Artificial Intelligence in Motorsport: A Scoping Review of Applications, Challenges, and Research Gaps

This study presents a state-of-the-art review of Artificial Intelligence and Machine Learning applications in motorsport, with a particular focus on Formula 1. As modern racing generates increasingly large volumes of high-frequency telemetry data, traditional physics-based analysis methods have proven insufficient to capture the complex, non-linear relation-ships between vehicle, driver, and environment. AI has emerged as a critical tool for extracting actionable insights from this data across six core domains: vehicle performance optimization, race strategy, autonomous racing and real-time decision support, telemetry and race data analysis, driver coaching and simulation, and predictive maintenance. This review further examines transferable methodologies from adjacent fields — particularly autonomous driving research — and assesses their relevance to high-performance motorsport contexts. A systematic analysis of peer-reviewed publications and preprints reveals a highly uneven distribution of research, with race strategy and vehicle optimization relatively well-covered, while predictive maintenance and computer vision applications remain largely unexplored.

@article{Lucaciu2026,
  title     = {Artificial Intelligence in Motorsport: A Scoping Review of Applications, Challenges, and Research Gaps},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {5},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Radu C. Lucaciu and Cornelia Aurora Gyorödi},
  doi       = {10.14569/IJACSA.2026.01705101},
  url       = {https://doi.org/10.14569/IJACSA.2026.01705101}
}