ForzaETH Race Stack by the D-ITET Center for Project Based Learning (PBL) at ETH Zurich.
Accompanying this repository, a paper titled ForzaETH Race Stack - Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardware is available on arXiv, detailing the system's architecture, algorithms, and performance benchmarks.
We provide an installation guide here.
After installation, the car (or the simulation environment) is ready to be tested. For examples on how to run the different modules on the car, refer to the stack_master README. As a further example, the time-trials or the head-to-head checklists are a good starting point.
In case you find our package helpful and want to contribute, please either raise an issue or directly make a pull request. To create pull request please follow the guidelines in CONTRIBUTING.
This project would not be possible without the use of multiple great open-sourced code bases as listed below:
- f1tenth_system
- F1TENTH Racecar Simulator
- Veddar VESC Interface
- Cartographer
- Cartographer ROS Integration
- global_racetrajectory_optimization
- RangeLibc
- BayesOpt4ROS
- cpu_monitor
If you found our race stack helpful in your research, we would appreciate if you cite it as follows:
@misc{baumann2024forzaeth,
title={ForzaETH Race Stack - Scaled Autonomous Head-to-Head Racing on Fully Commercial off-the-Shelf Hardware},
author={Nicolas Baumann and Edoardo Ghignone and Jonas Kühne and Niklas Bastuck and Jonathan Becker and Nadine Imholz and Tobias Kränzlin and Tian Yi Lim and Michael Lötscher and Luca Schwarzenbach and Luca Tognoni and Christian Vogt and Andrea Carron and Michele Magno},
year={2024},
eprint={2403.11784}
}