Reactive Control Algorithm for F1TENTH Autonomous Vehicles in Unknown Dynamic Environments

Artur Morys-Magiera, Aleksandra Lis, Jakub Pudlo, Andrzej Papierok, Marek Dlugosz, Pawel Skruch

Abstract: Autonomous control of scaled racing cars is an increasingly popular approach for testing and presenting new control algorithms while also reducing the operational costs required to bring such a model along with a proper racing track to life. In most cases where autonomous vehicles come into play, the environment map is not known a priori, in which case the algorithm should sufficiently drive the car in real time, without having planned an optimal trajectory. This study presents a reactive method based solely on LiDAR readings that successfully navigates the car through unknown dynamic environments with obstacles, taking into account the principle of safety: not to crash into obstacles or other vehicles. We show that the algorithm significantly outperforms two other existing reactive algorithms and renders a cost-effective solution to the problem of autonomous driving on unknown maps, as well as it renders itself as a possibly good “specialist" model in Expert Intervention Learning (EIL) approach-based models for autonomous vehicles, especially the F1TENTH cars.