Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field

Xie, Songtao; Hu, Junyan; Bhowmick, Parijat; Ding, Zhengtao; Arvin, Farshad

Abstract

Autonomous driving of multi-lane vehicle platoons have attracted significant attention in recent years due to their potential to enhance the traffic-carrying capacity of the roads and produce better safety for drivers and passengers. This paper proposes a distributed motion planning algorithm to ensure safe overtaking of autonomous vehicles in a dynamic environment using the Artificial Potential Field method. Unlike the conventional overtaking techniques, autonomous driving strategies can be used to implement safe overtaking via formation control of unmanned vehicles in a complex vehicle platoon in the presence of human-operated vehicles. Firstly, we formulate the overtaking problem of a group of autonomous vehicles into a multi-target tracking problem, where the targets are dynamic. To model a multi-vehicle system consisting of both autonomous and human-operated vehicles, we introduce the notion of velocity difference potential field and acceleration difference potential field. We then analyze the stability of the multi-lane vehicle platoon and propose an optimization-based algorithm for solving the overtaking problem by placing a dynamic target in the traditional artificial potential field. A simulation case study has been performed to verify the feasibility and effectiveness of the proposed distributed motion control strategy for safe overtaking in a multi-lane vehicle platoon.

Citation

Xie, S., Hu, J., Bhowmick, P., Ding, Z., & Arvin, F. (2022). Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field. IEEE Transactions on Intelligent Transportation Systems, 23(11), 21531- 21547. https://doi.org/10.1109/tits.2022.3189741

• Journal Article Type: Article
• Acceptance Date: Jul 6, 2022
• Online Publication Date: Jul 15, 2022
• Publication Date: 2022-11
• Deposit Date: Jul 25, 2022
• Publicly Available Date: Jul 26, 2022
• Journal: IEEE Transactions on Intelligent Transportation Systems
• Print ISSN: 1524-9050
• Electronic ISSN: 1558-0016
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 11
• Pages: 21531- 21547
• DOI: https://doi.org/10.1109/tits.2022.3189741
• Public URL: https://durham-repository.worktribe.com/output/1196977