Current driver assistance systems and autonomous driving stacks are limited to well-defined environment conditions and geo fenced areas. To increase driving safety in adverse weather conditions, broadening the application spectrum of autonomous driving and driver assistance systems is necessary. In order to enable this development, reproducible benchmarking methods are required to quantify the expected distortions. In this publication, a testing methodology for disturbances from spray is presented. It introduces a novel lightweight and configurable spray setup alongside an evaluation scheme to assess the disturbances caused by spray. The analysis covers an automotive RGB camera and two different LiDAR systems, as well as downstream detection algorithms based on YOLOv3 and PV-RCNN. In a common scenario of a closely cutting vehicle, it is visible that the distortions are severely affecting the perception stack up to four seconds showing the necessity of benchmarking the influences of spray.

翻译：为了提高恶劣天气条件下的驾驶安全,有必要扩大自动驾驶和驾驶辅助系统的应用范围。为了能够实现这一发展,需要采用可复制的基准方法来量化预期的扭曲现象。在本出版物中介绍了一种喷雾扰动测试方法。该出版物采用了一种新型的轻量和可配置喷雾装置,与评估计划一起评估喷雾引起的扰动。该分析包括一辆汽车的 RGB 照相机和两个不同的LIDAR系统,以及基于YOLOv3和PV-RCNN的下游探测算法。在紧切车辆的常见情况下,这些扭曲现象严重影响了人们的观念,高达四秒钟,表明有必要对喷雾的影响进行基准评估。