Stochastic Physical Simulation Framework to Quantify the Effect of Rainfall on Automotive Lidar 2019-01-0134
Due to the safety relevance of environment perceiving sensors for highly automated driving vehicles, one has to assess the sensors’ performance. One challenge is to quantify the effect of adverse weather conditions on the performance early in the sensor development. The lidar equation was previously employed in this context to derive estimates of a lidar’s maximum range. In this work, we present a stochastic simulation framework based on a probabilistic extension of the lidar equation, to quantify the effect of adverse rainfall conditions on a lidar’s raw detection performance. To this end, we combine basic probabilistic models for key rainfall parameters with Mie theory and the theory of signal detection in a Monte Carlo simulation framework. This allows to analyze and optimize a sensor’s design early in the sensor development, when physical testing is not yet possible. A challenge not addressed in this work is to include the effect of road spray water on the lidars performance. Combining the effect of other noise sources with the presented framework with a ray tracer is an opportunity for realistic physical lidar simulations and would allow to virtually estimate the performance of a lidar’s object detection and tracking performance.
Mario Berk, Michael Dura, Jose Vargas Rivero, Olaf Schubert, Hans-Martin Kroll, Boris Buschardt, Daniel Straub
Technical University of Munich, AUDI AG