High-speed, head-to-head autonomous racing presents substantial technical and logistical challenges, including precise localization, rapid perception, dynamic planning, and real-time control-compounded by limited track access and costly hardware. This paper introduces the Autonomous Race Stack (ARS), developed by the IU Luddy Autonomous Racing team for the Indy Autonomous Challenge (IAC). We present three iterations of our ARS, each validated on different tracks and achieving speeds up to 260 km/h. Our contributions include: (i) the modular architecture and evolution of the ARS across ARS1, ARS2, and ARS3; (ii) a detailed performance evaluation that contrasts control, perception, and estimation across oval and road-course environments; and (iii) the release of a high-speed, multi-sensor dataset collected from oval and road-course tracks. Our findings highlight the unique challenges and insights from real-world high-speed full-scale autonomous racing.
@article{arxiv.2512.06892,
title = {From Zero to High-Speed Racing: An Autonomous Racing Stack},
author = {Hassan Jardali and Durgakant Pushp and Youwei Yu and Mahmoud Ali and Ihab S. Mohamed and Alejandro Murillo-Gonzalez and Paul D. Coen and Md. Al-Masrur Khan and Reddy Charan Pulivendula and Saeoul Park and Lingchuan Zhou and Lantao Liu},
journal= {arXiv preprint arXiv:2512.06892},
year = {2025}
}