Phenomenological, Measurement Based LiDAR Sensor Model

The advancing automation within the mobility sector poses new challenges. The open parameter space of potential traffic scenarios turns out to be difficult in the development and certification of advanced driver assistance systems. Scenario based, simulative validation of driving functions appears to be a promising solution. Given the assumption that only a fraction of all traffic scenarios is safety critical and should be considered for the evaluation of driver assistance systems, a simulation based selection of test relevant driving scenarios can be carried out. With realistic sensor models available the virtual testing of driver assistance systems is cheaper and faster than conventional test drives. Phenomenological sensor models do not require detailed environment models and therefore compromise accuracy and effort. The objective of this work is the development of a phenomenological LiDAR sensor model that reproduces the actual, measured detection capability of LiDAR sensors. Avoiding empirical radar backscatter cross sections, that strongly distort the detection capability of conventional LiDAR sensor models and mapping the measured detection capability onto the phenomenological LiDAR sensor model promises enhanced model accuracy over traditional phenomenological modeling approaches.