CNN-LSTM Driving Style Classification Model Based on Driver Operation Time Series Data

Author(s)	K.Bhavana, A.Gopichand, G.Charan sai
Country	India
Abstract	Driving style recognition plays a crucial role in enhancing the effectiveness of intelligent transportation systems and advanced driver assistance systems (ADAS). As vehicles become increasingly connected and autonomous, there is a growing demand for personalized and adaptive solutions that can respond to individual driver behaviors. Traditional methods such as rule-based systems and static models struggle to interpret the dynamic and sequential nature of driving behavior. With the rise of machine learning, particularly deep learning techniques, new opportunities have emerged for real-time, data-driven behavioral analysis. This research builds on these advancements by introducing a hybrid CNN-LSTM model designed to accurately classify driving styles from time-series data.
Keywords	Driving Style Recognition, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Time-Series Data, Feature Extraction, Temporal Dependencies, Classification Accuracy.
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-04-13
Cite This	CNN-LSTM Driving Style Classification Model Based on Driver Operation Time Series Data - K.Bhavana, A.Gopichand, G.Charan sai - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3567
DOI	https://doi.org/10.71097/IJSAT.v16.i2.3567
Short DOI	https://doi.org/g9fcfk

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