Safe and human-like path planning in urban environments remain one of the most challenging problems for autonomous driving. This paper proposes a solution to plan traffic-free optimal and smooth paths using the most spread open source navigation data structure, OpenStreetMaps. To that end, a procedure to automatically transform raw OSM data into the drivable space is combined with a MINLP optimization algorithm that finds the most suitable position of intermediate waypoints to connect Bézier primitive curves. The proposed planner is validated in a real complex roundabout, for which very smooth paths are generated, even considering obstacles.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Smooth path planning for urban autonomous driving using OpenStreetMaps


    Contributors:


    Publication date :

    2017-06-01


    Size :

    1106540 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Smooth Path Planning for Urban Autonomous Driving Using OpenStreetMaps

    Artuñedo, Antonio / Godoy, Jorge / Villagra, Jorge | British Library Conference Proceedings | 2017


    Smooth path planning for urban autonomous driving using OpenStreetMaps

    Artuñedo, Antonio / Godoy, Jorge / Villagrá, Jorge | BASE | 2017

    Free access

    Path Planning for Autonomous Bus Driving in Urban Environments

    Oliveira, Rui / Lima, Pedro F. / Pereira, Gonçalo Collares et al. | ArXiv | 2019

    Free access

    Smooth Path Planning for Autonomous Parking System

    Yang, Yi / Zhang, Lu / Qu, Xin et al. | British Library Conference Proceedings | 2017


    Smooth path planning for autonomous parking system

    Yi, Yang / Lu, Zhang / Xin, Qu et al. | IEEE | 2017