Behavior planning and decision-making are some of the biggest challenges for highly automated systems. A fully automated vehicle (AV) is faced with numerous tactical and strategical choices. Most state-of-the-art AV platforms are implementing tactical and strategical behavior generation using finite state machines. However, these usually result in poor explainability, maintainability and scalability. Research in robotics has raised many architectures to mitigate these problems, most interestingly behavior-based systems and hybrid derivatives. Inspired by these approaches, we propose a hierarchical behavior-based architecture for tactical and strategical behavior generation in automated driving. It is a generalizing and scalable decision-making framework, utilizing modular behavior blocks to compose more complex behaviors in a bottom-up approach. The system is capable of combining a variety of scenario- and methodology-specific solutions, like POMDPs, RRT* or learning-based behavior, into one understandable and traceable architecture. We extend the hierarchical behavior-based arbitration concept to address scenarios where multiple behavior options are applicable, but have no clear priority among each other. Then, we formulate the behavior generation stack for automated driving in urban and highway environments, incorporating parking and emergency behaviors as well. Finally, we illustrate our design in an explanatory evaluation.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme


    Beteiligte:


    Erscheinungsdatum :

    2020-10-19


    Format / Umfang :

    763248 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Decision-making for automated vehicles at intersections adapting human-like behavior

    de Beaucorps, Pierre / Streubel, Thomas / Verroust-Blondet, Anne et al. | IEEE | 2017


    Decision-Making for Automated Vehicles at Intersections Adapting Human-Like Behavior

    de Beaucorps, Pierre / Streubel, Thomas / Verroust-Blondet, Anne et al. | British Library Conference Proceedings | 2017



    Implicit cooperative decision-making for automated vehicles

    Kurzer, Karl | TIBKAT | 2023

    Freier Zugriff

    Arbitration decision in Indianapolis

    Engineering Index Backfile | 1914