Perception sensors such as camera, radar, and lidar have gained considerable popularity in the automotive industry in recent years. In order to reach the next step towards automated driving it is necessary to implement fault diagnosis systems together with suitable mitigation solutions in automotive perception sensors. This is a crucial prerequisite, since the quality of an automated driving function strongly depends on the reliability of the perception data, especially under adverse conditions. This publication presents a systematic review on faults and suitable detection and recovery methods for automotive perception sensors and suggests a corresponding classification schema. A systematic literature analysis has been performed with focus on lidar in order to review the state-of-the-art and identify promising research opportunities. Faults related to adverse weather conditions have been studied the most, but often without providing suitable recovery methods. Issues related to sensor attachment and mechanical damage of the sensor cover were studied very little and provide opportunities for future research. Algorithms, which use the data stream of a single sensor, proofed to be a viable solution for both fault detection and recovery.

中文翻译：

---

汽车感知传感器的故障检测，隔离，识别和恢复（FDIIR）方法，包括有关激光雷达的详细文献调查。

近年来，诸如摄像头，雷达和激光雷达的感知传感器在汽车工业中已获得相当大的普及。为了实现自动驾驶的下一步，有必要在汽车感知传感器中实施故障诊断系统以及合适的缓解方案。这是至关重要的前提条件，因为自动驾驶功能的质量在很大程度上取决于感知数据的可靠性，尤其是在不利条件下。该出版物提供了有关汽车感知传感器的故障以及合适的检测和恢复方法的系统综述，并提出了相应的分类方案。为了回顾最新技术并确定有前途的研究机会，已针对激光雷达进行了系统的文献分析。对与恶劣天气条件有关的故障进行了最多的研究，但往往没有提供适当的恢复方法。与传感器连接和传感器盖的机械损坏有关的问题很少研究，并为以后的研究提供了机会。使用单个传感器的数据流的算法被证明是故障检测和恢复的可行解决方案。