ForzaETH Race Stack - Scaled Autonomous Head-to-Head Racing on Fully Commercial Off-the-Shelf Hardware
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Date
2025-06
Publication Type
Journal Article
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yes
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Abstract
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, which is exemplified by the various modularly implemented state estimation and control systems. 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. Furthermore, the stack demonstrated its reliability and performance at unprecedented scales, up to over 10m s-1 $10\,{\text{m s}}<^>{-1}$ on tracks up to 150 m in length.
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Publication status
published
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Journal / series
Volume
42 (4)
Pages / Article No.
1037 - 1079
Publisher
Wiley
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Edition / version
Methods
Software
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Date collected
Date created
Subject
autonomous driving; autonomous racing; motion control; open source software; path planning; robotic perception; state estimation
Organisational unit
03996 - Benini, Luca / Benini, Luca
09563 - Zeilinger, Melanie / Zeilinger, Melanie
01225 - D-ITET Zentr. f. projektbasiertes Lernen / D-ITET Center for Project-Based Learning