Collision Avoidance Testing for Autonomous Driving Systems on Complete Maps

Collision avoidance is one of the crucial functions of autonomous driving systems (ADSs) to guarantee the safety of autonomous vehicles (AVs). It requires extensive testing before an AV is deployed to public roads. Most of the current ADS testing methods generate test cases either from real traffic data or manually designed for some specific scenarios. There is little work on systematic methods to generate test cases from a complete map where an AV operates. Systematic testing on such a map is challenging due to the enormous scenarios. In this paper, we propose a collision-avoidance testing method for ADSs running on a map, which aims to reduce the scenario space while maintaining scenario diversity. The method consists of test case classification and test case generation. First, we build the topology structure of a map, based on which we classify possible scenarios into different classes. Second, we divide test cases into different classes using the topology-based scenario classification and fuzzy number-based motion evaluation. Third, we implement a bisection method to generate test cases that can efficiently expose ADSs' failures. We evaluate our method on one of the state-of-the-art ADSs, Baidu Apollo. The experiment results show that our method discovers Apollo's issues effectively while reducing the number of generated test cases by 77.36%, compared with the random method.