University of Waterloo
Engineering 5 (E5), 6th Floor
Phone: 519-888-4567 ext.32600
Design team members: Edward Cheung, Kee Tang, Christine Wong
Supervisor: Professor M.E. Jernigan
Drowsiness is a serious concern when driving and can cause accidents because it impairs the elements of human performance that are critical to safe driving: slower reaction time, reduced vigilance, deficits in information processing.
Existing drowsiness detection methods include:
Carnegie-Mellon Research Institute:
PERCLOS SystemsPERCLOS (percentage closure) is defined as the measurement of the percentage of time the pupils of the eyes are 80% or more occluded over a specified time interval. It has been found that PERCLOS is a reliable measure in detecting drowsiness.
Head position metrics:
Systems have been devised such that the head position of the driver is detected and when the head leaves the headrest past a certain threshold percentage, the system alerts the driver.
The purpose of the drowsiness detection system is to aid in the prevention of accidents passenger and commercial vehicles. The system will detect the early symptoms of drowsiness before the driver has fully lost all attentiveness and warn the driver that they are no longer capable of operating the vehicle safely. This device will not, however, guarantee that the driver will be fully awakened and that an accident will be avoided. It is simply a tool for improving driver safety; focusing primarily on long-haul truck drivers, nighttime drivers, people driving long distances alone or people suffering from sleep deprivation.
The methodology used to design the Drowsiness Detection System is an iterative research and analysis cycle. The research stage generates concepts and the analysis stage selects concepts, analyze requirements and constraints. The cycle is then repeated to generate more refined concepts and these concepts are further analyzed.
Reliability: The solution should reliably detect drowsiness so that it can serve its purpose as a system for promoting driver safety.
Real-time response: The operation of a vehicle can involve relatively high speeds, a system that cannot detect drowsiness and warn that driver promptly can lead to serious consequences.
Unobtrusive: It is very important that the solution is as transparent to the driver as possible.
Economical: Existing solutions to this problem are available today but the effective ones are usually too expensive for widespread implementation.
Flexible: To be effective, the solution should be designed so as to accommodate for all types of users, in terms of physical attributes.
Space: The solution needs to be implemented in a space-efficient manner. It must not interfere with the existing controls of the car.
Power: There will be a limited power source so the solution needs to designed so that it can operate properly on limited power requirements.
Eye detection algorithm:
The physiological properties and appearances of the eyes will be investigated and the method of capturing these properties of the eyes using infrared lighting will be explored. Kalman trackers will be used to determine eyes and head dynamics between successive images and a probabilistic model will be used to calculate the driver’s vigilance.
Lane tracking detection algorithm:
Using image processing techniques measures the behaviour of a driven vehicle with respect to the vehicle’s position to the surroundings.