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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
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Erratum published on 19 August 2020, see Appl. Sci. 2020, 10(17), 5745.
Open AccessArticle

Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for 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(12), 4229; https://doi.org/10.3390/app10124229
Received: 11 May 2020 / Revised: 3 June 2020 / Accepted: 15 June 2020 / Published: 19 June 2020
(This article belongs to the Collection Computer Science in Sport)
Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers. View Full-Text
Keywords: racing; simulation; strategy; circuit; motorsport; monte carlo racing; simulation; strategy; circuit; motorsport; monte carlo
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MDPI and ACS Style

Heilmeier, A.; Graf, M.; Betz, J.; Lienkamp, M. Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport. Appl. Sci. 2020, 10, 4229. https://doi.org/10.3390/app10124229

AMA Style

Heilmeier A, Graf M, Betz J, Lienkamp M. Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport. Applied Sciences. 2020; 10(12):4229. https://doi.org/10.3390/app10124229

Chicago/Turabian Style

Heilmeier, Alexander; Graf, Michael; Betz, Johannes; Lienkamp, Markus. 2020. "Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport" Appl. Sci. 10, no. 12: 4229. https://doi.org/10.3390/app10124229

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