F1TENTH: Enhancing Autonomous Systems Education Through Hands-On Learning and Competition

Johannes Betz, Hongrui Zheng, Felix Jahncke, Zirui Zang, Florian Sauerbeck, Yahong Rosa Zheng, Joydeep Biswas, Venkat Krovi, Rahul Mangharam

Abstract: Teaching autonomous and intelligent transportation systems in higher education has traditionally focused on theory, often lacking comprehensive coverage of the practical techniques required for real-world applications. To overcome this, we developed a new university course centered around hands-on learning with a modular autonomous small-scale vehicle platform called F1TENTH. This paper presents a detailed overview of the new course design, its underlying philosophy, the individual teaching modules, and the modular hardware/software of the F1TENTH platform. This new course was then evaluated with a survey conducted at five universities that have adopted the teaching modules for their semester-long undergraduate and graduate courses. The results show that approximately 80% of all involved students strongly agree that the hardware platform and modules significantly increased their motivation to learn. More than 70% of the students agreed that the hardware enhanced their understanding of the material The findings demonstrate that our course setup and the F1TENTH hardware effectively combine theoretical knowledge with practical application, greatly enhancing the educational outcomes and the students' computational thinking skills. Future research is needed to explore the long-term impact of hands-on learning on students' career development in intelligent autonomous systems.

External IDs:dblp:journals/tiv/BetzZJZSZBKM25