Pushing the Performance Limits in Autonomous Racing: Continuous Stability-Aware Adaptive Velocity Planning in Formula Student Driverless

2026-06-09

AI Summary

This work addresses the significant challenge of achieving stable and efficient velocity planning in high-speed autonomous Formula racing, where real-time adaptation to track conditions, environmental disturbances, and control inaccuracies is critical. The authors propose a continuous stability-aware adaptive speed planning method that avoids explicit estimation of tire–road friction coefficients. Instead, it implicitly models the aggregate effect of disturbances through vehicle stability feedback and infers a continuously varying scaling factor to construct a dynamic friction map, enabling real-time generation of optimal target velocities. Experimental validation on a physical vehicle demonstrates that the proposed approach improves average lap time by 35% over ten consecutive laps compared to a non-adaptive baseline, with an average gain of 8%, substantially enhancing system performance and robustness in complex, dynamic environments.

Abstract

In autonomous racing, especially in competitions such as Formula Student Driverless, precise planning of the target velocity of a race car is crucial for competitive lap times and stable driving behavior. Especially at high speeds, Velocity Planning (VP) is a significant challenge as it has to be performed in real time, taking into account track layouts, environmental influences, mechanical tolerances, and the resulting control inaccuracies. In this paper, we present a novel approach to VP that dynamically adapts to such changing conditions. Instead of estimating the physical Tire-Road Friction Coefficient (TRFC), a continuous scaling factor is inferred indirectly from vehicle stability. This factor not only reflects the effective tire-road interaction but also captures effects of control inaccuracies. From this, we generate a continuous friction map, which serves as a robust, adaptive basis for computing the optimal target speed, accounting for both vehicle and environmental limits. Our proposed approach was evaluated on a real Formula Student race car, showing a lap time improvement of 35 % over ten laps and an average increase of 8 % compared to a non-adaptive approach.

Problem

• Velocity Planning
• Autonomous Racing
• Vehicle Stability
• Tire-Road Friction
• Real-time Adaptation

Innovation

• adaptive velocity planning
• vehicle stability
• continuous friction map
• autonomous racing
• real-time optimization