University of Western Ontario

Published purpose

Driving cessation for many aging people is associated with a poorer quality of life brought about by the loss of mobility and independence, which ultimately can lead to physical and mental health problems such as depression, social isolation, diminished independence, etc. Connected and automated vehicle (CAV) technologies can provide feasible solutions to help ameliorate this problem. Although fully autonomous driving remains the goal of all involved R&D efforts, advanced driving assistance systems (ADASs) are already deployed at a mass scale that provide enhanced safety and warning systems and even enable partial automated driving. Recent year production vehicles are equipped with many ADAS features such as collision imminent braking systems, blind spot warning, automated lane centering, adaptive cruise control, automated parking, etc. All these features can help delay driving cessation for the aging population by compensating for the decline in cognitive capacity required for safe driving. However, current production ADAS systems do not consider driver behavior and intention. Typical implementations of ADAS systems are reactive in nature in that the safety of driving actions (acceleration and steering) are evaluated against the environment (presence of traffic ahead or in blind spots on adjacent lanes) after the fact. The proposed research aims to build an artificial intelligence (AI) based driver intentionality predictor that will augment and enhance typical ADAS systems to transform their nature from reactive to proactive. Since driver gaze directly correlates with driver intentionality and precedes driving action, this project will utilize a driver gaze monitoring system to develop an AI-based tool to predict driver intentionality and action. Such predictions can be used to provide warning or even enable automated vehicle actions, such as braking, speed reduction, collision avoidance. This will significantly improve driving safety and increase availability of mobility for aging people in the following ways: • The time horizon of conventional ADAS systems will be broadened to better include drivers with sub-optimal cognitive function. • Aging population can postpone driving cessation until much later stages in their lives without compromising their independence and the quality of life.