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Vehicle Speed Prediction Using Deep Learning

机译：使用深度学习进行车速预测

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摘要

Global optimization of the energy consumption of dual power source vehicles such as hybrid electric vehicles, plug-in hybrid electric vehicles, and plug in fuel cell electric vehicles requires knowledge of the complete route characteristics at the beginning of the trip. One of the main characteristics is the vehicle speed profile across the route. The profile will translate directly into energy requirements for a given vehicle. However, the vehicle speed that a given driver chooses will vary from driver to driver and from time to time, and may be slower, equal to, or faster than the average traffic flow. If the specific driver speed profile can be predicted, the energy usage can be optimized across the route chosen. The purpose of this paper is to research the application of Deep Learning techniques to this problem to identify at the beginning of a drive cycle the driver specific vehicle speed profile for an individual driver repeated drive cycle, which can be used in an optimization algorithm to minimize the amount of fossil fuel energy used during the trip.
机译：对混合动力电动汽车，插电式混合动力电动汽车和插电式燃料电池电动汽车等双电源车辆的能源消耗进行全球优化需要在旅途开始时了解完整的路线特性。主要特征之一是整个路线上的车速曲线。该配置文件将直接转换为给定车辆的能源需求。但是，给定驾驶员选择的车速会因驾驶员的不同而有所不同，并且可能会比平均交通流量更慢，相等或更快。如果可以预测特定的驾驶员速度曲线，则可以在所选路线上优化能耗。本文的目的是研究深度学习技术在此问题上的应用，以在驾驶周期开始时识别单个驾驶员重复驾驶周期的特定于驾驶员的车速曲线，可以将其用于优化算法中以最小化旅途中使用的化石燃料能源量。

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