Safety Evaluation and Improvement of Autonomous Vehicles

先进的驾驶员辅助系统(ADA)承诺无事故道路，然而，在达到5级自动驾驶(全自动车辆)之前，最终的驾驶责任取决于人类驾驶员。过去的研究表明，司机很容易对半自动系统感到自满。因此，需要充分了解自动驾驶功能的安全影响，以提高道路安全。拟议的研究将开发工具来衡量司机的认知负荷，进行安全评估，并将评估与大量ADA相关的安全益处和风险，以帮助安大略省交通部制定关于在安大略省道路上使用ADA的政策和法规。此外，拟议的研究将作为合作伙伴组织之一德雷耶夫正在开发的商业驾驶员监测系统的基准，以评估认知负荷措施的潜力，以提高最先进的驾驶员监测系统的性能。此外，拟议的研究将开发信号处理和机器学习技术，以提高从生理测量获得的认知负荷信息的质量，如瞳孔大小、眼睛凝视和心率。然后，将结合德雷耶夫为有效监控司机而使用的现有信号来分析这些措施。最后，建议的研究将证明认知负荷测量的好处，从非侵入性和廉价的生理传感器收集，以提高驾驶员监控系统的性能和准确性。

Advanced driver assistance systems (ADAS) promise accident-free roads, however, until level-5 autonomy (the fully automated vehicle) is reached, ultimate driving responsibility relies on the human driver. Past research shows that drivers easily become complacent about the semi-automated system. Hence, the safety implications of the automated driving features need to be fully understood to enhance road safety. The proposed research will develop tools to measure the cognitive load of drivers for safety evaluation and will assess the safety benefits and risks associated with a vast array of ADAS in order to help the Ontario Ministry of Transportation develop policies and regulations on ADAS use on Ontario roads. Further, the proposed research will benchmark a commercial driver monitoring system being developed by Dreyev, one of the partner organizations, to assess the potential of cognitive load measures to enhance the performance of state-of-the-art driver monitoring systems. In addition, the proposed research will develop signal processing and machine learning techniques to enhance the quality of cognitive load information obtained from physiological measures, such as pupil size, eye-gaze, and heart rate. Then, these measures will be analyzed in conjunction with the existing signals used by Dreyev for effective driver monitoring. Finally, the proposed research will demonstrate the benefits of cognitive load measures, collected from non-invasive and inexpensive physiological sensors to improve the performance and accuracy of driver monitoring systems.