Using Bidirectional Long Short Term Memory with Attention Layer to Estimate Driver Behavior

Abstract

Driver distraction is one of the primary causes of fatal car accidents in U.S. Analyzing driver behavior using different types of data including driving data, driver status or a combination of them is an emerging machine learning solution to detect the distraction level and notify the driver. Deep learning methods such as recurrent neural networks outperform other machine learning methods in car safety applications. In this paper, we used time-sequenced driving data that we collected in eight driving contexts to measure the driver distraction level. Our RNN is also capable of detecting the type of behavior that caused distraction. We used the driver interaction with the car infotainment system as the distracting activity. Two types of LSTM networks were used including bidirectional LSTM network and attention network. We compare the performance of these two complex networks to that of the simple LSTM in estimating driver behavior. We show that our attention network outperforms the other two, while adding bidirectional LSTM networks enhanced the training process of simple LSTM network.