In-Field Measurement and Methodology for Modeling and Validation of Precipitation Effects on Solid-State LiDAR Sensors

There is a strong demand for high fidelity sensor models which are capable of simulating realistic automotive sensor perception of Radar, LiDAR and camera sensors in real time, in order to validate advanced driving assistance functions like lane change assist (LCA), automated emergency breaking (AEB), or even path planning virtually. For central data fusion the sensor models need to deliver realistic, artificial sensor raw data. In especially, optical sensors are heavily influenced by precipitation, fog and sun irradiance. However, most LiDAR models lack the capability of replicating the impact of specific weather characteristics. Furthermore, there i - in contrast to numerous publicly available LiDAR datasets - a strong lack of datasets which are annotated with quantitative weather data such as the precipitation rate or meteorological visibility in order to develop and validate such models. Hence, within this work, an automated infrastructure is setup to measure time-correlated LiDAR and weather data to develop and calibrate weather models. The effects of varying precipitation rates on an automotive Flash LiDAR system is demonstrated based on in-field measurements and a respective modeling methodology is developed. Based on the in-field measurement data, raw data LiDAR models can be developed which augment virtual LiDAR data obtained from raytracing capable driving simulation suits as well as real data, recorded under ideal weather conditions.