Intelligent testing environment generation for autonomous vehicles with implicit distributions of traffic behaviors

Abstract

The advancement of autonomous vehicles hinges significantly on addressing safety concerns and obtaining reliable evaluation results. Testing the safety of autonomous vehicles is challenging due to the complexity of the high-dimensional traffic environment and the rarity of safety-critical events, often requiring billions of miles to achieve comprehensive validation, which is inefficient and costly. Current approaches, such as accelerated testing using importance sampling, aim to provide unbiased estimates of the performance of autonomous vehicles by generating a new distribution of background vehicles’ behaviors based on an initial nominal distribution. However, these methods require knowledge of the original distribution of traffic behaviors, which is often difficult to obtain in practice. In response to these challenges, we introduce a novel methodology termed implicit importance sampling (IIS). Unlike traditional methods, IIS is designed to generate intelligent driving environments based on implicit distributions of traffic behaviors where the true distributions are unknown or not explicitly defined. IIS method leverages accept-reject sampling to construct an unnormalized proposal distribution, which increases the likelihood of sampling adversarial cases. Through applying importance sampling technique with unnormalized proposal distribution, IIS enhances testing efficiency and obtains reliable and representative evaluation results as well. The bias caused by unnormalization is also proved to be controlled and bounded.