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

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

|Authors: Ann-Kathrin Schwehn, Alexander Langmann, Mattia Piccinini, Johannes Betz

|Venue: arXiv preprint | Year: 2026

|arXiv: 2608.14448v1

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.

Key Contributions

• Autonomous racecars operate at the limits of vehicle dynamics, where small control errors transla…
• Trajectory planners assume perfect tracking and remain blind to execution errors
• To guarantee safety, trajectory planners therefore restrict themselves to conservative spatial ma…
• To overcome these issues, we introduce a control-informed online trajectory planning framework th…
• By measuring systematic tracking deviations during runtime, we dynamically adapt spatial track co…

Topics

• planning
• control

Code & Data

No code repository linked in paper metadata.

BibTeX

@article{Schwehn2026_260814448v1,
  title     = {Control-Informed Constraint Adaptation in Minimum-Time Trajectory Planning for Autonomous Racing},
  author    = {Ann-Kathrin Schwehn and Alexander Langmann and Mattia Piccinini and Johannes Betz},
  year      = {2026},
  eprint    = {2608.14448v1},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url       = {https://arxiv.org/abs/2608.14448v1}
}