Preliminary Results for the Automated Assessment of Driving Simulation Results for Drivers with Cognitive Decline
Bruce Wallace, Sylvain Gagnon, Arne Stinchcombe, Stéphanie Yamin, Rafik Goubran, Frank Knoefel
Abstract
Aging related changes and pathology affecting cognition and the ability to drive are significant issues for individuals, their families and the general population. Ensuring that unsafe drivers have their license suspended or get the additional training they need is important for the safety of the general population. On the other hand, allowing a person to continue to drive as long as they are safe is important for the social, emotional and cognitive wellbeing of the individual. This paper presents results of a preliminary study to see if an automated assessment based on trained machine learning models can correctly classify simulator drives as safe or unsafe in comparison to expert driver assessment opinion. The results show that the machine learning is able to achieve 85% accuracy in comparison to the experts for a combined group of 47 drivers that included 20 Healthy Controls, 9 diagnosed with Lewy Body Dementia and 18 diagnosed with mild Dementia of Alzheimer's Type. This work shows the potential for automated driver simulation assessment, which could reduce the burden on clinicians regarding driver safety evaluation.