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Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning

Author

Listed:
  • Sorin Liviu Jurj

    (OFFIS e.V. Institute for Information Technology, Escherweg 2, 26121 Oldenburg, Germany)

  • Dominik Grundt

    (OFFIS e.V. Institute for Information Technology, Escherweg 2, 26121 Oldenburg, Germany)

  • Tino Werner

    (OFFIS e.V. Institute for Information Technology, Escherweg 2, 26121 Oldenburg, Germany)

  • Philipp Borchers

    (OFFIS e.V. Institute for Information Technology, Escherweg 2, 26121 Oldenburg, Germany)

  • Karina Rothemann

    (OFFIS e.V. Institute for Information Technology, Escherweg 2, 26121 Oldenburg, Germany)

  • Eike Möhlmann

    (OFFIS e.V. Institute for Information Technology, Escherweg 2, 26121 Oldenburg, Germany)

Abstract

This paper presents a novel approach for improving the safety of vehicles equipped with Adaptive Cruise Control (ACC) by making use of Machine Learning (ML) and physical knowledge. More exactly, we train a Soft Actor-Critic (SAC) Reinforcement Learning (RL) algorithm that makes use of physical knowledge such as the jam-avoiding distance in order to automatically adjust the ideal longitudinal distance between the ego- and leading-vehicle, resulting in a safer solution. In our use case, the experimental results indicate that the physics-guided (PG) RL approach is better at avoiding collisions at any selected deceleration level and any fleet size when compared to a pure RL approach, proving that a physics-informed ML approach is more reliable when developing safe and efficient Artificial Intelligence (AI) components in autonomous vehicles (AVs).

Suggested Citation

  • Sorin Liviu Jurj & Dominik Grundt & Tino Werner & Philipp Borchers & Karina Rothemann & Eike Möhlmann, 2021. "Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning," Energies, MDPI, vol. 14(22), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7572-:d:677898
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    References listed on IDEAS

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    1. Pengwei Wang & Song Gao & Liang Li & Binbin Sun & Shuo Cheng, 2019. "Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm," Energies, MDPI, vol. 12(12), pages 1-14, June.
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    Cited by:

    1. Sorin Liviu Jurj & Tino Werner & Dominik Grundt & Willem Hagemann & Eike Möhlmann, 2022. "Towards Safe and Sustainable Autonomous Vehicles Using Environmentally-Friendly Criticality Metrics," Sustainability, MDPI, vol. 14(12), pages 1-52, June.
    2. Arno Eichberger & Zsolt Szalay & Martin Fellendorf & Henry Liu, 2022. "Advances in Automated Driving Systems," Energies, MDPI, vol. 15(10), pages 1-5, May.

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