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.