首页> 外国专利> A VEHICLE VELOCITY PREDICTOR USING NEURAL NETWORKS BASED ON V2X DATA AUGMENTATION TO ENABLE PREDICTIVE OPTIMAL CONTROL OF CONNECTED AND AUTOMATED VEHICLES

A VEHICLE VELOCITY PREDICTOR USING NEURAL NETWORKS BASED ON V2X DATA AUGMENTATION TO ENABLE PREDICTIVE OPTIMAL CONTROL OF CONNECTED AND AUTOMATED VEHICLES

机译:基于V2X数据增强的神经网络的车辆速度预测器能够实现连接和自动车辆的预测最优控制

摘要

Some implementations of the disclosure are directed to reducing or removing time lag in vehicle velocity prediction by training a model for vehicle velocity prediction using labeled features that provide indication of a feature associated with a vehicle acceleration or deceleration event. In one implementation, a method includes: receiving multiple time series datasets, each of the time series datasets including sensor data, GPS data, and vehicle state data collected over time; extracting features from each of the time series datasets that are indicative of a future velocity of a vehicle; labeling the extracted features of each of the time series datasets to indicate vehicle acceleration or deceleration events; and after labeling the extracted features of each of the time series datasets, using at least a subset of the extracted and labeled time series datasets to train a machine learning model that predicts vehicle velocity some time into the future.
机译:本公开的一些实施方式旨在通过使用标记特征训练用于车辆速度预测的模型来减少或去除车辆速度预测中的时间滞后,该特征提供与车辆加速度或减速事件相关联的特征的指示。 在一个实现中,一种方法包括:接收多个时间序列数据集,每个时间序列数据集包括随时间收集的传感器数据,GPS数据和车辆状态数据; 从每个时间序列数据集中提取特征,指示车辆的未来速度; 标记每个时间序列数据集的提取特征,以指示车辆加速度或减速事件; 在标记每个时间序列数据集的提取特征之后,使用提取的和标记的时间序列数据集的至少一个子集来训练预测车辆速度的机器学习模型到未来。

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