AIMC Topic: Distracted Driving
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Detection and prediction of driver drowsiness using artificial neural network models.
Accident; analysis and prevention
Dec 6, 2017
Not just detecting but also predicting impairment of a car driver's operational state is a challenge. This study aims to determine whether the standard sources of information used to detect drowsiness can also be used to predict when a given drowsine...
Modeling of injury severity of distracted driving accident using statistical and machine learning models.
PloS one
Jan 1, 2025
Distracted Driving (DD) is one of the global causes of high mortality and fatality in road traffic accidents. The increase in the number of distracted driving accidents (DDAs) is one of the concerns among transportation communities. The present study...
Automated Assessment of Driver Distraction Using Multimodal Wearable Data and Squeeze-Excitation Networks.
Studies in health technology and informatics
Aug 22, 2024
Driver distraction, crucial for road safety, can benefit from multimodal physiological signals assessment. However, fusion of heterogeneous data is highly challenging. In this study, we address this challenge by exploring 1D convolution neural networ...
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