论文标题
自动驾驶汽车的可解释安全验证
Interpretable Safety Validation for Autonomous Vehicles
论文作者
论文摘要
自主驾驶的一个开放问题是如何在模拟中验证自动驾驶汽车的安全性。自动测试程序可以发现自主系统的失败，但是由于其高维度，这些失败可能难以解释，并且可能不太重要，以至于不重要。这项工作描述了一种发现自主系统可解释故障的方法。失败是通过人类可以理解的信号时间逻辑表达式描述的，并且被优化以产生可能性很高的故障。在未受保护的左转弯和与行人的人行横道的背景下，我们证明了我们的方法用于对自动驾驶汽车的安全验证。与基线重要性采样方法相比，我们的方法论发现更多的失败，而在保持可解释性的同时，可能性更高。
An open problem for autonomous driving is how to validate the safety of an autonomous vehicle in simulation. Automated testing procedures can find failures of an autonomous system but these failures may be difficult to interpret due to their high dimensionality and may be so unlikely as to not be important. This work describes an approach for finding interpretable failures of an autonomous system. The failures are described by signal temporal logic expressions that can be understood by a human, and are optimized to produce failures that have high likelihood. Our methodology is demonstrated for the safety validation of an autonomous vehicle in the context of an unprotected left turn and a crosswalk with a pedestrian. Compared to a baseline importance sampling approach, our methodology finds more failures with higher likelihood while retaining interpretability.
