Understanding the role of partially automated systems in improving older driver safety

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Adults 70 years old and older are at a greatly inflated risk of crashing cars at traffic intersections. Older drivers have been shown to have difficulty with executing appropriate scanning behaviour at intersections. Several explanations have been proposed, including decreases in upper torso mobility, working memory capacity, the ability to attend to relevant stimuli, and the ability to process information simultaneously, both centrally and peripherally. With the rapid proliferation of technology, several automated systems can aid human performance by simplifying the driving task and its requirements. However, given the vast variety of issues that the aging population tends to deal with, including cognitive, visual, and motor declines, there is an evident need for an adaptive ambient interface that requires minimal effort on the part of the user but more importantly can effectively engage the user in critical situations through appropriately timed warnings and alerts. The proposed program not only seeks to understand whether the availability of autonomous systems can improve older driver safety at intersections, but more specifically seeks to develop and evaluate a prototype collision warning system that is personalized to older drivers. The program will 1) develop an ambient intelligent collision warning interface for aging drivers to improve safety at intersections and other crash situations; 2) develop, test, integrate, and validate efficient algorithms for predictive and adaptive learning; and 3) iteratively evaluate the effectiveness of the warning system prototypes on older driver safety as measured by eye movements, vehicle profiles, physiological data, and subjective measures. The research objectives will be accomplished through four tasks that will involve the exposure of highly qualified personnel to several domains including, but not limited to experimental design, machine learning, embedded systems, human factors, cognitive psychology, instrumentation, and computer vision. The proposed project will apply qualitative and quantitative experimental techniques, involve the use of multiple sensors for data collection, collect experimental data from human participants and address challenges in data synchronization and interpretation. The research will yield a positive health impact on the public by increasing the safety of aging populations and other drivers. The resultant knowledge and technology will represent important advancements at the intersection of aging, human factors, safety, and autonomous vehicles. The proposed research will advance Canada's position as a leader in surface transportation human factors and will benefit the driving community globally.