Driving safety has been a concern since the first cars appeared on the streets. Driver
inattention has been singled out as a major cause of accidents early on. This is hardly …

In recent years, many studies have used poor cognitive functions to explain risk safety
differences among drivers. Working memory is a cognitive function with information storage …

Abstract To apply Artificial Intelligence (AI), Machine Learning (ML) and Machine Reasoning
(MR) in health informatics are often challenging as they comprise with multivariate …

Distracted driving such as phone use during driving is risky, as it increases the probability of
severe crashes. Detecting distraction using Naturalistic Driving Studies was attempted in …

In a mixed-traffic scenario where both autonomous vehicles and human-driving vehicles
exist, a timely prediction of driving intentions of nearby human-driving vehicles is essential …

Driver inattention is among major contributing factors to traffic accidents. There have been
and continue to be efforts by governing bodies, car manufacturers, and researchers to …

Many driver monitoring systems (DMSs) have been proposed to reduce the risk of human-
caused accidents. Traditional DMSs focus on detecting specific predefined abnormal driving …

One debatable issue in traffic safety research is that the cognitive load by secondary tasks
reduces primary task performance, ie, driving. In this paper, the study adopted a version of …

In the last decade, distraction detection of a driver gained a lot of significance due to
increases in the number of accidents. Many solutions, such as feature based, statistical …

The frequent traffic accidents lead to a large number of casualties and large related financial
losses every year; this serious state is owed to several factors; among those, driving …