Highlights It serves as an introduction and overview of the potentially useful models and methodologies from artificial intelligence (AI) into the field of transportation engineering for autonomous vehicle (AV) control in the era of mixed autonomy. The first-of-its-kind survey paper in both transportation engineering and AI for mixed traffic modeling. Inspire our transportation community and stimulate conversations with other disciplines.

    Abstract This paper serves as an introduction and overview of the potentially useful models and methodologies from artificial intelligence (AI) into the field of transportation engineering for autonomous vehicle (AV) control in the era of mixed autonomy when AVs drive alongside human-driven vehicles (HV). It is the first-of-its-kind survey paper to comprehensively review literature in both transportation engineering and AI for mixed traffic modeling. We will discuss state-of-the-art applications of AI-guided methods, identify opportunities and obstacles, and raise open questions. We divide the stage of AV deployment into four phases: the pure HVs, the HV-dominated, the AV-dominated, and the pure AVs. This paper is primarily focused on the latter three phases. Models used for each phase are summarized, encompassing game theory, deep (reinforcement) learning, and imitation learning. While reviewing the methodologies, we primarily focus on the following research questions: (1) What scalable driving policies are to control a large number of AVs in mixed traffic comprised of human drivers and uncontrollable AVs? (2) How do we estimate human driver behaviors? (3) How should the driving behavior of uncontrollable AVs be modeled in the environment? (4) How are the interactions between human drivers and autonomous vehicles characterized? We also provide a list of public datasets and simulation software related to AVs. Hopefully this paper will not only inspire our transportation community to rethink the conventional models that are developed in the data-shortage era, but also start conversations with other disciplines, in particular robotics and machine learning, to join forces towards creating a safe and efficient mixed traffic ecosystem.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning


    Beteiligte:
    Di, Xuan (Autor:in) / Shi, Rongye (Autor:in)


    Erscheinungsdatum :

    2021-02-01




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    SYSTEM AND METHOD FOR REMOTE CONTROL GUIDED AUTONOMY FOR AUTONOMOUS VEHICLES

    YUMER MEHMET ERSIN / HOU XIAODI | Europäisches Patentamt | 2023

    Freier Zugriff

    Mixed-Autonomy Traffic Control with Proximal Policy Optimization

    Wei, Haoran / Liu, Xuanzhang / Mashayekhy, Lena et al. | IEEE | 2019


    AUTONOMOUS DRIVING VEHICLE AND CONTROL METHOD FOR AUTONOMOUS DRIVING VEHICLE

    ARAKAWA SEIJI | Europäisches Patentamt | 2019

    Freier Zugriff

    AUTONOMOUS DRIVING VEHICLE AND CONTROL METHOD OF AUTONOMOUS DRIVING VEHICLE

    CRONIN JOHN / HUFFINES CHRISTOPHER MICHAEL | Europäisches Patentamt | 2019

    Freier Zugriff