Traffic Risk Environment Impact Analysis and Complexity Assessment of Autonomous Vehicles Based on the Potential Field Method

Ying Cheng, Zhen Liu (), Li Gao, Yanan Zhao and Tingting Gao
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Ying Cheng: School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China
Zhen Liu: Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China
Li Gao: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Yanan Zhao: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Tingting Gao: School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China

IJERPH, 2022, vol. 19, issue 16, 1-14

Abstract: Although autonomous vehicles have introduced a promising potential for improving traffic safety and efficiency, ensuring the safety of autonomous vehicles in complex road traffic environments is still a huge challenge to be tackled. To quickly quantify the potential risk factors of autonomous vehicles in traffic environments, this paper focuses mainly on the influence of the depth and breadth of the environment elements on the autonomous driving system, uses the potential field theory to establish a model of the impact of the environmental elements on the autonomous driving system, and combines AHP to quantify equivalent virtual electric quantity of each environment element, so as to realize the quantitative evaluation of the traffic environment complexity. The proposed method comprehensively considers the physical attributes and state parameters of the environmental elements, which compensates for the fact that the shortage of the factors considered in the traffic environment complexity assessment is not comprehensive. Finally, a series of experiments was carried out to verify the reliability of our proposed method. The results show that the complexity of the static elements is determined only by the physical attributes and shape of the obstacle; the complexity of the dynamic elements is determined by the movement of the obstacle and the movement of the autonomous vehicle, and the comprehensive complexity mainly depends on the complexity of their dynamic elements. Compared with other methods, the complexity evaluation values are generally consistent, the absolute percentage error of the majority of samples was within ±5%, and the degree of deviation was −1.143%, which provides theoretical support for autonomous vehicles on safety and the risk assessment in future.

Keywords: autonomous vehicles; traffic safety; environment impact analysis; risk evaluation; potential field theory (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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