Race strategy optimization in Formula 1: an explainable AI approach
Sanna, Francesca Romana (A.A. 2024/2025) Race strategy optimization in Formula 1: an explainable AI approach. Tesi di Laurea in Algorithmis, Luiss Guido Carli, relatore Irene Finocchi, pp. 38. [Bachelor's Degree Thesis]
|
PDF (Full text)
Download (1MB) | Preview |
Abstract/Index
Literature review. Theoretical framework. Current state of knowledge. Research gaps. Key concepts and definitions. Methodology. Research design. System architecture and implementation. Data collection and processing. Analytical approach: modeling and explanation. The real-time explainable AI (XAI) engine. Delivery and visualization layer. Evaluation metrics. Experiments and results. Experimental setup. Machine learning model performance. Real-time XAI system performance. Qualitative use case: live race simulation. Discussion. Analysis of key findings.
References
Bibliografia: pp. 34-35.
| Thesis Type: | Bachelor's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Bachelor's Degree Programs > Bachelor's Degree Program in Management and Computer Science, English language (L-18) |
| Chair: | Algorithmis |
| Thesis Supervisor: | Finocchi, Irene |
| Academic Year: | 2024/2025 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 28 Apr 2026 08:17 |
| Last Modified: | 28 Apr 2026 08:17 |
| URI: | https://tesi.luiss.it/id/eprint/45521 |
Downloads
Downloads per month over past year
Repository Staff Only
 |
View Item |
