2015International Journal of Vehicle PerformanceRequires access

Driver vigilance level detection systems: a literature survey

Laith Dababneh, Moustafa El Gindy

Open publisher page 7 citations

Abstract

Several studies have shown that alertness loss during driving is considered as one of the major causes of vehicle accidents around the world. As a result, work has been done to develop detection systems that are capable of monitoring the vigilance levels of drivers as well as warn drivers to avoid imminent crash accidents. This paper reviews the causes of the prolonged alertness loss characterised by sleepiness, fatigue and monotony. It also gives an overview of the vigilance monitoring techniques along with products that are commercially available. In addition, the paper reviews alertness monitoring techniques that use artificial neural networks (ANNs) for their ability to classify different levels of alertness. Finally, based on this review the study concludes that the vehicle driver interface monitoring technique is cheap, non-intrusive and requires low computational power and thus further research is recommended to find a better correlation between drowsiness and this technique.

About this research paper

What this paper is about

Several studies have shown that alertness loss during driving is considered as one of the major causes of vehicle accidents around the world. As a result, work has been done to develop detection systems that are capable of monitoring the vigilance levels of drivers as well as warn drivers to avoid imminent crash accidents. This paper reviews the causes of the prolonged alertness loss characterised by sleepiness, fatigue and monotony. It also gives an overview of the vigilance monitoring techniques along with products that are commercially available. In addition, the paper reviews alertness monitoring techniques that use artificial neural networks (ANNs) for their ability to classify different levels of alertness. Finally, based on this review the study concludes that the vehicle driver interface monitoring technique is cheap, non-intrusive and requires low computational power and thus further research is recommended to find a better correlation between drowsiness and this technique.

Why it matters

OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Several studies have shown that alertness loss during driving is considered as one of the major causes of vehicle accidents around the world. As a result, work has been done to develop detection systems that are capable of monitoring the vigilance levels of drivers as well as warn drivers to avoid imminent crash accidents. This paper reviews the causes of the prolonged alertness loss characterised by sleepiness, fatigue and monotony. It also gives an overview of the vigilance monitoring techniques along with products that are commercially available. In addition, the paper reviews alertness monitoring techniques that use artificial neural networks (ANNs) for their ability to classify different levels of alertness. Finally, based on this review the study concludes that the vehicle driver interface monitoring technique is cheap, non-intrusive and requires low computational power and thus further research is recommended to find a better correlation between drowsiness and this technique.

Key concepts: Alertness, Vigilance (psychology), Crash, Computer science, Computer security, Psychology, Cognitive psychology, Psychiatry

Related papers

Back to paper searchBrowse research topicsOriginal source
Driver vigilance level detection systems: a literature survey — Research Paper | ScholarLens