A multi-vehicle longitudinal trajectory collision avoidance strategy using AEBS with vehicle-infrastructure communication

Zhang, R., Li, K., Wu, Y., Zhao, D. , Lv, Z., Li, F., Chen, X., Qiu, Z. and Yu, F. (2022) A multi-vehicle longitudinal trajectory collision avoidance strategy using AEBS with vehicle-infrastructure communication. IEEE Transactions on Vehicular Technology, 71(2), pp. 1253-1266. (doi: 10.1109/TVT.2021.3132558)

Abstract

Shortening inter-vehicle distance can increase traffic throughput on roads for increasing volume of vehicles. In the process, traffic accidents occur more frequently, especially for multi-car accidents. Furthermore, it is difficult for drivers to drive safely under such complex driving conditions. This paper investigates multi-vehicle longitudinal collision avoidance issue under such traffic conditions based on the Advanced Emergency Braking System (AEBS). AEBS is used to avoid collisions or mitigate the impact during critical situations by applying brake automatically. Hierarchical multi-vehicle longitudinal collision avoidance controller is proposed to guarantee safety of multi-cars using Vehicle-to-Infrastructure (V2I) communication. High-level controller is designed to ensure safety of multi-cars and optimize total energy by calculating the target braking force. Vehicle network is used to get the key vehicle-road interaction data and constrained hybrid genetic algorithm (CHGA) is adopted to decouple the vehicle-road interactive system. Lower level non-singular Fractional Terminal Sliding Mode(NFTSM) Controller is built to achieve control goals of high-level controller. Simulations are carried out under typical driving conditions. Results verify that the proposed system in this paper can avoid or mitigate the collision risk compared to the vehicle without this system.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhao, Dr Dezong
• Authors: Zhang, R., Li, K., Wu, Y., Zhao, D., Lv, Z., Li, F., Chen, X., Qiu, Z., and Yu, F.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Vehicular Technology
• Publisher: IEEE
• ISSN: 0018-9545
• ISSN (Online): 1939-9359
• Published Online: 03 December 2021
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in IEEE Transactions on Vehicular Technology 71(2): 1253-1266
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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