Disturbance-aware minimum-time planning strategies for motorsport vehicles with probabilistic safety certificates

TL;DR

This paper introduces disturbance-aware planning methods for motorsport vehicles that incorporate probabilistic safety certificates, improving trajectory robustness and lap-time performance under uncertainty.

Contribution

It proposes two novel covariance-based planning formulations, one open-loop and one closed-loop, that enhance safety and efficiency in minimum-time racing trajectories.

Findings

Closed-loop approach reduces lap-time penalties compared to open-loop.

Both methods ensure safety probability under uncertainty.

Nominal trajectories become infeasible when uncertainties are considered.

Abstract

This paper presents a disturbance-aware framework that embeds robustness into minimum-lap-time trajectory optimization for motorsport. Two formulations are introduced. (i) Open-loop, horizon-based covariance propagation uses worst-case uncertainty growth over a finite window to tighten tire-friction and track-limit constraints. (ii) Closed-loop, covariance-aware planning incorporates a time-varying LQR feedback law in the optimizer, providing a feedback-consistent estimate of disturbance attenuation and enabling sharper yet reliable constraint tightening. Both methods yield reference trajectories for human or artificial drivers: in autonomous applications the modelled controller can replicate the on-board implementation, while for human driving accuracy increases with the extent to which the driver can be approximated by the assumed time-varying LQR policy. Computational tests on a representative Barcelona-Catalunya sector show that both schemes meet the prescribed safety probability, yet the closed-loop variant incurs smaller lap-time penalties than the more conservative open-loop solution, while the nominal (non-robust) trajectory remains infeasible under the same uncertainties. By accounting for uncertainty growth and feedback action during planning, the proposed framework delivers trajectories that are both performance-optimal and probabilistically safe, advancing minimum-time optimization toward real-world deployment in high-performance motorsport and autonomous racing.