# High-Speed Cornering Control and Real-Vehicle Deployment for Autonomous Electric Vehicles

arXiv: https://arxiv.org/abs/2411.11762 · HTML: https://mlsanity.com/paper/2411.11762/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Placed in

- 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, 3 of 3)

- [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)
- [Cooperative Automated Vehicles: a Review of Opportunities and Challenges in Socially Intelligent Vehicles Beyond Networking](https://mlsanity.com/paper/1710.00461/) (2017)

## Built on by (most cited first, 2 of 2)

- [Dynamics-Decoupled Trajectory Alignment for Sim-to-Real Transfer in Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2511.07155/) (2025)
- [Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example](https://mlsanity.com/paper/2506.22894/) (2025)

## Abstract (arXiv)

Executing drift maneuvers during high-speed cornering presents significant challenges for autonomous vehicles, yet offers the potential to minimize turning time and enhance driving dynamics. While reinforcement learning (RL) has shown promising results in simulated environments, discrepancies between simulations and real-world conditions have limited its practical deployment. This study introduces an innovative control framework that integrates trajectory optimization with drift maneuvers, aiming to improve the algorithm's adaptability for real-vehicle implementation. We leveraged Bezier-based pre-trajectory optimization to enhance rewards and optimize the controller through Twin Delayed Deep Deterministic Policy Gradient (TD3) in a simulated environment. For real-world deployment, we implement a hybrid RL-MPC fusion mechanism, , where TD3-derived maneuvers serve as primary inputs for a Model Predictive Controller (MPC). This integration enables precise real-time tracking of the optimal trajectory, with MPC providing corrective inputs to bridge the gap between simulation and reality. The efficacy of this method is validated through real-vehicle tests on consumer-grade electric vehicles, focusing on drift U-turns and drift right-angle turns. The control outcomes of these real-vehicle tests are thoroughly documented in the paper, supported by supplementary video evidence (https://youtu.be/5wp67FcpfL8). Notably, this study is the first to deploy and apply an RL-based transient drift cornering algorithm on consumer-grade electric vehicles.