Research on Collision Avoidance Systems for Intelligent Vehicles Considering Driver Collision Avoidance Behaviour

Author:

Liu Guosi1,Bei Shaoyi2,Li Bo2ORCID,Liu Tao3,Daoud Walid4ORCID,Tang Haoran2,Guo Jinfei2,Zhu Zhaoxin2

Affiliation:

1. College of Mechanical Engineering, Jiangsu University of Technology, Changzhou 213001, China

2. College of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, China

3. Equipment Research Institute, Beijing 102202, China

4. Department of Mechanical Engineering, City University of Hong Kong, Hong Kong 999077, China

Abstract

In this paper, a new collision avoidance switching system is proposed to address the lack of adaptability of intelligent vehicles under different collision avoidance operating conditions. To ensure the rationality of the collision avoidance switching strategy for intelligent vehicles, the NGSIM road dataset is introduced to analyse the driver’s collision avoidance behaviour, and a two-layer fuzzy controller considering the overlap rate is established to design the collision avoidance switching strategy. In order to achieve real-time collision avoidance system activation, a lane change collision avoidance model based on MPC control is also developed. Finally, a simulation environment was created using Matlab/CarSim for simulation verification. The simulation results show that the collision avoidance switching system is more responsive and has a shorter start-up distance and is more adaptable to different driving conditions.

Funder

National Natural Science Foundation of China

Natural Science Foundation of the Jiangsu Higher Education of China

Changzhou International Science and Technology Cooperation Fund

Publisher

MDPI AG

Subject

Automotive Engineering

Reference29 articles.

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2. Local Path Planning and Tracking Control of Vehicle Collision Avoidance System;Xu;J. Nanjing Univ. Nat. Sci.,2018

3. Adaptive automatic emergency braking control strategy based on an ESHB system;Yong;J. Automot. Saf. Energy,2022

4. Zhang, Z., Luo, D., Rasim, Y., Li, Y., Meng, G., Xu, J., and Wang, C. (2016). A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation. Sensors, 16.

5. Research on Hierarchical Control Strategy for Autonomous Emergency Braking System Based on Prescan;You;Agric. Equip. Veh. Eng.,2022

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