用深度学习预测赛车轮胎能量，辅助车队制定最优进站策略
Explainable Time Series Prediction of Tyre Energy in Formula One Race Strategy
- 基于历史遥测数据训练深度模型预测轮胎能量
- 模型准确率高，可为进站时机提供决策支持
- 引入可解释AI方法，让预测结果更透明可信
一级方程式赛车（F1）比赛策略在高压快节奏环境中进行，毫秒之差可能决定胜负。两大核心决策是何时进站换胎以及选择何种胎压（硬、中、软）。最优进站策略可通过预测不同胎压的磨损情况来确定，而磨损程度可由施加于轮胎的能量（即轮胎能量）推算。本文使用梅赛德斯-AMG PETRONAS F1车队的历史遥测数据，训练深度学习模型以预测比赛中轮胎能量，并对比了基于决策树的XGBoost算法，两者均表现优异。此外，我们结合特征重要性和反事实解释两种可解释人工智能方法，深入分析预测逻辑。本研究提出了一种可解释、自动化的轮胎能量预测方法，可帮助车队优化比赛策略。
原文摘要 · Abstract (English)
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.
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