Data-driven simulation and parametrization of traffic scenarios for the development of advanced driver assistance systems

Abstract

The validation and verification of cognitive skills of highly automated vehicles is an important milestone for legal and public acceptance of advanced driver assistance systems (ADAS). In this paper, we present an innovative data-driven method in order to create critical traffic situations from recorded sensor data. This concept is completely contrary to previous approaches using parametrizable simulation models. We demonstrate our concept at the example of parametrizing lane change maneuvers: Firstly, the road layout is automatically derived from observed vehicle trajectories. The road layout is then used in order to detect vehicle maneuvers, which is shown exemplarily on lane change maneuvers. Then, the maneuvers are parametrized using data operators in order to create critical traffic scenarios. Finally, we demonstrate our concept using LIDAR-captured traffic situations on urban and highway scenes, creating critical scenarios out of safely recorded data.