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Erschienen in:
Deep Convolutional Encoder–Decoder Models for Road Extraction from Aerial Imagery Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

Unveiling Driver Behavior Through CNN-LSTM-BILSTM Analysis of Operational Time Series Data

verfasst von : Sunil Kumar Nahak, Sanjit Kumar Acharya, Dushmant Padhy

Erschienen in: ICT: Innovation and Computing

Verlag: Springer Nature Singapore

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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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Vorheriges Kapitel Panorama: A Multi-language Software Information Prospecting Facility
Nächstes Kapitel A Systematic Review of Pomegranate Fruit Disease Detection and Classification Using Machine Learning and Deep Learning Techniques
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Metadaten
Titel
Unveiling Driver Behavior Through CNN-LSTM-BILSTM Analysis of Operational Time Series Data
verfasst von
Sunil Kumar Nahak
Sanjit Kumar Acharya
Dushmant Padhy
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
ICT: Innovation and Computing

Print ISBN: 978-981-9994-85-4

Electronic ISBN: 978-981-9994-86-1

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-99-9486-1_12

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