Classification of Driving Behaviour using Short-term and Long-term Summaries of Sensor Data

Abstract

The classification of driving behaviour is important for monitoring driving risk and fuel efficiency, as well as for adaptive driving assistance and car insurance industry. Starting from raw measurements of acceleration and speed, as provided by a telematics device placed on each vehicle, we define features summarizing instantaneous, short-term and long-term driving behaviour. We use these features along with conventional classification approaches, such as k-NN, SVM and decision trees, to distinguish between different types of driving behaviour. Experiments are performed on a dataset comprising time series of measurements. The results lead to the conclusion that the proposed features, along with decision trees, achieve the highest classification accuracy, whereas they outperform RNN-based approaches. © 2020 IEEE.