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›› 2019, Vol. 41 ›› Issue (3): 307-312.doi: 10.19562/j.chinasae.qcgc.2019.03.010

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A Study on Vehicle Collision Risk Estimation Algorithm Based on External Dynamic Environment

Zhou Bing1, Zhao Hua1, Wu Xiaojian2, Chen Xiaolong1 & Zeng Fanyi1   

  1. 1.Hunan University, State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Changsha 410082;
    2.Nanchang University, Nanchang 330031
  • Received:2018-05-28 Online:2019-03-25 Published:2019-03-25

Abstract: In view of that the most existing vehicle collision risk estimation algorithms under emergency only consider the uncertainty caused by measurement noise interference, a vehicle collision risk estimation algorithm is proposed concurrently considering both the uncertainty in dynamic road environment and that caused by measurement noise interference. Firstly, the fuzzy inference model for “road condition-speed-maximum acceleration” is constructed, in which the maximum braking acceleration of vehicle is quickly obtained by using the intelligent algorithm of fuzzy inference based on road condition and host vehicle speed. A kinematics-based prediction model is also established, and with consideration of the dynamic changes of road adhesion condition and the uncertainty caused by measurement noise interference, Monte Carlo method is used to calculate the crash probability of host vehicle under current driving environment. The trajectories of vehicle emergency braking and steering are predicted according to vehicle dynamics and road-related parameters and hence the crash probabilities with collision avoidance by braking or lane change are obtained. Taking the intersection crossing and rear end collision conditions as examples, the crash probabilities with braking and steering collision avoidances under different road conditions are contrastively analyzed so as to choose a reasonable mode of vehicle collision avoidance. The results show that the proposed risk estimation algorithm is relatively consistent with the emergency collision avoidance maneuver in real dynamic traffic environment with good effectiveness and feasibility

Key words: emergency collision avoidance, dynamic road environment, uncertainty, crash probability prediction