Predicting driver drowsiness using vehicle measures: recent insights and future challenges.
BACKGROUND
Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs.


METHODS
The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time.


RESULTS
Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time.


CONCLUSIONS
Further research is necessary to examine more complex functions, as well as individual differences between drivers.


CONCLUSIONS
A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.
Liu
Charles C
CC
Accident Research Centre, Monash University, Building 70, Clayton VIC, 3800, Australia. Charles.Liu@muarc.monash.edu.au
Hosking
Simon G
SG
Lenné
Michael G
MG
D000063:Accidents, Traffic / Q000517:prevention & control*; D001334:Automobile Driving; D003198:Computer Simulation; D006801:Humans; D008328:Man-Machine Systems; D012894:Sleep Stages