Researchers at the University of Waterloo create a system that recognizes such distracting actions as texting, talking on a cell phone and reaching into the backseat.
by Staff
September 19, 2017
Photo by Intel Free Press via Wikimedia Commons.
2 min to read
Photo by Intel Free Press via Wikimedia Commons.
Engineering researchers at the University of Waterloo in Ontario, Canada, have developed computer algorithms that can accurately determine when drivers are texting or engaged in other distracting activities, according to the university.
The system uses cameras and artificial intelligence (AI) to detect hand movements that deviate from normal driving behavior and grades or classifies them as potential safety threats. The information could be used to improve road safety by warning or alerting drivers when they’re dangerously distracted, said Fakhri Karray, an electrical and computer engineering professor at Waterloo.
Ad Loading...
Additionally, in an emergency situation, an automated driving system might assume control of the vehicle.
“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 Center for Pattern Analysis and Machine Intelligence (CPAMI) at Waterloo.
Algorithms at the heart of the technology were trained using machine-learning techniques to recognize such actions as texting, talking on a cell phone and reaching into the backseat to retrieve something. The system assesses the seriousness of the action based on duration and other factors.
This project builds on previous CPAMI research on recognizing the signs of drowsy driving. 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 into a single system.
Ad Loading...
“It has a huge impact on society,” said Karray, citing estimates that distracted drivers are to blame for up to 75% 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.
Karray’s research was performed in collaboration with PhD candidates Arief Koesdwiady and Chaojie Ou and with post-doctoral fellow Safaa Bedawi. Their findings were recently presented at the 14th International Conference on Image Analysis and Recognition in Montreal.
Distracted driving remains one of the most persistent risks in fleet operations. New approaches focus on removing mobile device use entirely while adding real-time safety support.
As distraction risks evolve, fleets are turning to smarter, more connected technologies to better understand what’s happening behind the wheel. Part 2 explores how these tools are helping identify risky behaviors and improve visibility across operations.
Distracted driving is often measured by what we can see—phones in hand, eyes off the road. But what about the distractions we can’t? A recent incident raises a bigger question about awareness, attention, and why subtle risks so often go unnoticed.
Fleets have more driver data than ever, so why isn't behavior changing? Training requires more than reports and coaching — it requires real-world practice.
A two-part conversation with Stefan Heck on how AI is transforming the fight against distracted driving. As fleets adopt smarter tools, the focus shifts from reacting to preventing risk. In Part 1, we look at where AI is making an impact for fleets today.
An 11% drop in pedestrian fatalities in early 2025 signals progress in U.S. road safety, but elevated death rates and ongoing risks underscore the need for continued action from fleets and policymakers.
