Fabio Vito Meier

Julian Nubert, Niklas Funk, Fabio Meier et al.

Our solution is implemented in and for the frame of Duckietown. The goal of Duckietown is to provide a relatively simple platform to explore, tackle and solve many problems linked to autonomous driving. "Duckietown" is simple in the basics, but an infinitely expandable environment. From controlling single driving Duckiebots until complete fleet management, every scenario is possible and can be put into practice. So far, none of the existing modules was capable of reliably detecting obstacles and reacting to them in real time. We faced the general problem of detecting obstacles given images from a monocular RGB camera mounted at the front of our Duckiebot and reacting to them properly without crashing or erroneously stopping the Duckiebot. Both, the detection as well as the reaction have to be implemented and have to run on a Raspberry Pi in real time. Due to the strong hardware limitations, we decided to not use any learning algorithms for the obstacle detection part. As it later transpired, a working "hard coded" software needs thorough analysis and understanding of the given problem. In layman's terms, we simply seek to make Duckietown a safer place.

Miguel de la Iglesia Valls, Hubertus Franciscus Cornelis Hendrikx, Victor Reijgwart et al.

This paper introduces flüela driverless: the first autonomous racecar to win a Formula Student Driverless competition. In this competition, among other challenges, an autonomous racecar is tasked to complete 10 laps of a previously unknown racetrack as fast as possible and using only onboard sensing and computing. The key components of flüela's design are its modular redundant sub-systems that allow robust performance despite challenging perceptual conditions or partial system failures. The paper presents the integration of key components of our autonomous racecar, i.e., system design, EKF-based state estimation, LiDAR-based perception, and particle filter-based SLAM. We perform an extensive experimental evaluation on real-world data, demonstrating the system's effectiveness by outperforming the next-best ranking team by almost half the time required to finish a lap. The autonomous racecar reaches lateral and longitudinal accelerations comparable to those achieved by experienced human drivers.