使用深度学习模型为F1提供数据驱动的停车场决策支持
Abhijai Sasikumar1, A Anny Leema1, P Balakrishnan1
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Frontiers in artificial intelligence
|November 21, 2025
概括
预测最佳的F1站停时间对于比赛的成功至关重要. 这项研究使用了FastF1数据的深度学习模型,发现Bi-LSTM为比赛策略提供了最佳的预测准确性.
科学领域:
- 赛车运动分析 赛车运动分析
- 运动中的机器学习
- 数据驱动的决策 基于数据的决策
背景情况:
- 一级方程式赛车的成功取决于战略决策,特别是停车时间.
- 在动态的比赛条件下,人类对排站的判断是不可靠的.
- 利用原始遥测数据为客观,数据驱动的战略提供了潜力.
研究的目的:
- 开发和评估一个数据驱动的框架,用于预测最佳的F1站停时间.
- 为此预测任务比较各种深度学习架构的性能.
- 通过使用先进的数据技术,提高野外停车时间预测的稳定性和准确性.
主要方法:
- 使用来自FastF1 API的原始遥测数据.
- 实施的数据预处理技术:规范化,归算和合成少数人过量采样技术 (SMOTE) 用于类平衡.
- 培训并评估了五种深度学习模型:Bi-LSTM,TCN-GRU,GRU,InceptionTime和CNN-BiLSTM.
- 使用精度,回忆和F1得分指标评估模型性能.
主要成果:
- 与其他架构相比,Bi-LSTM模型表现出卓越的性能.
- 在测试组中,Bi-LSTM获得了0.77的精度,0.86的回忆,以及0.81的F1得分.
- 该模型的有效性归因于其捕捉远程时间依赖性的能力.
- 开发了一个可视化界面来显示模型预测.
结论:
- 深度学习,特别是Bi-LSTM模型,提供了一种强大而准确的方法来预测最佳的F1站停时间.
- 数据驱动的方法在竞争激烈的赛车场景中明显超过了传统的人类判断力.
- 这个框架为赛车运动中赛车战略优化提供了宝贵的工具.
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