Manuscript received November 2, 2022; revised November 24, 2022; accepted February 20, 2023.
Abstract—This paper presents an embedded deep neural network model to predict the driver rank and the optimum pitstop strategy. In formula one racing, the race strategy is critical to determine optimal pitstops and finish the race in the best possible position. Considering a system with only one racing car, the pitstop can be decided just by looking at the degradation of the tires. But in reality, the formula one environment is more complex, and multiple probabilistic factors (like safety car phases, opponent strategy, and overtaking) influence the pitstop decision. Deep-Racing is a prediction and decision algorithm for formula one racing cars that uses neural networks with embedding layers. The algorithm is developed after carefully reviewing formula one racing and appropriate statistical modeling techniques, which can be trained for pre-race and real-time predictions during the race using the data from previous laps. Deep-Racing has the potential to help team principals and race engineers to decide the optimized strategy for making pitstops. It is trained on the data from seasons 2015- 2022. This project is the first to utilize an embedded layer in motorsport racing predictions, and the results show an improvement in predictive accuracy compared with the previously available literature. This paper significantly expands the previous research in this field and proposes trends in the data available from the latest seasons.
Index Terms—Neural networks applications, formula one racing, embedded deep neural networks, predictive analysis
Syeda Sitara Wishal Fatima and Jennifer Johrendt are with the Department of Mechanical, Automotive and Materials Engineering, University of Windsor, Canada.
*Correspondence: fatima92@uwindsor.ca (S.S.W.F.)
Cite: Syeda Sitara Wishal Fatima and Jennifer Johrendt, "Deep-Racing: An Embedded Deep Neural Network (EDNN) Model to Predict the Winning Strategy in Formula One Racing," International Journal of Machine Learning vol. 13, no. 3, pp. 97-103, 2023.
Copyright @ 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).