Dynamic on-road driving scenarios require robust methods for planning a safe and feasible vehicle motion coping with both static and dynamic obstacles. Many of the different approaches which have been proposed to tackle this challenge are based on optimal control and employ local continuous or discrete optimization schemes. While discrete methods possess the ability to find reasonable solutions in a search space incorporating local minima, they tend to sacrifice optimality for real-time performance. On the other hand, local continuous methods require suitable initialization to handle the combinatorial nature of on-road driving scenarios but are capable of quickly returning an optimized solution. The presented work proposes a hybrid solution which embraces both strategies to unite their distinct advantages. A discrete optimization scheme is augmented by parametric optimization to achieve both low planning times as well as robust (re-)initialization.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Hybrid discrete-parametric optimization for trajectory planning in on-road driving scenarios


    Contributors:


    Publication date :

    2016-11-01


    Size :

    548868 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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

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



    TOWARDS RISK MINIMIZING TRAJECTORY PLANNING IN ON-ROAD SCENARIOS

    Ward, Erik / Folkesson, John | British Library Conference Proceedings | 2018


    TRAJECTORY PLANNING IN AUTONOMOUS DRIVING VEHICLES FOR UNFORESEEN SCENARIOS

    JIANG SHU / WU SZU HAO / LIU HAO et al. | European Patent Office | 2024

    Free access

    Hybrid trajectory planning approach for roundabout merging scenarios

    Hidalgo, Carlos / Lattarulo, Ray / Perez, Joshue et al. | IEEE | 2019