Semantic Grid-Based Road Model Estimation for Autonomous Driving

For autonomous driving, knowledge about the current environment and especially the driveable lanes is of utmost importance. Currently this information is often extracted from meticulously (hand-)crafted offline high-definition maps, restricting the operation of autonomous vehicles to few well-mapped areas and making it vulnerable to temporary or permanent environment changes. This paper addresses the issues of map-based road models by building the road model solely from online sensor measurements. Based on Dempster-Shafer theory and a novel frame of discernment, sensor measurements, such as lane markings, semantic segmentation of drivable and non-drivable areas and the trajectories of other observed traffic participants are fused into semantic grids. Geometrical lane information is extracted from these grids via an iterative path-planning method. The proposed approach is evaluated on real measurement data from German highways and urban areas.