In the driving simulator of a Group, an Audi TT car surrounded by a curved screen, twenty drivers participated in a monotonous driving task of following a car on a motorway. At five-minute intervals, they were asked to assess their level of tiredness using the Karolinska Sleepiness Scale.
The validation test was conducted using Blueskeye’s machine learning algorithms. These algorithms utilized Near Infra-Red cameras mounted in the car’s left and right pillars to capture and analyze eye, head, and facial muscle movement multiple times per second, identifying early signs of fatigue. The results showed that the machine assessments of tiredness met the EU’s threshold of 40% sensitivity, indicating that the system can detect when a driver is drowsy and meets the standard required by the EU for new cars.
Blueskeye’s Chief Scientific Officer, Professor Michel Valstar, expressed his ultimate goal of using AI face and voice analysis through existing vehicle cameras and microphones to aid automotive manufacturers in developing vehicles that can respond to occupants’ emotions. This technology validation for driver drowsiness and attention shows the effectiveness of their underlying technology, positioning them well to assist clients in meeting future legislative demands like the EU’s Euro NCAP Vision 2030.
Dr. David R Large, a Senior Research Fellow in the Human Factors Research Group, highlighted the benefits of collaboration with commercial companies like Blueskeye as an opportunity to apply academic expertise and facilities to solve real-world challenges. Participants in the study responded positively to the idea of their cars monitoring them for signs of drowsiness and intervening if necessary, indicating potential receptiveness to such safety measures in the future.