Safety-critical scenario test for intelligent vehicles via hybrid participation of natural and adversarial agents

The intensity levels of autonomous vehicles should be thoroughly evaluated before deployment, while vehicle tests are difficult for the sake of heavy experimental resources and large numbers of cases, especially tests that include safety-critical scenarios. In this study, a new scenario generation method is proposed to accelerate the test, which is based on a multiagent reinforcement learning (MARL) framework incorporating the driving potential field (DPF). This framework is used to train some background vehicles to enable high-risk and marginal scenes, where the DPF is applied to enact the rewards of the adversarial background agents. Other background vehicles that use reasonable driving policies, which serve as naturalistic agents to increase scenario diversity, are also considered. The coexistence of naturalistic and adversarial agents enriches the experiences learned by the background cars, providing more marginal and risky scenarios for accelerating the test. The experimental results demonstrate the efficiency of the generation of high-risk and marginal scenes, with comprehensive assessments via a novel field-based dynamic risk evaluation method.