Rank Position Forecasting in Car Racing
Rank Position Forecasting in Car Racing
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DOI：
10.1109/ipdps49936.2021.00082
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Bo Peng;Jiayu Li;Selahattin Akkas;Fugang Wang;Takuya Araki;Ohno Yoshiyuki;J. Qiu
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文献类型：
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作者：
Bo Peng;Jiayu Li;Selahattin Akkas;Fugang Wang;Takuya Araki;Ohno Yoshiyuki;J. Qiu
当在时间序列数据上使用基于深度学习的模型时，赛车中的排名位置预测是一个具有挑战性的问题。其特点是赛车之间具有高度复杂的全局依赖性，存在由现有因素和外部因素造成的不确定性；这也是一个数据稀缺的问题。现有的方法，包括统计模型、机器学习回归模型和几种最先进的深度预测模型在这个问题上都表现不佳。通过对进站事件的详细分析，我们发现分解因果关系并分别对排名位置和进站事件进行建模至关重要。在从不同的神经网络模型中选择子模型时，我们发现对全局依赖结构假设较弱的模型表现最好。基于这些观察，我们提出了 RankNet，它是编码器-解码器网络和单独的多层感知网络的组合，能够提供概率预测来模拟赛车中的进站事件和排名位置。此外，在特征优化的帮助下，RankNet 展示了显着的性能改进，其中 MAE 在两圈预测任务中比最佳基线提高了 19%，在临时预测任务中比最佳基线提高了 7%，并且在适应未见过的新数据时也更加稳定。介绍了模型优化和性能分析的详细信息。它有望在预测赛车中提供有用的神经网络交互，并为一般预测问题中类似挑战性问题的解决方案提供线索。
Rank position forecasting in car racing is a challenging problem when using a Deep Learning-based model over time-series data. It is featured with highly complex global dependency among the racing cars, with uncertainty resulted from existing and external factors; and it is also a problem with data scarcity. Existing methods, including statistical models, machine learning regression models, and several state-of-the-art deep forecasting models all perform not well on this problem. By an elaborate analysis of pit stop events, we find it critical to decompose the cause-and-effect relationship and model the rank position and pit stop events separately. In choosing a sub-model from different neural network models, we find the model with weak assumptions on the global dependency structure performs the best. Based on these observations, we propose RankNet, a combination of the encoder-decoder network and a separate Multilayer Perception network that is capable of delivering probabilistic forecasting to model the pit stop events and rank position in car racing. Further with the help of feature optimizations, RankNet demonstrates a significant performance improvement, where MAE improves 19% in two laps forecasting task and 7% in the stint forecasting task over the best baseline and is also more stable when adapting to unseen new data. Details of the model optimizations and performance profiling are presented. It is promising to provide useful interactions of neural networks in forecasting racing cars and shine a light on solutions to similar challenging issues in general forecasting problems.