Authors: Laith Dababneh; Moustafa El-Gindy
Addresses: Automotive Centre of Excellence, Truck Driving Simulator Laboratory, Faculty of Engineering and Applied Science, The University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa ON L1H 7K4, Canada ' Automotive Centre of Excellence, Truck Driving Simulator Laboratory, Faculty of Engineering and Applied Science, The University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa ON L1H 7K4, Canada
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.
Keywords: crash avoidance; prolonged alertness loss; vigilance monitoring; artificial neural networks; ANNs; driver vigilance; literature review; vehicle accidents; driver safety; sleepiness; fatigue; monotony; drowsiness.
International Journal of Vehicle Performance, 2015 Vol.2 No.1, pp.1 - 29
Available online: 11 Jan 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article