一级方程式比赛策略中轮胎能量的可解释时间序列预测
Explainable Time Series Prediction of Tyre Energy in Formula One Race Strategy
AI总结：
本文基于 Mercedes-AMG PETRONAS F1 车队历史遥测数据，用深度学习和 XGBoost 预测比赛中的轮胎能量，并结合特征重要性与反事实解释提升可解释性，以辅助进站和轮胎策略优化。
摘要：
一级方程式（F1）比赛策略发生在高压且快节奏的环境中，瞬间决策可能显著影响比赛结果。比赛策略的两个核心决策是何时进站（即更换赛车轮胎）以及选择哪种轮胎配方（正常情况下为硬胎、中性胎或软胎）。最佳进站决策可以通过估算这些配方的轮胎退化来确定，而轮胎退化又可根据施加于每个轮胎的能量（即轮胎能量）计算得出。在本研究中，我们利用 Mercedes-AMG PETRONAS F1 车队由遥测数据构成的历史比赛数据训练深度学习模型，以预测比赛期间的轮胎能量。此外，我们在同一数据集上拟合了 XGBoost（一种基于决策树的机器学习算法）并比较结果，两者均表现出令人印象深刻的性能。进一步地，我们引入了两种不同的可解释人工智能方法，即特征重要性和反事实解释，以深入理解预测背后的推理。因此，我们的贡献形成了一种可解释的自动化方法，可帮助 F1 车队优化其比赛策略。
英文摘要：
Formula One (F1) race strategy takes place in a high-pressure and fast-paced environment where split-second decisions can drastically affect race results. Two of the core decisions of race strategy are when to make pit stops (i.e. replace the cars' tyres) and which tyre compounds (hard, medium or soft, in normal conditions) to select. The optimal pit stop decisions can be determined by estimating the tyre degradation of these compounds, which in turn can be computed from the energy applied to each tyre, i.e. the tyre energy. In this work, we trained deep learning models, using the Mercedes-AMG PETRONAS F1 team's historic race data consisting of telemetry, to forecast tyre energies during races. Additionally, we fitted XGBoost, a decision tree-based machine learning algorithm, to the same dataset and compared the results, with both giving impressive performance. Furthermore, we incorporated two different explainable AI methods, namely feature importance and counterfactual explanations, to gain insights into the reasoning behind the forecasts. Our contributions thus result in an explainable, automated method which could assist F1 teams in optimising their race strategy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
