An Image-Based Approach for Classification of Driving Behaviour Using CNNs

Abstract

In this work we present an approach for the classification of driving behaviour using Convolutional Neural Networks (CNNs), based on measurements that have been obtained by the internal CAN-bus of the vehicle. As is the case with different driving behaviours, CAN-bus sensor data reflect the driving patterns associated with different types of vehicles. The experimental evaluation is performed on a real-life dataset composed by measuring 27 attributes, for 4 different car types, namely vacuum, car, truck and garbage truck. These features are processed to form pseudocolored images, capturing both temporal and qualitative features of parts of routes. For classification, we use a deep CNN architecture. Results indicated an accuracy of 91% and increased performance compared to other neural network-based approaches. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.