  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.
