Risk Scenario Generation for Autonomous Driving Systems based on Scenario Evaluation Model

The development of deep learning-based au-tonomous driving systems is becoming increasingly prevalent in recent times, however, several safety concerns have emerged. In certain uncommon situations, the generalization and robustness of deep learning have resulted in safety crises and accidents. To address this, it is crucial to comprehensively cover a range of possible conditions in simulators and identify risk scenarios within the system, which poses a significant high-dimensional search problem. To efficiently, accurately, and comprehensively generate diverse risk scenarios, we propose a scenario evaluation model. This model can learn the distribution of risk factors from a limited number of scenario samples and provide pre-evaluation to guide the generation process. The experimental results show that that our method can generate an average of 40.6% more risk scenarios compared to other generation methods, while also requiring 63.6% fewer simulations. The method based on the scenario evaluation model can effectively improve efficiency, accuracy, and the identification of more risk scenarios, thus providing a more thorough evaluation of the safety performance of autonomous driving systems.