In a bid to avoid future accidents, a team of researchers has developed first-of-its-kind computer algorithms that can accurately determine when a driver is texting or engaged in other distracting activities while driving.
According to the University Of Waterloo in Ontario, Canada, the system uses cameras and artificial intelligence (AI) to detect hand movements that deviate from normal driving behaviour and grades or classifies them in terms of possible safety threats.
A researcher Fakhri Karray said that information could be used to improve road safety by warning or alerting drivers when they are dangerously distracted.
As advanced self-driving features are increasingly added to conventional cars, he said, 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 recognise 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.
“It has a huge impact on society,” said Karray, citing estimates that distracted drivers are to blame for up to 75 percent of all traffic accidents worldwide.
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.
The research, which was 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.