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

功能齐全、价格实惠、批量购买的自动驾驶汽车(AV)将需要很多年的时间。目前的半自动驾驶汽车(SA)需要司机监督并在SA车辆无法处理的情况下立即接手，将被广泛使用。监督SA系统的司机必须保持对道路和车辆的情景感知。如果需要的话，监控司机注意力水平的方法是有限的。这种系统的一个应用是监控注意力不集中的司机，包括分心驾驶或使用麻醉剂，还有一些年龄较大的司机，他们的驾驶能力可能会因为衰老或疾病而下降。该项目将研究使用环境感应技术评估驾驶员生命体征的技术，这些技术可以整合到普通车辆的功能中。它将结合和扩大申请者对司机健康和驾驶行为之间关系的研究，以及环境传感器用于幸福感评估的新应用。将探索通过可见光和热视频的非接触式视频放大来评估环境，以评估心脏和呼吸频率。血压的环境感知仍然是一个巨大的挑战，尽管从两个不同身体位置的心率可以确定脉搏通过时间(PTT)。PTT的变化与相对血压的变化相关。手和头为心率的评估提供了两个潜在的位置，因为血压的变化可能表明压力、焦虑或嗜睡。注意力分析可以通过面向司机的热光和可见光视频进行，该视频结合了头部跟踪、眼睛跟踪和眼皮分析。在该计划中，将使用数据融合技术整合各种指标，以获得驾驶员注意力的衡量标准以及控制车辆的一般能力。一个关键的关注点是对车载条件具有健壮性的分析技术，需要针对环境光线水平、运动、摄像头位置和环境温度的变化创建算法解决方案。申请人在复杂算法和分析方面的背景将有助于解决这一复杂的问题，包括从大型老年驾驶数据集中捕获见解的大量出版物。从他在SAM3创新中心的领导职位来看，申请人与国际临床研究社区有工作关系，开发用于检测和测量幸福感的远程传感器应用程序。申请人目前正在使用驾驶模拟器，它允许在受控条件下进行研究实验、验证和测试场景，同时确保安全。此外，申请者还可以使用渥太华地区X.0自动车辆测试轨道，这是一个独特的高度仪器化的设施，用于使用实际车辆进行测试，包括无人机和SA车辆驾驶应用程序。

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.