Evaluation method with digital expert on the criticality of car-following scenarios for autonomous vehicles testing

Abstract

Evaluation of autonomous vehicles is one of the major challenges before they can be released. Due to the advantages in efficiency, cost, and safety, scenario-based simulation methods have recently received great attention. Even so, as the complexity and uncertainty exist in the real driving environment, the scenarios that autonomous vehicles may encounter are infinite. Therefore, it is necessary to classify simulation scenarios according to their criticality. It contributes to accelerating the evaluation processes. This paper presents a novel criticality evaluation method, based on a proposed Digital Expert, for car-following autonomous driving. The Digital Expert acts as the evaluator to evaluate the criticality of scenarios depending on their driving performance. Driving performance refers to the achieved degree of driving intentions. Firstly, a Digital Expert is established as the evaluator for the criticality of the scenario using the inverse reinforcement learning method. Then, based on the fact that the intention of Digital Expert is to maximize its internal reward function, the reward function is used to evaluate driving performance. Finally, calculating the criticality of the car-following scenario according to the mapping relationship between driving performance and criticality. Using the driving data in the NGSIM data set, this paper generates two groups of simulated car-following scenarios and evaluates the criticalities of the two scenarios. The experimental results show that the proposed criticality evaluation method can reasonably evaluate the criticality of car-following scenarios.