# Learning to Drift with Individual Wheel Drive: Maneuvering Autonomous Vehicle at the Handling Limits

arXiv: https://arxiv.org/abs/2507.23339 · HTML: https://mlsanity.com/paper/2507.23339/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)
- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [High-speed Autonomous Drifting with Deep Reinforcement Learning](https://mlsanity.com/paper/2001.01377/) (2020)
- [MuSHR: A Low-Cost, Open-Source Robotic Racecar for Education and Research](https://mlsanity.com/paper/1908.08031/) (2019)
- [F1/10: An Open-Source Autonomous Cyber-Physical Platform](https://mlsanity.com/paper/1901.08567/) (2019)
- [Autonomous Drifting with 3 Minutes of Data via Learned Tire Models](https://mlsanity.com/paper/2306.06330/) (2023)
- [A Hierarchical Control Framework for Drift Maneuvering of Autonomous Vehicles](https://mlsanity.com/paper/2109.06730/) (2021)
- [Consecutive Inertia Drift of Autonomous RC Car via Primitive-based Planning and Data-driven Control](https://mlsanity.com/paper/2306.12604/) (2023)
- [A Nonlinear Model Predictive Control for Automated Drifting with a Standard Passenger Vehicle](https://mlsanity.com/paper/2405.10859/) (2024)

## Abstract (arXiv)

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 paper, we present a reinforcement learning framework with GPU-accelerated parallel simulation and systematic domain randomization 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 maintaining controlled sideslip angles throughout complex maneuvers in both simulated and real-world environments.