Intelligent chassis systems are the basic platform for autonomous driving systems, cockpit systems, and power systems, and play a crucial role in the progress and innovation of automotive technology. With the intelligent chassis system, automobiles can realize autonomous navigation, automatic parking, remote vehicle monitoring, and other functions, providing users with a more convenient and safer travel experience.
With the integration of multiple sensors into the chassis system, the intelligent chassis system senses the external traffic environment (e.g., information such as surrounding vehicles' motion, the tire-road friction coefficient, etc.) and vehicle dynamic states (e.g., center-of-mass side deviation angle, etc.) with greater accuracy. Intelligent chassis systems can assess in real time the driving risks caused by vehicle-vehicle collisions and dynamic instability based on multi-source information. Furthermore, after accurately measuring the risk of driving, the vehicle needs to be precisely controlled to avoid accidents, and the design of the controller usually needs to satisfy the requirements of real-time and robustness.
Although the research related to intelligent chassis systems is rapidly growing, there are still problems such as low perception accuracy, difficulty in recognizing safety risks, and ineffective vehicle control. The combination of artificial intelligence and traditional model-based methods is the key to solving these problems. In this background, the purpose of this special session is to establish a platform for researchers and practitioners to share their latest findings, thereby contributing to the advancement of intelligent chassis systems, covering topics including but not limited to:
- Multi-sensor fusion for macro-detection and micro-identification of roads
- Multi-modal object detection in intelligent chassis systems
- Fault diagnosis of intelligent chassis systems
- State estimation and parameters identification for intelligent chassis systems
- Cross-domain safety risk assessment of intelligent chassis systems
- Personalized Human-Machine Interaction for Intelligent Chassis Systems
- Multi-domain distributed cooperative control of intelligent chassis systems
- Data-driven learning control of intelligent chassis systems
Submission Deadline: 31 December 2024
Guest Editors
Dr. Yan Wang, The Hong Kong Polytechnic University, China
Prof. Guodong Yin, Southeast University, China
Prof. Yang Shi, University of Victoria, Canada
Prof. Haiping Du, University of Wollongong, Australia
Prof. Lu Xiong, Tongji University, China
Prof. Lin Hu, Changsha University of Science & Technology, China
Dr. Chao Huang, The Hong Kong Polytechnic University, China
Dr. Henglai Wei, Beihang University, China
All inquiries and correspondence must be addressed to the Editorial Office of CJME and CC Dr. Yan Wang.