Autonomous Drifting

Autonomous drifting research focuses on developing control algorithms that enable vehicles to execute controlled slides at high speeds, primarily for improved maneuverability and performance in challenging driving scenarios. Current efforts leverage machine learning, particularly neural networks for tire force modeling and reinforcement learning for policy optimization, often integrated with nonlinear model predictive control frameworks to achieve precise trajectory tracking. This work is significant for advancing vehicle control systems, potentially improving safety in emergency maneuvers and enhancing performance in applications like autonomous racing or advanced driver-assistance systems.