ForzaETH – Autonomous Racing

ForzaETH is a flagship project in autonomous racing, offering students at the Bachelor, Semester, and Master thesis levels the opportunity to develop and implement cutting-edge robotic algorithms in a real-world racing environment. Participants work hands-on with physical robotic systems, pushing the limits of embedded systems, sensor technologies, perception, planning, and control to optimize race performance.

Beyond its educational value, ForzaETH serves as a powerful research platform.
The 1:10 scale of the vehicles is large enough to support onboard sensing and computation, enabling real-time decision-making and autonomy, while remaining small enough to be cost-effective and safe, allowing for extensive experimentation. By pushing the cars to their limits and exploring high-speed multi-agent robot interactions, students engage in cutting-edge research in robotics, control theory, and AI-driven autonomy.

Due to the excitement and engagement of past participants, students took the initiative to establish a student association, external page ForzaETH, where they now run and organize races to further develop and showcase their skills.

Additionally, we offer the Autonomous Robots and Cars P&S course, providing D-ITET Bachelor Students with a hands-on introduction to autonomous driving. This course serves as a gateway into ROS and robotics, equipping students with fundamental skills in perception, planning, and control while preparing them for more advanced projects such as the ForzaETH flagship project.
Join us in shaping the future of autonomous racing!

For inquiries: Edoardo Ghignone

Publications:

Wiley Journal of Field Robotics (JFR) 2024: external page ForzaETH Race Stack - Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardware
IEEE Robotics and Automation Letters (RA-L) 2024: external page Predictive Spliner: Data-Driven Overtaking in Autonomous Racing Using Opponent Trajectory Prediction
IEEE Robotics and Automation Letters (RA-L) 2025: external page Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute
IEEE International Conference on Robotics and Automation (ICRA) 2023: external page Model- and Acceleration-based Pursuit Controller for High-Performance Autonomous Racing