Artificial Intelligence powered Smart Cameras to Monitor Drowsiness of Pilots and Drivers.

"Human fatigue is a serious issue affecting the safety of the traveling public in all modes of transportation" - The Centre Suisse d'Electronique et de Microtechnique (CSEM)

Smart cameras are to have a key role in a new system which uses artificial intelligence to monitor driver's drowsiness & fatigue. The aim of the European Clean Sky project HIPNOSIS is to provide tools to evaluate pilots’ fatigue state.

Coordinated by CSEM and under the guidance of Honeywell, the project will combine artificial intelligence (AI) with aeronautics expertise and automotive mobility, contributing to the advent of next generation cockpits.

Consisting of smart cameras and wearable electronics, a safety kit will enable the real-time detection of signs of drowsiness. The aim is to improve fatigue-risk management.

The team behind the project cite an incident last November when an Australian pilot fell asleep while operating a passenger flight, overshooting its destination by 50 kilometers. A few months earlier, in the US, investigators found that an air disaster had been narrowly avoided in San Francisco the previous year. The danger had been brought about by a pilot’s lack of sleep.

"Human fatigue is a serious issue affecting the safety of the traveling public in all modes of transportation", said the team. Nearly 20% of the major US Transportation Safety Board investigations completed between 2001 and 2012 identified fatigue as a probable cause, contributing factor, or a finding.

The HIPNOSIS consortium, led by CSEM, aims to improve the evaluation of pilot fatigue by providing innovative monitoring tools—namely, a specific vision-based system combined with a bio-physiological signal sensor.

Machine learning at the service of onboard safety HIPNOSIS won the tender launched by the Clean Sky 2 Joint Undertaking, a European research program dedicated to aeronautics.

Andrea Dunbar, head of Embedded Vision Systems at CSEM, said: “We will implement computer vision and machine learning algorithms in order to detect signs of drowsiness in pilots in real time.”