Non-intrusive assessment of vigilance in drivers based on eye movement and blinking

Due to lifestyle and work demands, chronic sleep deprivation is now experienced by many people, leading to increased drowsiness and fatigue which can have a negative influence on health, safety and work performance. Drowsiness, in particular, can influence fitness to drive and put people at significant risk. With this in mind and in response to increasing demand from market and public domains, Alcohol Countermeasure Systems (ACS) has launched innovative research into methods and technology for improving driver and vehicle safety. The main objective of this research is to develop non-intrusive techniques for real-time assessment of the state of vigilance of drivers based on behavioural patterns (particularly, eye movements and blinks). In this project, advanced machine learning and signal processing techniques will be used to develop appropriate methodologies for real-time monitoring of drowsiness.

Faculty Supervisor:

Mark Coates

Student:

Min Liang

Partner:

Alcohol Countermeasure Systems Corp.

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

McGill University