High-Speed Autonomous Drifting with Deep Reinforcement Learning

Peide Cai, Xiaodong Mei, Lei Tai, Yuxiang Sun, Ming Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

70 Citations (Scopus)


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 reward are designed. After being trained on tracks with different levels of difficulty, our controller is capable of making the vehicle drift through various sharp corners quickly and stably in the unseen map. The proposed controller is further shown to have excellent generalization ability, which can directly handle unseen vehicle types with different physical properties, such as mass, tire friction, etc.

Original languageEnglish
Article number8961997
Pages (from-to)1247-1254
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
Publication statusPublished - Apr 2020
Externally publishedYes


  • Deep learning in robotics and automation
  • deep reinforcement learning
  • field robots
  • motion control
  • racing car

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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