New detection system uses AI to find distracted drivers on the road
The information could be used to improve road safety by warning or alerting drivers when they are dangerously distracted.
Engineering researchers at the University of Waterloo have developed computer algorithms that can accurately determine when drivers are texting or engaged in other distracting activities.
The system is designed to detect hand movements that deviate from normal driving behaviour. Using cameras and artificial intelligence (AI) the system grades or classifies them in terms of possible safety threats.
According to Fakhri Karray, an electrical and computer engineering professor at Waterloo, the information could be used to improve road safety by warning or alerting drivers when they are dangerously distracted.
New vehicle features have increased autonomous capabilities, he said, signs of serious driver distraction could be employed to trigger protective measures.
“The car could actually take over driving if there was imminent danger, even for a short while, in order to avoid crashes,” said Karray, a University Research Chair and director of the Centre for Pattern Analysis and Machine Intelligence (CPAMI) at Waterloo.
The team trained the algorithms using machine-learning techniques to recognize actions such as texting, talking on a cellphone or reaching into the backseat to retrieve something. The seriousness of the action is assessed based on duration and other factors.
That work builds on extensive previous research at CPAMI on the recognition of signs, including frequent blinking, that drivers are in danger of falling asleep at the wheel. Head and face positioning are also important cues of distraction. Ongoing research at the centre now seeks to combine the detection, processing and grading of several different kinds of driver distraction in a single system.
It is estimated that 75 per cent of all traffic incidents worldwide are due to distracted drivers. So the implications of this new technology could revolutionize the way drivers interact with their vehicle and their fellow drivers.
Another research project at CPAMI is exploring the use of sensors to measure physiological signals such as eye-blinking rate, pupil size and heart-rate variability to help determine if a driver is paying adequate attention to the road.
Karray’s research — done in collaboration with PhD candidates Arief Koesdwiady and Chaojie Ou, and post-doctoral fellow Safaa Bedawi — was recently presented at the 14th International Conference on Image Analysis and Recognition in Montreal.