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University of Waterloo develops software for detecting distracted driving

Engineering researchers at Canada’s University of Waterloo (UW) have developed computer algorithms that can accurately determine when drivers are texting or engaged in other distracting activities.

The system developed at UW’s Center for Pattern Analysis and Machine Intelligence (CPAMI) uses interior cameras and artificial intelligence (AI) to detect hand movements that deviate from normal driving behavior, and grades or classifies them in terms of possible safety threats.

The research team says the information could be used to improve road safety by warning or alerting drivers when they are dangerously distracted, and as advanced self-driving features are increasingly added to conventional cars, signs of serious driver distraction could be employed to trigger protective measures.

Algorithms at the heart of the technology were ‘trained’ using machine-learning techniques to recognize actions such as texting, talking on a cell phone or reaching into the backseat to retrieve something. The seriousness of the action is assessed based on duration and other factors.

The new work builds on extensive previous research at CPAMI on the recognition of indicators, including frequent eye 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 center now seeks to combine the detection, processing and grading of several different kinds of driver distraction in a single system.

The research was led by Fakhri Karray (right), an electrical and computer engineering professor and director of the CPAMI at Waterloo. The 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.

Another research project at CPAMI is currently 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.

Citing estimates that distracted drivers are to blame for up to 75% of all traffic accidents worldwide, Karray said, “It has a huge impact on society. The car could actually take over driving if there was imminent danger, even for a short while, in order to avoid crashes.”

September 12, 2017

Written by Adam Frost

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