Driver Profiling by Using LSTM Networks with Kalman Filtering

Abstract

Nowadays, the most common way to model the driver behavior is to create, under some assumptions, a model of common patterns in driver maneuvers. These patterns are often modeled with averaged driver model. While this idea is very simple and intuitive in the context of driver classification by his/her patterns of maneuvers, our previous works demonstrated that assumptions underlying such models are often inaccurate and not applicable in general settings. In fact, it is very hard to express driving patterns with simple models. In this article we present a new way of modeling drivers: we employ Long-Short Term Memory networks to learn driver models from telematics data. In particular, our neural network models learn to predict driving-related signals, such as speed or acceleration, given the evolution of these signals up to the point of prediction. Solving this prediction task allows us to capture the behavioral model of the driver. We tested our models on several drivers, by predicting their future decisions. By learning our models on one driver and then evaluating them on another driver, we demonstrate that LSTM models are a powerful tool for driver profiling and detection of abnormal situations. We also evaluate the influence of data preprocessing on the quality of predictions. In this context we use Kalman filering, which can remove noise from uncertain dynamic measurements, in effect giving the best linearly estimated data.