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.
Semantic Grid-Based Road Model Estimation for Autonomous Driving
2019 IEEE Intelligent Vehicles Symposium (IV) ; 2329-2336
2019-06-01
3472538 byte
Conference paper
Electronic Resource
English
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