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.


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    Title :

    Semantic Grid-Based Road Model Estimation for Autonomous Driving


    Contributors:


    Publication date :

    2019-06-01


    Size :

    3472538 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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