Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
Journal:
arXiv
Published Date:
Jun 28, 2025
Abstract
Autonomous drifting is a complex and crucial maneuver for safety-critical
scenarios like slippery roads and emergency collision avoidance, requiring
precise motion planning and control. Traditional motion planning methods often
struggle with the high instability and unpredictability of drifting,
particularly when operating at high speeds. Recent learning-based approaches
have attempted to tackle this issue but often rely on expert knowledge or have
limited exploration capabilities. Additionally, they do not effectively address
safety concerns during learning and deployment. To overcome these limitations,
we propose a novel Safe Reinforcement Learning (RL)-based motion planner for
autonomous drifting. Our approach integrates an RL agent with model-based drift
dynamics to determine desired drift motion states, while incorporating a
Predictive Safety Filter (PSF) that adjusts the agent's actions online to
prevent unsafe states. This ensures safe and efficient learning, and stable
drift operation. We validate the effectiveness of our method through
simulations on a Matlab-Carsim platform, demonstrating significant improvements
in drift performance, reduced tracking errors, and computational efficiency
compared to traditional methods. This strategy promises to extend the
capabilities of autonomous vehicles in safety-critical maneuvers.
