Generating a Hazard Map of Dynamic Objects Using Lidar Mobile Mapping

YOU? · · 2016 · Open Access · · DOI: https://doi.org/10.14358/pers.82.12.967

One of the hardest problems for future self-driving cars is to predict hazardous situations involving pedestrians and cyclists. Human drivers solve this problem typically by having a deeper understanding of the scene. The technical equivalent of this is to provide a hazard map, which serves as a prior for self-driving cars, enabling them to adjust driving speed and processing thresholds. In this paper, we present a method to derive such a hazard map using lidar mobile mapping. Pedestrians and cyclists are obtained from a sequence of point clouds by segmentation and classification. Their locations are then accumulated in a grid map, which serves as a “heat map” for possible hazardous situations. To demonstrate our approach, we generated a map using lidar mobile mapping, obtained by twelve measurement campaigns in Hanover (Germany). Our results show different outcomes for the city center, residential areas, busy roads, and road junctions.