Counterfactual regret minimization for the safety verification of autonomous driving

Rare safety-critical events remain a major challenge in autonomous vehicle testing. This paper proposes to use game theory to build a novel testing environment for autonomous vehicles. In this environment, a virtual agent based on counterfactual minimization (CFR) is used to accelerate testing and validate the safety performance of autonomous vehicles. The virtual agent updates the adversarial policies to be enforced by continuously accumulating regret values, thus increasing the probability of security-critical events occurring during the testing process. Finally, recognized metrics such as Time-to-Collision (TTC) and Minimum Safe Distance Factor (MSDF) are introduced to assess the quality of the scenario. Experimental results show that the virtual agent based on counterfactual minimization explicitly generates more safety-critical scenarios and accelerates the evaluation process by multiple orders of magnitude (\(10^{3}\) times faster).