A cooperation-aware lane change method for automated vehicles

Sheng, Z., Liu, L., Xue, S., Zhao, D. , Jiang, M. and Li, D. (2023) A cooperation-aware lane change method for automated vehicles. IEEE Transactions on Intelligent Transportation Systems, 24(3), pp. 3236-3251. (doi: 10.1109/TITS.2022.3225875)

Abstract

Lane change for automated vehicles (AVs) is an important but challenging task in complex dynamic traffic environments. Due to difficulties in guaranteeing safety as well as a high efficiency, AVs are inclined to choose relatively conservative strategies for lane change. To avoid the conservatism, this paper presents a cooperation-aware lane change method utilizing interactions between vehicles. We first propose an interactive trajectory prediction method to explore possible cooperations between an AV and the others. Further, an evaluation on safety, efficiency and comfort is designed to make a decision on lane change. Thereafter, we propose a motion planning algorithm based on model predictive control (MPC), which incorporates AV’s decision and surrounding vehicles’ interactive behaviors into constraints so as to avoid collisions during lane change. Quantitative testing results show that compared with the methods without an interactive prediction, our method enhances driving efficiencies of the AV and other vehicles by 14.8% and 2.6%, respectively, which indicates that a proper utilization of vehicle interactions can effectively reduce the conservatism of the AV and promote the cooperation between the AV and others.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhao, Dr Dezong
• Authors: Sheng, Z., Liu, L., Xue, S., Zhao, D., Jiang, M., and Li, D.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Intelligent Transportation Systems
• Publisher: IEEE
• ISSN: 1524-9050
• ISSN (Online): 1558-0016
• Published Online: 06 December 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Transactions on Intelligent Transportation Systems 24(3): 3236-3251
• Publisher Policy: Reproduced with the permission of the Publisher

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
314774		Toward Energy Efficient Autonomous Vehiciles via Cloud-Aided learning	Dezong Zhao	Engineering and Physical Sciences Research Council (EPSRC)	EP/S001956/2	ENG - Autonomous Systems & Connectivity