ForzaETH Race Stack -- Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardware
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Abstract (arXiv)
Autonomous racing in robotics combines high-speed dynamics with the necessity for reliability and real-time decision-making. While such racing pushes software and hardware to their limits, many existing full-system solutions necessitate complex, custom hardware and software, and usually focus on Time-Trials rather than full unrestricted Head-to-Head racing, due to financial and safety constraints. This limits their reproducibility, making advancements and replication feasible mostly for well-resourced laboratories with comprehensive expertise in mechanical, electrical, and robotics fields. Researchers interested in the autonomy domain but with only partial experience in one of these fields, need to spend significant time with familiarization and integration. The ForzaETH Race Stack addresses this gap by providing an autonomous racing software platform designed for F1TENTH, a 1:10 scaled Head-to-Head autonomous racing competition, which simplifies replication by using commercial off-the-shelf hardware. This approach enhances the competitive aspect of autonomous racing and provides an accessible platform for research and development in the field. The ForzaETH Race Stack is designed with modularity and operational ease of use in mind, allowing customization and adaptability to various environmental conditions, such as track friction and layout. Capable of handling both Time-Trials and Head-to-Head racing, the stack has demonstrated its effectiveness, robustness, and adaptability in the field by winning the official F1TENTH international competition multiple times.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Mar 2024.
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Citing papers per year: 2025: 4; 2026: 2.
6 papers.
- 2025Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Modelscited by 11
- 2025RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platformscited by 2
- 2026Efficient Real-World Autonomous Racing via Attenuated Residual Policy Optimizationcited by 1
- 2026Before Parc Ferm'e: RL-Time Pruning for Efficient Embodied LLMs in Autonomous Drivingcited by 1
- 2025Planar Velocity Estimation for Fast-Moving Mobile Robots Using Event-Based Optical Flowcited by 0
- 2025Accelerating Real-World Overtaking in F1TENTH Racing Employing Reinforcement Learning Methodscited by 0
- 2026Before Parc Ferm'e: RL-Time Pruning for Efficient Embodied LLMs in Autonomous Driving29 May 2026
- 2026Efficient Real-World Autonomous Racing via Attenuated Residual Policy Optimization13 Mar 2026
- 2025Accelerating Real-World Overtaking in F1TENTH Racing Employing Reinforcement Learning Methods30 Oct 2025
- 2025Planar Velocity Estimation for Fast-Moving Mobile Robots Using Event-Based Optical Flow16 May 2025
- 2025Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Models15 Apr 2025
- 2025RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms28 Jan 2025