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

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.

Risk analysis of autonomous vehicle test scenarios using a novel analytic hierarchy process method

“…The results showed that the road segment type is the main factor affecting static complexity, and finally, the test scenarios with high total complexity were selected to evaluate the safety performance of autonomous vehicles. In the study of risk factors analysis for autonomous vehicles [23], Cheng et al calculated the complexity of static scenes at intersections using the AHP method. The interaction with traffic signals was considered to be the most important factor.…”

Leveraging LiDAR-Based Simulations to Quantify the Complexity of the Static Environment for Autonomous Vehicles in Rural Settings

“…Wang et al [ 45 ], on the other hand, proposed three different fields (moving objects, road environment, and driver characteristics). Similarly, Cheng et al [ 46 ] based their environmental complexity evaluation model on the potential field theory. The environment elements are represented by a positive point charge or uniformly charged wires that create a potential field in their vicinity.…”

Unlocking Mutual Gains—An Experimental Study on Collaborative Autonomous Driving in Urban Environment

“…The comprises stationary elements of the environment, such as road segments, tunnels, signage, road markings, vegetation, etc., whereas the encompasses dynamic elements such as vulnerable road users, vehicles, and non-motorized vehicles. To calculate the for , we adopt the equation from the study conducted by Cheng et al [ 27 ]. This equation combines the complexity of both static and dynamic elements, and the result gives the overall complexity of the traffic environment.…”

“…To calculate the for , we adopt the equation from the study conducted by Cheng et al [ 27 ]. This equation combines the complexity of both static and dynamic elements, and the result gives the overall complexity of the traffic environment.…”