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Efficient driver behavior prediction system based on temporal fusion of sensor data using deep (bidirectional) recurrent neural network
Efficient driver behavior prediction system based on temporal fusion of sensor data using deep (bidirectional) recurrent neural network
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机译：使用深度（双向）递归神经网络基于传感器数据的时间融合的高效驾驶员行为预测系统
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摘要
By way of example, the technology disclosed by this document may be implemented in a method that includes receiving stored sensor data describing characteristics of a vehicle in motion at a past time and extracting features for prediction and features for recognition from the stored sensor data. The features for prediction may be input into a prediction network, which may generate a predicted label for a past driver action based on the features for prediction. The features for recognition may be input into a recognition network, which may generate a recognized label for the past driver action based on the features for recognition. In some instances, the method may include training prediction network weights of the prediction network using the recognized label and the predicted label.
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