In this paper, we review the state-of-the-art technologies for driver inattention monitoring,
which can be classified into the following two main categories: 1) distraction and 2) fatigue …

Driving in busy highways and roads is becoming complex and challenging, as more cars are
hitting the roads. Safe driving requires attentive drivers, quality perception of the …

Train driver fatigue and distraction are the main reasons for railway accidents. One of the
new technologies to monitor drivers is by using the EEG signals, which provides vital signs …

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 …

The ability to classify driver behavior lays the foundation for more advanced driver
assistance systems. In particular, improving safety at intersections has been identified as a …

Accurate recognition of driver distraction is significant for the design of human-machine
cooperation driving systems. Existing studies mainly focus on classifying varied distracted …

Accurate classification of eye state is a prerequisite for preventing automobile accidents due
to driver drowsiness. Previous methods of classification, based on features extracted for a …

There is accumulating evidence that driver distraction is a leading cause of vehicle crashes
and incidents. In particular, increased use of so-called in-vehicle information systems (IVIS) …

We propose a condition-adaptive representation learning framework for driver drowsiness
detection based on a 3D-deep convolutional neural network. The proposed framework …

Abstract Vehicular Ad hoc Networks (VANETs) have gained a great attention due to the
rapid development of mobile Internet and Internet of Things (IoT) applications. On the other …