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IATSL develops assistive technology that is adaptive, flexible, and intelligent, enabling users to participate fully in their daily lives. Learn more about our research

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Projects

Acceptability of autonomous cars

Keywords: driverless cars, older adults, user acceptability, driving simulation


Overview of Research

Individuals over 65 could constitute more than one-fourth of the Canadian population by the year 2017, as reported by Statistics Canada. Drivers aged 65 and older are 16% more prone to cause accidents in comparison to adult drivers (25 to 64 years old). On the other hand, depriving the older adults of driving can diminish their independence considerably. Even though autonomous cars can be a practical solution for problems regarding the older adultsí driving, much remains to be understood about how older drivers will interact with or trust an autonomous vehicle technology.

This study aims to investigate the acceptability of driverless cars among older adults by simulating a realistic autonomous driving experience for them. Three scenarios of autonomous driving will be developed in a driving simulator with 360 degree field-of-view and a high resolution stereoscopic projection system. The scenarios of traffic congestion, rainy night as well as a normal driving condition (day light and minimal visual distractions) will be tested in both control modes of fully manual and fully autonomous. The reported experience of the subjects in each trial would help us evaluate the level of acceptability of autonomous control among older adults and how these previously reported avoided driving conditions will affect their levels of acceptability. The data collected on the older adultsí acceptability of fully autonomous mode of control as well as the collected driving performance measurements will shed light on the reasons why older adults might potentially be reluctant to adopt this technology. This data can be used in future studies to develop technologies to increase the acceptability of autonomous cars among older adults.


Research Team

Alex Mihailidis (University of Toronto)
Shabnam Haghzare (University of Toronto)