On-road motion planning for autonomous vehicles is in general a challenging problem. Past efforts have proposed solutions for urban and highway environments individually. We identify the key advantages/shortcomings of prior solutions, and propose a novel two-step motion planning system that addresses both urban and highway driving in a single framework. Reference Trajectory Planning (I) makes use of dense lattice sampling and optimization techniques to generate an easy-to-tune and human-like reference trajectory accounting for road geometry, obstacles and high-level directives. By focused sampling around the reference trajectory, Tracking Trajectory Planning (II) generates, evaluates and selects parametric trajectories that further satisfy kinodynamic constraints for execution. The described method retains most of the performance advantages of an exhaustive spatiotemporal planner while significantly reducing computation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Focused Trajectory Planning for autonomous on-road driving


    Contributors:
    Gu, Tianyu (author) / Snider, Jarrod (author) / Dolan, John M. (author) / Lee, Jin-woo (author)


    Publication date :

    2013-06-01


    Size :

    1879437 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    FOCUSED TRAJECTORY PLANNING FOR AUTONOMOUS ON-ROAD DRIVING

    Gu, T. / Snider, J. / Dolan, J. et al. | British Library Conference Proceedings | 2013


    Hybrid Trajectory Planning for Autonomous Driving in On-Road Dynamic Scenarios

    Lim, Wonteak / Lee, Seongjin / Sunwoo, Myoungho et al. | IEEE | 2021


    Driving-behavior-oriented trajectory planning for autonomous vehicle driving on urban structural road

    Zeng, Dequan / Yu, Zhuoping / Xiong, Lu et al. | SAGE Publications | 2021


    Trajectory Planning of Autonomous Driving Vehicles Based on Road-Vehicle Fusion

    Li, Han / Yu, Guizhen / Zhou, Bin et al. | ASCE | 2020