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.
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Morys - Magiera, A., Lis, A., Pudlo, J., Papierok, A., Dlugosz, M., Skruch, P. (2023). Reactive Control Algorithm for F1TENTH Autonomous Vehicles in Unknown Dynamic Environments. In: Pawelczyk, M., Bismor, D., Ogonowski, S., Kacprzyk, J. (eds) Advanced, Contemporary Control. PCC 2023. Lecture Notes in Networks and Systems, vol 709. Springer, Cham. https://doi.org/10.1007/978-3-031-35173-0_22
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DOI: https://doi.org/10.1007/978-3-031-35173-0_22
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