The transportation industry is currently undergoing a drastic shift towards autonomous vehicles (AV) and processes. It is likely that autonomous transportation systems will be deployed within retirement communities significantly sooner than they are available to the general public: these communities have a particularly acute need for assisted mobility, and the controlled environment of a retirement campus simplifies many of the technical problems related to autonomous control. The elderly population has a relatively high prevalence of physical, sensory and cognitive limitations that must be addressed in the design of an AV system. We believe that a successful user interface in this domain will require a high level of passenger awareness. External and in-vehicle sensors will monitor the position, activities and mental state of passengers. The central research questions that we consider are 1) How can machine learning models be used to extract relevant passenger information? 2) How should an AV user interface incorporate passenger monitoring data to provide safe and reliable mobility service for the elderly?
In Summer 2019: The team was awarded a grant title: “Independent Mobility for the Elderly: Machine-Learning-Based Passenger-Aware User Interfaces for Autonomous Vehicles”, it is Funded by the Jeffress Trust Awards Program in Interdisciplinary Research for the amount of $120,000
In Summer 2020: The team was awarded a grant title: "Cyber Security of Transportation Networks", it is funded by the Commonwealth Cyber Initiative for the amount of $74,318