An active safety control method of collision avoidance for intelligent connected vehicle based on driving risk perception

Chuan Sun, Sifa Zheng, Yulin Ma (), Duanfeng Chu, Junru Yang, Yuncheng Zhou, Yicheng Li and Tingxuan Xu
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Chuan Sun: Tsinghua University
Sifa Zheng: Tsinghua University
Yulin Ma: Tsinghua University
Duanfeng Chu: Wuhan University of Technology
Junru Yang: Wuhan University of Technology
Yuncheng Zhou: China Design Group Co., Ltd.
Yicheng Li: Jiangsu University
Tingxuan Xu: The Affiliated High School of SCNU

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 5, No 2, 1249-1269

Abstract: Abstract As the complex driving scenarios bring about an opportunity for application of deep learning in safe driving, artificial intelligence based on deep learning has become a heatedly discussed topic in the field of advanced driving assistance system. This paper focuses on analysing vehicle active safety control of collision avoidance for intelligent connected vehicles (ICVs) in a real driving risk scenario, and driving risk perception is based on the ICV technology. In this way, trajectories of surrounding vehicles can be predicted and tracked in a real-time manner. In this paper, vehicle dynamics based state-space equations conforming to model predictive controllers are set up to primarily explore and identify a safety domain of active collision avoidance. Furthermore, the model predictive controller is also designed and calibrated, thereby implementing the active collision avoidance strategy for vehicles based on the model predictive control method. At last, functional testing is conducted for the proposed active collision avoidance control strategy in a designed complex traffic scenario. The research findings here can effectively improve automatic driving, intelligent transportation efficiency and road traffic safety.

Keywords: Vehicle active safety; Collision avoidance; Model predictive control; Driving risk; Intelligent connected vehicle (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10845-020-01605-x

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