Motion planning plays a key role in autonomous driving. In this work, we introduce the combinatorial aspect of motion planning which tackles the fact that there are usually many possible and locally optimal solutions to accomplish a given task. Those options we call maneuver variants. We argue that by partitioning the trajectory space into discrete solution classes, such that local optimization methods yield an optimum within each discrete class, we can improve the chance of finding the global optimum as the optimum trajectory among the manuever variants. This work provides methods to enumerate the maneuver variants as well as constraints to enforce them. The return of the effort put into the problem modification as suggested is gaining assuredness in the convergency behaviour of the optimization algorithm. We show an experiment where we identify three local optima that would not have been found with local optimization methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    The combinatorial aspect of motion planning: Maneuver variants in structured environments


    Contributors:


    Publication date :

    2015-06-01


    Size :

    750495 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Maneuver Motion Planning Based on Virtual Target for UAVs

    Ma, Rong / Hou, Lin / Zhang, Xianglun | Springer Verlag | 2021


    Maneuver Motion Planning Based on Virtual Target for UAVs

    Ma, Rong / Hou, Lin / Zhang, Xianglun | TIBKAT | 2022


    Semantic state space for high-level maneuver planning in structured traffic scenes

    Kohlhaas, Ralf / Bittner, Thomas / Schamm, Thomas et al. | IEEE | 2014


    Automated Driving in Uncertain Environments: Planning With Interaction and Uncertain Maneuver Prediction

    Hubmann, Constantin / Schulz, Jens / Becker, Marvin et al. | IEEE | 2018