Simulating Road Spray Effects in Automotive Lidar Sensor Models

Although lidar sensors have emerged as a cornerstone sensing modality in autonomous driving, they face significant challenges in adverse weather conditions. A particularly detrimental effect is spray — a phenomenon where water particles are whirled up by vehicles driving with high velocities on wet roads. Spray often causes clutter points in lidar data that are falsely classified as vehicles by downstream object detectors. In this work, a phenomenological spray simulation model, suitable as an augmentation method for object detection algorithms, is presented. Two distinct datasets featuring real-world spray scenarios are recorded and analyzed, with the first serving for calibrating the simulation model through extensive experiments that vary vehicle speeds, types, and pavement wetness levels. The second dataset functions as a spray test set to evaluate the effectiveness of the simulation model in the context of object detection. Employing the simulation model as an augmentation tool reveals an improvement of up to 17% in Average Precision for state-of-the-art object detection methods in real spray conditions.