Autonomous driving technologies are expected to not only improve mobility and road safety but also bring energy efficiency benefits. In the foreseeable future, autonomous vehicles (AVs) will operate on roads shared with human-driven vehicles. To maintain safety and liveness while simultaneously minimizing energy consumption, the AV planning and decision-making process should account for interactions between the autonomous ego vehicle and surrounding human-driven vehicles. In this chapter, we describe a framework for developing energy-efficient autonomous driving policies on shared roads by exploiting human-driver behavior modeling based on cognitive hierarchy theory and reinforcement learning.


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    Titel :

    Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning


    Weitere Titelangaben:

    Lecture Notes in Intelligent Transportation and Infrastructure


    Beteiligte:
    Murphey, Yi Lu (Herausgeber:in) / Kolmanovsky, Ilya (Herausgeber:in) / Watta, Paul (Herausgeber:in) / Li, Huayi (Autor:in) / Li, Nan (Autor:in) / Kolmanovsky, Ilya (Autor:in) / Girard, Anouck (Autor:in)


    Erscheinungsdatum :

    08.09.2022


    Format / Umfang :

    23 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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