Outracing a National Level Model Racing Car Champion: A Hybrid Model‐Based Data‐Driven Approach

ABSTRACT

This paper discusses lap time optimization, focusing on a single lap without considering opponents in autonomous racing. The paper presents a control and optimization architecture composed of a model‐based low level controller and a higher level iterative learning algorithm with the goal of obtaining the fastest qualifying lap in autonomous racing competitions. First principles models are extremely expensive to calibrate near the handling limit, to solve this issue our algorithm learns the position varying acceleration limits of the vehicle over multiple laps. The proposed algorithm brings together the robustness and generalization capability of model‐based approaches with the performance of data‐driven methods. To validate the approach and its computational efficiency, we implement the solution on a high performance small scale vehicle and test it against a human driver on a racing track with speed up to 50 km/h and lateral accelerations of 1.2 g. The proposed approach beats a national level champion in terms of qualifying lap for small scale vehicles, on the considered test track.