible. Full article
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22 pages, 7484 KiB  
Article
Improved ADRC-Based Autonomous Vehicle Path-Tracking Control Study Considering Lateral Stability
by Nan Kang, Yi Han, Tian Guan and Siyu Wang
Appl. Sci. 2022, 12(9), 4660; https://doi.org/10.3390/app12094660 - 06 May 2022
Cited by 2 | Viewed by 1378
Abstract
The antidisturbance control problem of autonomous vehicle path tracking considering lateral stability is studied in this paper. This paper proposes an improved active disturbance rejection control (IADRC) control method including an improved extended state observer (IESO) and an error compensator based on LQR, [...] Read more.
The antidisturbance control problem of autonomous vehicle path tracking considering lateral stability is studied in this paper. This paper proposes an improved active disturbance rejection control (IADRC) control method including an improved extended state observer (IESO) and an error compensator based on LQR, where a new continuous nonlinear function is proposed in the IESO instead of the classical piecewise function. Based on the IADRC, an autonomous vehicle path-tracking controller considering lateral stability is designed. Using the output wheel steering angle and external yaw moment, the IESO estimates the disturbance value and compensates for the disturbance in the feedback to meet the goal of antidisturbance control. Based on the concept of control allocation (CA), the control distributor is designed to distribute the external yaw moment to the four wheels in a reasonable and optimal way to achieve differential braking. Finally, the control scheme is evaluated in the form of CarSim/Simulink cosimulation; the results show that the proposed autonomous vehicle path-tracking control scheme has better path-tracking effect and higher antidisturbance robustness. Full article
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