# Self driving algorithm for an active four wheel drive racecar

arXiv: https://arxiv.org/abs/2506.06077 · HTML: https://mlsanity.com/paper/2506.06077/

## 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 17 of its 41 references that are in the map.
- First posted on arXiv 6 Jun 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, 17 of 17)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Emergence of Locomotion Behaviours in Rich Environments](https://mlsanity.com/paper/1707.02286/) (2017)
- [Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning](https://mlsanity.com/paper/2008.07971/) (2020)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning](https://mlsanity.com/paper/1909.08399/) (2019)
- [Contextual Tuning of Model Predictive Control for Autonomous Racing](https://mlsanity.com/paper/2110.02710/) (2021)
- [Two-timescale Mechanism-and-Data-Driven Control for Aggressive Driving of Autonomous Cars](https://mlsanity.com/paper/2109.05170/) (2021)
- [Physics-Guided Reinforcement Learning System for Realistic Vehicle Active Suspension Control](https://mlsanity.com/paper/2408.08425/) (2024)
- [AARK: An Open Toolkit for Autonomous Racing Research](https://mlsanity.com/paper/2410.00358/) (2024)
- [Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning](https://mlsanity.com/paper/2412.03803/) (2024)
- [Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning](https://mlsanity.com/paper/2308.13491/) (2023)
- [Control Informed Design of the IAC Autonomous Racecar for Operation at the Dynamic Envelope](https://mlsanity.com/paper/2407.17737/) (2024)
- [On learning racing policies with reinforcement learning](https://mlsanity.com/paper/2504.02420/) (2025)
- [Vision based driving agent for race car simulation environments](https://mlsanity.com/paper/2504.10266/) (2025)

## Abstract (arXiv)

Controlling autonomous vehicles at their handling limits is a significant challenge, particularly for electric vehicles with active four wheel drive (A4WD) systems offering independent wheel torque control. While traditional Vehicle Dynamics Control (VDC) methods use complex physics-based models, this study explores Deep Reinforcement Learning (DRL) to develop a unified, high-performance controller. We employ the Proximal Policy Optimization (PPO) algorithm to train an agent for optimal lap times in a simulated racecar (TORCS) at the tire grip limit. Critically, the agent learns an end-to-end policy that directly maps vehicle states, like velocities, accelerations, and yaw rate, to a steering angle command and independent torque commands for each of the four wheels. This formulation bypasses conventional pedal inputs and explicit torque vectoring algorithms, allowing the agent to implicitly learn the A4WD control logic needed for maximizing performance and stability. Simulation results demonstrate the RL agent learns sophisticated strategies, dynamically optimizing wheel torque distribution corner-by-corner to enhance handling and mitigate the vehicle's inherent understeer. The learned behaviors mimic and, in aspects of grip utilization, potentially surpass traditional physics-based A4WD controllers while achieving competitive lap times. This research underscores DRL's potential to create adaptive control systems for complex vehicle dynamics, suggesting RL is a potent alternative for advancing autonomous driving in demanding, grip-limited scenarios for racing and road safety.