Xie, Songtao;
Hu, Junyan;
Bhowmick, Parijat;
Ding, Zhengtao;
Arvin, Farshad;
(2022)
Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field.
IEEE Transactions on Intelligent Transportation Systems
, 23
(11)
pp. 21531-21547.
10.1109/TITS.2022.3189741.
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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.
Type: | Article |
---|---|
Title: | Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/TITS.2022.3189741 |
Publisher version: | https://doi.org/10.1109/TITS.2022.3189741 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Motion planning, intelligent vehicles, artificial potential field, autonomous overtaking, collision avoidance, distributed systems |
UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
URI: | https://discovery.ucl.ac.uk/id/eprint/10151756 |
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