Lane heading difference: An innovative model for drowsy driving detection using retrospective analysis around curves

• Published in: Accident analysis and prevention Vol. 80; pp. 117 - 124
• Main Authors: Morris, Drew M., Pilcher, June J., Switzer III, Fred S.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.07.2015
• Subjects: Accidents, Traffic; Adolescent; Arousal; Attention - physiology; Automobile Driving; Automobiles; Automotive engineering; Computer Simulation; Driver assistance system; Driver fatigue; Driving; Driving performance; Drowsy driver detection; Fatigue (materials); Fatigue - physiopathology; Female; Humans; Lane deviation; Lanes; Male; Night; Psychomotor Performance; Reaction Time; Sleep Deprivation - physiopathology; Sleep Stages; Statistics as Topic; Transportation safety; Vehicle heading; Young Adult; Driver assistance system; Transportation safety; Drowsy driver detection; Vehicle heading; Lane deviation; Driving performance
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2015.04.007

•A new statistical method was developed to analyze driving performance.•A preexisting statistical method was tested against the new method to determine driving performance following sleep deprivation.•Two vehicle behavioral metrics were used to monitor driving variability.•The new statistical method used in conjunction with a vehicle heading difference metric was most effective at detecting driver variability due to drowsiness. Driving while sleepy is a serious contributor to automobile accidents. Previous research has shown that drowsy drivers produce systematic errors (variability) in vehicle behavior which are detectable using vehicle monitoring technology. The current study developed a new methodological approach using a vehicle heading difference metric to detect drowsy driving more effectively than other more commonly used methods. Twenty participants completed a driving scenario as well as several measures of fatigue in five testing sessions across a night of sleep deprivation. Each simulated highway driving session lasted 20min, and was analyzed for lateral lane position variability and vehicle heading difference variability with two statistical methods. Fatigue measures monitored reaction time, attention, and oculomotor movement. The results showed that examining lane heading difference using the absolute value of the raw data detected driving variability better across the night than other statistical models. The results from the fatigue measures indicated an increase in reaction time and response lapses, as well as a decrease in oculomotor reactivity across the night. These results suggest that in fatigued drivers the statistical model using the absolute value of lane heading could be an improved metric for drowsy driving detection that could accurately detect detriments in driving ability at lower levels of fatigue.