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Erratum: Heilmeier, A., et al. Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport. Appl. Sci. 2020, 10, 4229
Article

Virtual Strategy Engineer: Using Artificial Neural Networks for Making Race Strategy Decisions in Circuit Motorsport

1
Institute of Automotive Technology, Technical University of Munich, 85748 Garching, Germany
2
BMW Motorsport, 80939 Munich, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(21), 7805; https://doi.org/10.3390/app10217805
Received: 25 September 2020 / Revised: 29 October 2020 / Accepted: 31 October 2020 / Published: 4 November 2020
(This article belongs to the Collection Computer Science in Sport)
In circuit motorsport, race strategy helps to finish the race in the best possible position by optimally determining the pit stops. Depending on the racing series, pit stops are needed to replace worn-out tires, refuel the car, change drivers, or repair the car. Assuming a race without opponents and considering only tire degradation, the optimal race strategy can be determined by solving a quadratic optimization problem, as shown in the paper. In high-class motorsport, however, this simplified approach is not sufficient. There, comprehensive race simulations are used to evaluate the outcome of different strategic options. The published race simulations require the user to specify the expected strategies of all race participants manually. In such simulations, it is therefore desirable to automate the strategy decisions, for better handling and greater realism. It is against this background that we present a virtual strategy engineer (VSE) based on two artificial neural networks. Since our research is focused on the Formula 1 racing series, the VSE decides whether a driver should make a pit stop and which tire compound to fit. Its training is based on timing data of the six seasons from 2014 to 2019. The results show that the VSE makes reasonable decisions and reacts to the particular race situation. The integration of the VSE into a race simulation is presented, and the effects are analyzed in an example race. View Full-Text
Keywords: race; simulation; strategy; motorsport; machine learning; neural network; decision making race; simulation; strategy; motorsport; machine learning; neural network; decision making
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MDPI and ACS Style

Heilmeier, A.; Thomaser, A.; Graf, M.; Betz, J. Virtual Strategy Engineer: Using Artificial Neural Networks for Making Race Strategy Decisions in Circuit Motorsport. Appl. Sci. 2020, 10, 7805. https://doi.org/10.3390/app10217805

AMA Style

Heilmeier A, Thomaser A, Graf M, Betz J. Virtual Strategy Engineer: Using Artificial Neural Networks for Making Race Strategy Decisions in Circuit Motorsport. Applied Sciences. 2020; 10(21):7805. https://doi.org/10.3390/app10217805

Chicago/Turabian Style

Heilmeier, Alexander, André Thomaser, Michael Graf, and Johannes Betz. 2020. "Virtual Strategy Engineer: Using Artificial Neural Networks for Making Race Strategy Decisions in Circuit Motorsport" Applied Sciences 10, no. 21: 7805. https://doi.org/10.3390/app10217805

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