AIMC Topic: Distracted Driving

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A hierarchical machine learning classification approach for secondary task identification from observed driving behavior data.

Accident; analysis and prevention
According to NHTSA, more than 3477 people (including 551 non-occupants) were killed and 391,000 were injured due to distraction-related crashes in 2015. The distracted driving epidemic has long been under research to identify its impact on driving be...

Detection and prediction of driver drowsiness using artificial neural network models.

Accident; analysis and prevention
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
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
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...