Goelles Thomas, Schlager Birgit, Muckenhuber Stefan
VIRTUAL VEHICLE Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria.
Sensors (Basel). 2020 Jun 30;20(13):3662. doi: 10.3390/s20133662.
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.
近年来，诸如摄像头、雷达和激光雷达等感知传感器在汽车行业中颇受欢迎。为了向自动驾驶迈进一步，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解决方案。这是一个关键前提，因为自动驾驶功能的质量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。本出版物对汽车感知传感器的故障以及合适的检测和恢复方法进行了系统综述，并提出了相应的分类架构。为了回顾当前的技术水平并识别有前景的研究机会，已针对激光雷达进行了系统的文献分析。与恶劣天气条件相关的故障研究最多，但往往没有提供合适的恢复方法。与传感器安装和传感器罩机械损坏相关的问题研究很少，为未来研究提供了机会。事实证明，使用单个传感器数据流的算法对于故障检测和恢复都是可行的解决方案。
