High-Speed Autonomous Drifting With Deep Reinforcement Learning

@article{Cai2020HighSpeedAD,
  title={High-Speed Autonomous Drifting With Deep Reinforcement Learning},
  author={Peide Cai and X. Mei and L. Tai and Yuxiang Sun and M. Liu},
  journal={IEEE Robotics and Automation Letters},
  year={2020},
  volume={5},
  pages={1247-1254}
}
  • Peide Cai, X. Mei, +2 authors M. Liu
  • Published 2020
  • Engineering, Computer Science
  • IEEE Robotics and Automation Letters
  • Drifting is a complicated task for autonomous vehicle control. Most traditional methods in this area are based on motion equations derived by the understanding of vehicle dynamics, which is difficult to be modeled precisely. We propose a robust drift controller without explicit motion equations, which is based on the latest model-free deep reinforcement learning algorithm soft actor-critic. The drift control problem is formulated as a trajectory following task, where the error-based state and… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 23 REFERENCES
    Effect of nodes reordering on the schedulability of real-time messages in timed token networks
    2
    Human-level control through deep reinforcement learning
    9319
    CARLA: An Open Urban Driving Simulator
    705
    A Controller for Automated Drifting Along Complex Trajectories
    1