Autonomous vehicle racing is facing a growing interest both in industrial and academic settings spanning multiple disciplines. In this paper, we will explore how to create a robust, efficient, and reliable decision-making mechanism to decide, at every point in time, the trajectories that a vehicle should take to overtake its opponents and win the race. The proposed framework combines a graph-based path planner with a game-theoretic model to generate powerful racing strategies. We implemented the framework, and we carried out an experimental evaluation to show its effectiveness and evaluate the impact of the different parameters.
When graphs meet game theory: A scalable approach for robotic car racing / Tikna, A.; Roveri, M.; Fontanelli, D.; Palopoli, L.. - 2023-:(2023), pp. 1-8. ( 47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023 ita 2023) [10.1109/COMPSAC57700.2023.00011].
When graphs meet game theory: A scalable approach for robotic car racing
Tikna A.
;Roveri M.
;Fontanelli D.
;Palopoli L.
2023-01-01
Abstract
Autonomous vehicle racing is facing a growing interest both in industrial and academic settings spanning multiple disciplines. In this paper, we will explore how to create a robust, efficient, and reliable decision-making mechanism to decide, at every point in time, the trajectories that a vehicle should take to overtake its opponents and win the race. The proposed framework combines a graph-based path planner with a game-theoretic model to generate powerful racing strategies. We implemented the framework, and we carried out an experimental evaluation to show its effectiveness and evaluate the impact of the different parameters.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
