Non-Contact Assessment of a Driver's Vital Signs - Driving Awareness

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Fully functional, affordable, and mass purchased Autonomous Vehicles (AV) will take many years. Current semi-autonomous vehicles (SA) that require drivers to supervise and immediately take over in situations the SA vehicle can't handle will be used extensively. Drivers supervising the SA system must maintain situational awareness of the road and vehicle. There are limited ways to monitor for a driver's attention levels to take over, if needed. One application for such a system is to monitor inattentive drivers including distracted driving or the use of intoxicants and there is also aging drivers who may be declining in driving competencies because of aging or illness. This program will research techniques for assessing driver vital signs using ambient sensing techniques that could be integrated into ordinary vehicle functionality. It will combine and expand research by the applicant into the connection between drivers' health and driving behaviours with work on novel applications of ambient sensors for the assessment of well-being. Ambient assessment through non-contact video magnification of visible light and thermal video will be explored to assess heart and respiration rate. The ambient sensing of blood pressure remains a significant challenge, though heart rate from two distinct body locations allows Pulse Transit Time (PTT) to be determined. Changes in PTT correlate with changes in relative blood pressure. Hands and head provide two potential locations for assessment of heart rate as blood pressure change can indicate stress, anxiety or drowsiness. Analysis of attention can prospectively be conducted through driver-facing thermal and visible light video that combines head tracking, eye tracking and eye lid analysis. In the program, a wide range of indicators will be integrated using data fusion techniques to obtain measures of driver attentiveness as well as general capability to take control of the vehicle. A key focus is analysis techniques that are robust to in-vehicle conditions, requiring the creation of algorithms solutions for variations in ambient light level, motion, camera location, and ambient temperature. The applicant's background in complex algorithms and analytics will help address this complex set of problems, including numerous publications capturing insights from a large geriatric driving dataset. From his leadership position within the SAM3 Innovation Hub, the applicant has working relationships with an international clinical research community, developing remote sensor applications to detect and measure well-being. The applicant is currently working with driving simulators, which allow for research experimentation, validation and testing scenarios in controlled conditions while also ensuring safety. In addition, the applicant has access to the Ottawa Area X.0 Autonomous Vehicle Test track, a unique, highly-instrumented facility for testing using actual vehicles, including AV and SA vehicle driving applications.