Pushing the Performance Limits in Autonomous Racing: Continuous Stability-Aware Adaptive Velocity Planning in Formula Student Driverless
This work addresses the real-time velocity planning challenge for autonomous race cars, improving lap times and stability in competitive racing scenarios.
The paper presents a novel velocity planning approach for autonomous racing that adapts to changing conditions by inferring a continuous scaling factor from vehicle stability, achieving a 35% lap time improvement over ten laps and an average 8% increase compared to a non-adaptive method on a real Formula Student race car.
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.