Autonomous Driving Systems (ADSs) are complex Cyber-Physical Systems (CPSs) that must ensure safety even in uncertain conditions. Modern ADSs often employ Deep Neural Networks (DNNs), which may not produce correct results in every possible driving scenario. Thus, an approach to estimate the confidence of an ADS at runtime is necessary to prevent potentially dangerous situations. In this paper we propose MarMot, an online monitoring approach for ADSs based on Metamorphic Relations (MRs), which are properties of a system that hold among multiple inputs and the corresponding outputs. Using domain-specific MRs, MarMot estimates the uncertainty of the ADS at runtime, allowing the identification of anomalous situations that are likely to cause a faulty behavior of the ADS, such as driving off the road. We perform an empirical assessment of MarMot with five different MRs, using two different subject ADSs, including a small-scale physical ADS and a simulated ADS. Our evaluation encompasses the identification of both external anomalies, e.g., fog, as well as internal anomalies, e.g., faulty DNNs due to mislabeled training data. Our results show that MarMot can identify up to 65\% of the external anomalies and 100\% of the internal anomalies in the physical ADS, and up to 54\% of the external anomalies and 88\% of the internal anomalies in the simulated ADS. With these results, MarMot outperforms or is comparable to other state-of-the-art approaches, including SelfOracle, Ensemble, and MC Dropout-based ADS monitors.

翻译：自动驾驶系统（ADS）是复杂的网络物理系统（CPS），即使在不确定条件下也必须确保安全。现代ADS通常采用深度神经网络（DNN），而DNN并非在所有可能的驾驶场景下都能产生正确结果。因此，需要一种在运行时估计ADS置信度的方法，以防止潜在的危险情况。本文提出MarMot，一种基于蜕变关系（MR）的ADS在线监控方法。蜕变关系是系统在多个输入及对应输出之间保持的特性。MarMot利用领域特定的MR，在运行时估计ADS的不确定性，从而能够识别可能导致ADS故障行为（例如驶离道路）的异常情况。我们使用五种不同的MR，对两个不同的目标ADS（包括一个小型物理ADS和一个模拟ADS）进行了MarMot的实证评估。我们的评估涵盖外部异常（例如雾）和内部异常（例如因训练数据标注错误导致的故障DNN）的识别。结果表明，在物理ADS中，MarMot能识别高达65%的外部异常和100%的内部异常；在模拟ADS中，能识别高达54%的外部异常和88%的内部异常。基于这些结果，MarMot的性能优于或可与包括SelfOracle、Ensemble和基于MC Dropout的ADS监控器在内的其他先进方法相媲美。