RaceEngineer AI
From Sim2Real in motorsport racing
Challenge
The RaceEngineerAI project is an innovative response to the needs of lower-level motor racing teams, which face significant challenges due to limited financial and technological resources. In a landscape where motorsport generates huge economic impacts, the big teams enjoy millionaire budgets, while the smaller teams operate on much tighter margins, limiting their access to cutting-edge technologies.
Solution
This project aims to respond to this challenge by researching and developing a solution that takes advantage of the data generated in racing simulators (SimRacing), combining it with data from real races and Artificial Intelligence and simulation techniques to optimise driver performance in real racing scenarios.
RaceEngineerAI works as a virtual assistant for race engineers, enabling real-time analysis and offering strategic recommendations during competitions. This tool is especially aimed at teams in championships such as GT3, GT4, LMP2, LMP3, F3 and F4, where the difference between winning or losing can lie in the ability to make quick and informed strategic decisions.
Finally, the project also aims to democratise access to the professional racing profession. By enabling small teams to carry out detailed driver profiling using SimRacing data, it is easier to identify talents with the potential to move from the virtual to the real, even in contexts with limited resources.
Objectives, Activities and Results expected / achieved
Specific expected objectives:
O1 - Increase the competitiveness of lower-level racing teams through a system that optimises the work of their race engineers;
O2 - Research and develop profiling mechanisms to identify virtual drivers with greater potential in a real racing context;
O3 - Research and develop simulation and forecasting mechanisms to optimise racing strategies;
O4 - Develop an assistant specialising in racing and automotive engineering.
Project Reference
CENTRO2030-FEDER-01494400Funding
Intervention Region
PortugalTotal Investment
880.416,80IPN Investment
375.168,80Total Eligible
880.416,80IPN Eligible
375.168,80EC Funding – Total
679.848,92EC Funding – IPN
318.893,48Duration
36 MonthsStart Date
2025-01-01End Date
2027-12-31Approval Date
2025-01-27Consortium
Quicktrend Unipessoal, LdaInstituto Pedro Nunes
Keywords
Artificial Inteligence;Big Data;
LLMs.
