Riassunto analitico
In the dynamic world of racing, offline simulations stand as a critical tool for predicting and evaluating car behaviour. These simulations play a pivotal role in race preparation, guiding setup decisions by showcasing the car's response to various configuration options. Additionally, simulations are instrumental in the development of new vehicle elements, offering a means to assess their impact before production.
This thesis delves into the crucial role of offline simulations verifying the correlation between offline simulations and real-life performance. Two primary sources of telemetry data, track data and driver-in-the-loop simulator data, are examined. The former, while diverse, is constrained by limited on-track time, subject to varying conditions and data quality. The latter, a controlled environment, provides more consistent and reliable data, facilitating a direct comparison with simulation outputs.
The thesis focuses on improving a specific aspect of offline simulations—the constraining of the simulations to represent the gap between car potential and human factor. By refining this aspect, the aim is to enhance the accuracy of lap-time optimization, accounting for the nuanced driving behaviour of professional drivers.
In conclusion, this thesis seeks to refine the constraining model within offline simulations to better align with real-world driver behaviour. By doing so, it aims to contribute to the ongoing efforts to enhance the accuracy and reliability of high-performance car simulations, ultimately providing the team with a more effective tool for race preparation and vehicle development.
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