Simulation of falling rain for robustness testing of video-based surround sensing systems

Recently, optical sensors have become a standard item in modern cars, raising questions with respect to the necessary testing under various ambient effects. In order to achieve a high test coverage of vision-based surround sensing systems, a lot of different environmental conditions need to be tested. Unfortunately, it is by far too time-consuming to build test sets of all relevant environmental conditions by recording real video data. This paper presents a novel approach for ambient-aware virtual prototyping and robustness testing. We propose a method to significantly reduce the needed on-road recordings being used for design and validation of vision-based Advanced Driver Assistance Systems (ADAS) and fully automated driving. Our approach facilitates the generation of comparable test sets by using largely reduced amounts of real on-road recordings and applying computer-generated variations of falling rain to it in a comprehensive virtual prototyping environment. In combination with the simulation of camera properties, which influence the visual effects of falling rain to a great extent, we are able to generate different rain scenarios under a wide variety of parameters. Our approach has been applied to an automotive lane detection system using a series of multiple rain scenarios. We have explored, how falling rain can influence such a system and how such behavior can be detected using simulated rain scenarios.