Game-theoretic decision making has the potential to bring human-like reasoning skills to autonomous vehicles (AVs), fostering trust between humans and AVs. However, to make these approaches sufficiently practical for real-world use, challenges such as game complexity and incomplete information have to be addressed. Game complexity refers to the difficulties in solving a game-theoretic problem, which include solution existence, algorithm convergence, and scalability. We show in our recent work that a possible solution to overcoming these difficulties is to use potential games. However, constructing a potential game often requires specific cost function designs, limiting their broad use. To address this challenge, we propose to employ a game projection technique in this paper, relaxing the cost function design conditions and making the potential game approach applicable to broader scenarios, even including the ones that cannot be modelled as a potential game. Incomplete information refers to the ego vehicle’s lack of knowledge of other traffic agents’ cost functions. In a driving scenario, deviations of the ego vehicle assumed/estimated others’ cost functions from their actual ones are often inevitable. This necessitate the robustness analysis of a game-theoretic solution. This paper defines the robustness margin of a game solution as the maximum magnitude of cost function deviations that can be accommodated without changing the optimality of the game solution. With this definition, closed-form robustness margins are derived. Numerical studies using highway lane-changing scenarios are reported.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Game Projection and Robustness for Game-Theoretic Autonomous Driving


    Beteiligte:
    Liu, Mushuang (Autor:in) / Eric Tseng, H. (Autor:in) / Filev, Dimitar (Autor:in) / Girard, Anouck (Autor:in) / Kolmanovsky, Ilya (Autor:in)


    Erscheinungsdatum :

    01.03.2025


    Format / Umfang :

    951437 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers

    Chandra, Rohan / Wang, Mingyu / Schwager, Mac et al. | ArXiv | 2022

    Freier Zugriff

    Game Theoretic Decision-Making for Autonomous Driving Considering Traffic Rules at Roundabouts

    Huang, Liting / Chu, Duanfeng / Deng, Zejian et al. | IEEE | 2024



    Intersection Management for Connected Autonomous Vehicles: A Game Theoretic Framework

    Wei, Haoran / Mashayekhy, Lena / Papineau, Jake | IEEE | 2018


    Game theoretic decision making

    DAI SIYU / BAE SANGJAE / ISELE DAVID F | Europäisches Patentamt | 2025

    Freier Zugriff