## 1 Introduction
### Research Goal and Scope  
As stated above, the user currently has to insert the pit stop information for all drivers into the RS. This applies to all published race simulations that are known to the authors. This is not only time-consuming, but also requires the user to make a valid estimate of the opponent driver's race strategies. Besides, user-defined strategies are fixed for the simulated race and can therefore not take advantage of tactical opportunities arising from random events during a race. For example,as described above, it could be beneficial to make a pit stop under FCY conditions. To overcome these limitations, we aim to automate strategy determination by means of software. Classic optimization methods are hardly applicable in this use case. Firstly, the RS would serve as an objective function for the optimization algorithm. However, the non-linear and discontinuous RS already eliminates many optimization algorithms. Secondly, this variant would again suffer from the problem that the strategy could not be adapted to suit the race situation during the simulated race, as was already the case with the user input. Thirdly, it would not be possible to optimize each driver's strategy simultaneously, from a selfish perspective. However, this is what happens in reality. Fourthly, the computation time would be high, due to the many RS evaluations required to find the optimum. This is all the more so when one considers that each strategy variant would again require many runs due to the probabilistic effects that are evaluated by Monte Carlo simulation. The use of stochastic models, e.g., a Markov chain, would be another possibility for the automation of strategy decisions. However, the fact that they are based on transition probabilities has several disadvantages compared to other methods. Firstly, even in situations where a pit stop does not seem reasonable based on the available timing data, stochastic models have a chance to decide in favor of a pit stop because there are few such cases in the training data. An example would be if the tires were replaced due to a defect and not due to wear, e.g., after a collision with another car. Other methods learn the underlying mechanisms, which is why rare cases have much less influence on the decision behavior. Secondly, there is no repeatability in the decisions of stochastic models, which makes no sense if we assume that the race situation reasons the decisions. This also makes it difficult to understand and verify the decision behavior of the model. Thirdly, the available real-world training data is very limited. Therefore, the transition probabilities for rare situations cannot be realistically determined given the enormous number of possible race situations. It is these aspects that gave us the idea of developing a virtual strategy engineer (VSE) based on machine-learning (ML) methods. These allow us to model relationships between inputs (e.g., tire age) and output (e.g., pit stop decision) that are otherwise hard to determine. The idea is to call the VSE of a driver once per lap to take the relevant strategy decisions (whether or not the driver should make a pit stop, which tires are to be fitted, and whether the car is to be refueled) based on the current race situation, tire age, etc. This concept has several more advantages. Firstly, the VSE could be used for any number of drivers while still behaving selfishly for the corresponding driver. Secondly, most ML methods are computationally cheap during inferencing. Thirdly, the VSE could be used not only in simulation, but also to support time-critical strategy decisions in real-world races without a simulation, e.g., when an FCY phase suddenly occurs. Our simulation environment and the idea for the VSE can be applied to various types of circuit-racing series. For several reasons, however, our research focuses on the FIA Formula 1 World Championship (F1).