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

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

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Songtao Xie

Junyan Hu

Parijat Bhowmick

Zhengtao Ding


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.

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
Public URL


Accepted Journal Article (9.6 Mb)

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