Optimal feature tuning model by variants of convolutional neural network with LSTM for driver distract detection in IoT platform
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Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SPRINGER LONDON
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Nowadays, traffic accidents are caused due to the distracted behaviors of drivers that have been noticed with the emergence of smartphones. Due to distracted drivers, more accidents have been reported in recent years. Therefore, there is a need to recognize whether the driver is in a distracted driving state, so essential alerts can be given to the driver to avoid possible safety risks. For supporting safe driving, several approaches for identifying distraction have been suggested based on specific gaze behavior and driving contexts. Thus, in this paper, a new Internet of Things (IoT)-assisted driver distraction detection model is suggested. Initially, the images from IoT devices are gathered for feature tuning. The set of convolutional neural network (CNN) methods like ResNet, LeNet, VGG 16, AlexNet GoogleNet, Inception-ResNet, DenseNet, Xception, and mobilenet are used, in which the best model is selected using Self Adaptive Grass Fibrous Root Optimization (SA-GFRO) algorithm. The optimal feature tuning CNN model processes the input images for obtaining the optimal features. These optimal features are fed into the long short-term memory (LSTM) for getting the classified distraction behaviors of the drivers. From the validation of the outcomes, the accuracy of the proposed technique is 95.89%. Accordingly, the accuracy of the existing techniques like SMO-LSTM, PSO-LSTM, JA-LSTM, and GFRO-LSTM is attained as 92.62%, 91.08%, 90.99%, and 89.87%, respectively, for dataset 1. Thus, the suggested model achieves better classification accuracy while detecting distracted behaviors of drivers and this model can support the drivers to continue with safe driving habits.
Açıklama
Anahtar Kelimeler
Convolutional neural network, Driver distract detection in IoT, Long short-term memory, Optimal feature tuning model, Self adaptive grass fibrous root optimization
Kaynak
Knowledge and Information Systems
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
67
Sayı
6
Künye
Farhan, H. M., Türkben, A. K., & Naseri, R. A. S. (2025). Optimal feature tuning model by variants of convolutional neural network with LSTM for driver distract detection in IoT platform. Knowledge and Information Systems, 67(6), 5151-5186. 10.1007/s10115-025-02342-4
