Intelligent Safety Testing of Autonomous Vehicles With Implicit Traffic Distributions by Dense Reinforcement Learning

Abstract

Reliable safety evaluation is essential for the development and large-scale deployment of autonomous vehicles (AVs). However, the complexity of high-dimensional traffic environments and the rarity of safety-critical events make direct testing extremely inefficient and costly, requiring an enormous number of simulation miles. Importance sampling has been employed to accelerate this process, but most existing methods rely on known or explicit traffic behavior distributions, which are often inaccessible in practice. To address this limitation, we propose Deep-IIS, which extends implicit importance sampling by utilizing rejection sampling to handle unknown distributions without requiring explicit density functions. By integrating dense deep reinforcement learning to adaptively optimize key sampling parameters, Deep-IIS intelligently concentrates sampling on high-risk regions, significantly improving testing efficiency while maintaining evaluation reliability. We validate the proposed method in two representative traffic scenarios, demonstrating its superior ability to uncover safety-critical cases and to produce accurate performance estimates under implicit distributions.