Milardo, S and Rathore, P and Santi, P and Ratti, C (2022) A Data-Driven Framework for Driving Style Classification. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 28 - 30 November 2022, Brisbane, pp. 253-265.
Full text not available from this repository. (Request a copy)Abstract
Traditional driving behaviour recognition algorithms leverage hand-crafted features extracted from raw driving data and then apply user-defined machine learning models to identify driving behaviours. However, such solutions are limited by the set of selected features and by the chosen model. In this work, we present a data-driven driving behaviour recognition framework that utilizes an unsupervised feature extraction and feature selection algorithm and a deep neural network architecture obtained using an Automated Machine Learning (AutoML) approach. To validate the feasibility of this solution, numerical evaluations were performed on a unique real-world driving datasets collected from 29 professional truck drivers in uncontrolled environments, including supervisor’s scoring of driver behavior that is used as ground truth data. Our experimental results show that the proposed deep neural network model achieves up to 95 % accuracy for multi-class classification, significantly outperforming five other popular machine learning models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
| Item Type: | Conference Paper |
|---|---|
| Publication: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher: | Springer Science and Business Media Deutschland GmbH |
| Additional Information: | The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH. |
| Keywords: | Behavioral research; Classification (of information); Data mining; Feature extraction; Learning algorithms; Learning systems; Network architecture; Truck drivers, Behaviour classification; Behaviour recognition; Data driven; Driving behavior classification; Driving behaviour; Driving style recognition; Driving styles; Feature extraction/selection; Machine learning models; Recognition algorithm, Deep neural networks |
| Department/Centre: | Division of Interdisciplinary Sciences > Robert Bosch Centre for Cyber Physical Systems |
| Date Deposited: | 31 Jan 2023 06:24 |
| Last Modified: | 31 Jan 2023 06:24 |
| URI: | https://eprints.iisc.ac.in/id/eprint/79588 |
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