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Modeling perception sensors is key for simulation based testing of automateddriving functions. Beyond weather conditions themselves, sensors are alsosubjected to object dependent environmental influences like tire spray causedby vehicles moving on wet pavement. In this work, a novel modeling approach forspray in lidar data is introduced. The model conforms to the Open SimulationInterface (OSI) standard and is based on the formation of detection clusterswithin a spray plume. The detections are rendered with a simple custom raycasting algorithm without the need of a fluid dynamics simulation or physicsengine. The model is subsequently used to generate training data for objectdetection algorithms. It is shown that the model helps to improve detection inreal-world spray scenarios significantly. Furthermore, a systematic real-worlddata set is recorded and published for analysis, model calibration andvalidation of spray effects in active perception sensors. Experiments areconducted on a test track by driving over artificially watered pavement withvarying vehicle speeds, vehicle types and levels of pavement wetness. Allmodels and data of this work are available open source.

Simulating Road Spray Effects in Automotive Lidar Sensor Models