In last two decades, various monitoring systems have been designed and deployed in urban
environments, toward the realization of the so called smart cities. Such systems are based …

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 …

Driver drowsiness and distraction are two main reasons for traffic accidents and the related
financial losses. Therefore, researchers have been working for more than a decade on …

Objective The objective of this study was to analyze a set of driver performance and
physiological data using advanced machine learning approaches, including feature …

This paper presents a novel approach to automated recognition of the driver's activity, which
is a crucial factor for determining the take-over readiness in conditionally autonomous …

Driver distraction has been identified as one major cause of unsafe driving. The existing
studies on cognitive distraction detection mainly focused on high-speed driving situations …

While driver distraction remains an issue in modernized societies, technological
advancements in data collection, storage and analysis provide the means for deeper …

The risk of incurring a road traffic crash has increased year by year. Studies show that lack of
attention during driving is one of the major causes of traffic accidents. In this work, in order to …

Threat assessment during semiautonomous driving is used to determine when correcting a
driver's input is required. Since current semiautonomous systems perform threat assessment …

Driving safety has been attracting more and more interest due to the unprecedented
proliferation of vehicles and the subsequent increase of traffic accidents. As such the …