Learning at the Racetrack: Data-Driven Methods to Improve Racing Performance Over Multiple Laps

Abstract

Autonomous vehicles will generate data from the variety of sensors they employ to track the surrounding environment. This data is inherently valuable, as it gives algorithm designers the potential to leverage prior experience in order to improve driving performance over time. This paper uses the lens of autonomous racing to provide an example of how data from previous iterations of driving can be used to improve quantitative metrics of performance. Two complementary algorithms are demonstrated in this paper. The first algorithm uses iterative learning control (ILC) to simultaneously improve lateral and longitudinal tracking of the desired racing trajectory over multiple laps, while the second algorithm is focused on altering the trajectory itself using a search method. When driven experimentally at the limits of handling, the result is a reduction in lap time of nearly 1.4 seconds, a major improvement.