The length of the planning horizon when using nonlinear model predictive control is limited by computational resources. To achieve long-horizon vehicle planning and control, the practical feasibility of applying real-time iterations is demonstrated with an experiment on an automated vehicle. The proposed implementation is real-time capable with a planning horizon of over 10s, requiring a fraction of the computational time compared to a solver for general nonlinear programming problems. The solutions obtained are close to the local optimum, even in the presence of real-world disturbances. Important details for the successful implementation in this domain are discussed and illustrated through simulation.


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    Titel :

    Long-Horizon Vehicle Planning and Control Through Real-Time Iterations


    Beteiligte:


    Erscheinungsdatum :

    2023-06-04


    Format / Umfang :

    1229074 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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