  Modeling perception sensors is key for simulation based testing of automated
driving functions. Beyond weather conditions themselves, sensors are also
subjected to object dependent environmental influences like tire spray caused
by vehicles moving on wet pavement. In this work, a novel modeling approach for
spray in lidar data is introduced. The model conforms to the Open Simulation
Interface (OSI) standard and is based on the formation of detection clusters
within a spray plume. The detections are rendered with a simple custom ray
casting algorithm without the need of a fluid dynamics simulation or physics
engine. The model is subsequently used to generate training data for object
detection algorithms. It is shown that the model helps to improve detection in
real-world spray scenarios significantly. Furthermore, a systematic real-world
data set is recorded and published for analysis, model calibration and
validation of spray effects in active perception sensors. Experiments are
conducted on a test track by driving over artificially watered pavement with
varying vehicle speeds, vehicle types and levels of pavement wetness. All
models and data of this work are available open source.

Robotics;Computer Vision and Pattern Recognition;Signal Processing