上海科技大学知识管理系统

Autonomous vehicle (AV) systems must be comprehensively tested and evaluated before they can be deployed. High-fidelity simulators such as CARLA or LGSVL allow this to be done safely in very realistic and highly customizable environments. Existing testing approaches, however, fail to test simulated AVs systematically, as they focus on specific scenarios and oracles (e.g., lane following scenario with the “no collision” requirement) and lack any coverage criteria measures. In this paper, we propose $\mathtt {AVUnit}$, a framework for systematically testing AV systems against customizable correctness specifications. Designed modularly to support different simulators, $\mathtt {AVUnit}$ consists of two new languages for specifying dynamic properties of scenes (e.g. changing pedestrian behaviour after waypoints) and fine-grained assertions about the AV's journey. $\mathtt {AVUnit}$ further supports multiple fuzzing algorithms that automatically search for test cases that violate these assertions, using robustness and coverage measures as fitness metrics. We evaluated the implementation of $\mathtt {AVUnit}$ for the LGSVL+Apollo simulation environment, finding 19 kinds of issues in Apollo, which indicate that the open-source Apollo does not perform well in complex intersections and lane-changing related scenarios.