Abstract
This paper presents a novel driving style recognition method with high accuracy, speed, and generalizable. The proposed approach addresses the limitations of existing unsupervised clustering algorithms and single convolutional neural network methods due to the lack of diverse driving data types. The method first collects driver’s operation time sequence information from imperfect driving data. Next, it extracts driver’s style features using a convolutional neural network. The temporal data is then processed using Long Short-Term Memory (LSTM) networks for driving style classification. Further improving this model, we have used advanced algorithm called CNN + LSTM + BILSTM. Experimental results demonstrate an impressive recognition accuracy exceeding 99.
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Nahak, S.K., Acharya, S.K., Padhy, D. (2024). Unveiling Driver Behavior Through CNN-LSTM-BILSTM Analysis of Operational Time Series Data. In: Joshi, A., Mahmud, M., Ragel, R.G., Karthik, S. (eds) ICT: Innovation and Computing. ICTCS 2023. Lecture Notes in Networks and Systems, vol 879. Springer, Singapore. https://doi.org/10.1007/978-981-99-9486-1_12
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DOI: https://doi.org/10.1007/978-981-99-9486-1_12
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