Learning to Drift with Individual Wheel Drive: Maneuvering Autonomous Vehicle at the Handling Limits
Yihan Zhou, Yiwen Lu, Bo Yang, Jiayun Li, Yilin Mo
AI summary
Problem
Autonomous drifting control struggles with a significant simulation-to-reality gap and high training costs, while existing research platforms lack the independent wheel actuation needed for advanced maneuvering at handling limits.
Approach
The authors train a reinforcement learning policy in a custom GPU-accelerated parallel simulator using systematic domain randomization, then deploy it on a newly designed open-source 1/10 scale Individual Wheel Drive RC car.
Key results
- Zero-shot sim-to-real transfer of drifting policies without real-world fine-tuning
- Open-sourced 1/10 scale Individual Wheel Drive RC car platform with independent torque vectoring
- GPU-accelerated simulator reducing reinforcement learning training time from hours to minutes
- Precise trajectory tracking and stable sideslip control across complex maneuvers like direction reversals and variable-curvature paths
Why it matters
Advances safe autonomous vehicle maneuvering at friction limits and provides an accessible hardware-software benchmark for agile control research.
Abstract
Drifting, characterized by controlled vehicle motion at high sideslip angles, is crucial for safely handling emergency scenarios at the friction limits. While recent reinforcement learning approaches show promise for drifting control, they struggle with the significant simulation-to-reality gap, as policies that perform well in simulation often fail when transferred to physical systems. In this letter, we present a reinforcement learning framework with GPU-accelerated parallel simulation and systematic domain ran- domization that effectively bridges the gap. The proposed approach is validated on both simulation and a custom-designed and open- sourced 1/10 scale Individual Wheel Drive (IWD) RC car platform featuring independent wheel speed control. Experiments across various scenarios from steady-state circular drifting to direction transitions and variable-curvature path following demonstrate that our approach achieves precise trajectory tracking while maintain- ing controlled sideslip angles throughout complex maneuvers in both simulated and real-world environments.