Designing traffic-smoothing cruise controllers that can be deployed onto autonomous vehicles is a key step towards improving traffic flow, reducing congestion, and enhancing fuel efficiency in mixed autonomy traffic. We bypass the common issue of having to carefully fine-tune a large traffic micro-simulator by leveraging real-world trajectory data from the I–24 highway in Tennessee, replayed in a one-lane simulation. Using standard deep reinforcement learning methods, we train energy-reducing wave-smoothing policies. As an input to the agent, we observe the speed and distance of only the vehicle in front, which are local states readily available on most recent vehicles, as well as non-local observations about the downstream state of the traffic. We show that at a low 4% autonomous vehicle penetration rate, we achieve significant fuel savings of over 15% on trajectories exhibiting many stop-and-go waves. Finally, we analyze the smoothing effect of the controllers and demonstrate robustness to adding lane-changing into the simulation as well as the removal of downstream information.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data


    Beteiligte:
    Lichtle, Nathan (Autor:in) / Jang, Kathy (Autor:in) / Shah, Adit (Autor:in) / Vinitsky, Eugene (Autor:in) / Lee, Jonathan W. (Autor:in) / Bayen, Alexandre M. (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    7946968 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning

    Naveed, Kaleb Ben / Qiao, Zhiqian / Dolan, John M. | IEEE | 2021


    Tackling Real-World Autonomous Driving using Deep Reinforcement Learning

    Maramotti, Paolo / Capasso, Alessandro Paolo / Bacchiani, Giulio et al. | ArXiv | 2022

    Freier Zugriff

    Tackling Real-World Autonomous Driving using Deep Reinforcement Learning

    Maramotti, Paolo / Capasso, Alessandro Paolo / Bacchiani, Giulio et al. | IEEE | 2022


    A Trajectory Simulation Approach for Autonomous Vehicles Path Planning using Deep Reinforcement Learning

    de Oliveira Lima, Jean Phelipe / Oliveira, Raimundo Correa de / Costa, Cleinaldo de Almeida | BASE | 2020

    Freier Zugriff

    Deep Reinforcement Learning combined with RRT for trajectory tracking of autonomous vehicles.

    Balint, Kovari / Gergo, Angyal Balint / Tamas, Becsi | Elsevier | 2024

    Freier Zugriff