Paper Info
Title
An Active Safety Control Method Of Collision Avoidance For Intelligent Connected Vehicle Based On Driving Risk Perception
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.
Year
DOI
Venue
2021
10.1007/s10845-020-01605-x
JOURNAL OF INTELLIGENT MANUFACTURING
Keywords
DocType
Volume
Vehicle active safety, Collision avoidance, Model predictive control, Driving risk, Intelligent connected vehicle
Journal
32
Issue
ISSN
Citations 
5
0956-5515
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Chuan Sun143.54
Zheng Sifa242.41
Yulin Ma3136.95
Duanfeng Chu442.46
Junru Yang500.34
Yuncheng Zhou600.34
Yicheng Li700.34
Tingxuan Xu800.34