mlsanityData to 14 Oct 2026

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

arXiv 2411.11762 ↗First posted 18 Nov 2024cs.RO cs.SY eess.SYcited by 2

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

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.

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.

Builds on

3 of 34 references.

References · 3

  1. 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 64
  2. 2020High-speed Autonomous Drifting with Deep Reinforcement Learningcited by 24
  3. 2017Cooperative Automated Vehicles: a Review of Opportunities and Challenges in Socially Intelligent Vehicles Beyond Networkingcited by 6

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Dynamics-Decoupled Trajectory Alignment for Sim-to-Real Transfer in Reinforcement Learning for Autonomous Drivingcited by 1
  2. 2025Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Examplecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Learning to Drift with Individual Wheel Drive: Maneuvering Autonomous Vehicle at the Handling Limits
  2. 2022Reinforcement Learning from Simulation to Real World Autonomous Driving using Digital Twin
  3. 2026Energy-Aware Path Following: Comparative Analysis of Reinforcement Learning and NMPC for Electric Vehicles
  4. 2026Track-Guided Hierarchical Reinforcement Learning for Autonomous Vehicle Drifting with Minimum-Lap-Time Planning
  5. 2024Reference-Free Formula Drift with Reinforcement Learning: From Driving Data to Tire Energy-Inspired, Real-World Policies
  6. 2025Self driving algorithm for an active four wheel drive racecar
  7. 2026Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning
  8. 2024Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data