Augmented Information in the Driving Environment: Increasing Pedestrian Safety with Augmented Reality Indicators for Older Adult Drivers in Maine

Brown, Justin R.; Fink, Paul D. S.; Movalia, Raina M.; Kastelein, Margaret E.; James, Kyle J.; Corey, Richard R.; Giudice, Nicholas A.

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Augmented Information in the Driving Environment: Increasing Pedestrian Safety with Augmented Reality Indicators for Older Adult Drivers in Maine

2025-10-31
By Brown, Justin R. ; Fink, Paul D. S. ; Movalia, Raina M.

File Language:

English

[PDF - 10.76 MB]

Details

Creators:

Brown, Justin R. ; Fink, Paul D. S. ; Movalia, Raina M. ; Kastelein, Margaret E. ; James, Kyle J. ; Corey, Richard R. ; Giudice, Nicholas A.
Corporate Creators:

VEMI Lab, University of Maine
Corporate Contributors:

Maine. Department of Transportation ; United States. Federal Highway Administration. State Planning and Research Program (SPR)
Subject/TRT Terms:
Resource Type:

Tech Report
Geographical Coverage:

United States ; Maine
Corporate Publisher:

University of Maine
Abstract:

This report describes the project activities conducted at The University of Maine's VEMI Lab to address the increasing rate of vehicle-pedestrian collisions within the state of Maine and to improve older adult driving safety. Leveraging VEMI's experience with virtual reality environments, human-subject research, and the lab's fully immersive autonomous vehicle simulator, a proof-of-concept, computer vision-assisted augmented reality (CVAR) system was designed and tested by Maine drivers aged 65 and older. This system displays an augmented reality (AR) visual indicator (i.e., CVAR icon) on the simulated vehicle's windshield whenever a pedestrian is detected in or around the roadway. The goal of this AR icon is to capture the driver's attention and direct it toward the pedestrian, increasing situational awareness, expediting driving reactions, and helping prevent potentially fatal collisions.
Format:

PDF
Download URL:

https://rosap.ntl.bts.gov/view/dot/87520/dot_87520_DS1.pdf
File Type:

[PDF - 10.76 MB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; US Transportation Collection
Main Document Checksum:

urn:sha-512:7b547298f2779b355b519c7a68a6d9dc6d42adb79e7cd8b2218929445361313e84a94ed5feb5cb5b60ea09ccb147e2ee85afe0fcc1face0fffe9a78cb613b2ac

File Language:

English

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