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.
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Acknowledgment
This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE (project code: T1EDK-03459).
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Spyrou, E., Vernikos, I., Savelonas, M., Karkanis, S. (2021). An Image-Based Approach for Classification of Driving Behaviour Using CNNs. In: Nathanail, E.G., Adamos, G., Karakikes, I. (eds) Advances in Mobility-as-a-Service Systems. CSUM 2020. Advances in Intelligent Systems and Computing, vol 1278. Springer, Cham. https://doi.org/10.1007/978-3-030-61075-3_26
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