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Predicting driver drowsiness using vehicle measures: recent insights and future challenges.

Charles C Liu¹, Simon G Hosking, Michael G Lenné

¹Accident Research Centre, Monash University, Building 70, Clayton VIC, 3800, Australia. Charles.Liu@muarc.monash.edu.au

Journal of Safety Research

|September 26, 2009

Summary

Detecting driver drowsiness in real-time using vehicle measures is crucial for road safety. Current research shows impact on performance, but more complex measures and individual differences need further study for reliable prediction.

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Area of Science:

Road Safety
Human Factors in Transportation
Automotive Technology

Background:

Driver drowsiness is a major cause of road accidents.
Technological solutions are needed to detect drowsiness and prevent crashes.

Purpose of the Study:

To review the current knowledge on using vehicle measures for real-time driver drowsiness prediction.
To assess the reliability of existing methods.

Main Methods:

Review of behavioral experiments on driver drowsiness.
Analysis of studies investigating driving performance under drowsiness.

Main Results:

Drowsiness significantly impairs driving performance in controlled settings.
Most studies focus on simple performance metrics and average results, limiting real-time applicability.

Conclusions:

Further research is needed on complex performance functions and individual driver variations.
Effective drowsiness countermeasures likely require multiple criteria and measures.