Assessment of LIDAR Imaging Systems for Autonomous Vehicles in Adverse Weather and Under Dynamic Conditions

One of the main challenges for the safety validation of autonomous driving vehicles lies in the influence of weather phenomena. As each of the main sensors, namely LIDAR, radar, and cameras increases its sensitivity to detect smaller objects faster and hence be able to drive autonomously at higher speeds, the possible influence of environmental perturbations on their perception increases. Those perturbations could cause false positives, confuse self-calibration algorithms and reduce the sensor range. On the other hand, they could constitute a source of valuable information if the dependencies are known and properly characterized to better evaluate and predict road conditions or adapt its operation mode. The main purpose of this work is to evaluate the impact of adverse weather conditions mainly heavy snow on LIDAR sensor performance in dynamic conditions, i.e. moving sensors and moving-target scenarios.