Széchenyi Plusz RRF

This paper introduces the winning algorithm of the 2024 Shell Eco-Marathon Autonomous Urban Challenge for autonomous parking. The task requires the vehicle to identify an available parking spot from multiple alternatives and precisely navigate into it, fully remaining within the designated area without touching any lane markings. Successful task execution requires not only reliable long-range detection of the parking space but also an accurate final orientation relative to the parking spot. To solve this task, we propose a novel method which relies on the combination neural networks and traditional point cloud processing methods. Since this is a highly specific problem tailored to the Shell Eco-Marathon setting, and no publicly available solutions from other teams have been observed, our earlier algorithm serves as the primary baseline for comparison.