Control-Informed Constraint Adaptation in Minimum-Time Trajectory Planning for Autonomous Racing

2026-08-14

AI Summary

This study addresses the performance limitations in autonomous racing trajectory planning caused by neglecting execution errors. We propose a control-aware online planning framework that integrates real-time tracking deviations into the planning layer. By dynamically adjusting spatial constraints and iteratively expanding the planning horizon, this approach enables adaptive constraint handling and compensates for cumulative errors, effectively overcoming the bottlenecks of traditional modular architectures. High-fidelity closed-loop simulations demonstrate that the proposed framework reduces lap time by 1.8 seconds while maintaining time optimality and safety guarantees. With a median computation time of only 25 ms, this method significantly enhances the vehicle's capability to operate at the limits of track performance.

Abstract

Autonomous racecars operate at the limits of vehicle dynamics, where small control errors translate into safety-critical behavior and lost performance. Trajectory planners assume perfect tracking and remain blind to execution errors. To guarantee safety, trajectory planners therefore restrict themselves to conservative spatial margins, leaving usable track space untapped. To overcome these issues, we introduce a control-informed online trajectory planning framework that learns from its own execution errors. By measuring systematic tracking deviations during runtime, we dynamically adapt spatial track constraints and iteratively expand the free-space planning area. The planner remains time-optimal while compensating for accumulated execution errors. This method was analyzed in a high-fidelity closed-loop simulation environment with autonomous racecars. The results demonstrate that our approach reduces lap time by 1.8\,s without increasing computational burden, maintaining a median runtime of 25 ms. Our finding indicates that feeding control-induced deviations back into the planning layer unlocks performance previously inaccessible to modular architectures and enables autonomous vehicles to exploit track limits systematically.

Problem

• Autonomous Racing
• Trajectory Planning
• Tracking Error
• Minimum-Time
• Constraint Adaptation

Innovation

• Control-Informed Planning
• Constraint Adaptation
• Execution Error Feedback
• Minimum-Time Trajectory Planning
• Autonomous Racing