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. · 2025 · ROSA P / University of Maine

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Summary

This study addresses the rising rate of vehicle-pedestrian collisions involving older adult drivers in Maine, a state with the oldest median population in the United States. Older adults face heightened risks due to age-related declines in visual perception and reaction time, particularly in low-visibility conditions such as nighttime and rain. The research investigates whether a computer vision-assisted augmented reality (CVAR) system can improve pedestrian detection and safety for this demographic. The project was motivated by the lack of AR driving tools specifically designed for older adults and the urgent need to reduce pedestrian fatalities, which have increased significantly in recent years. The research was conducted in three phases at the University of Maine’s VEMI Lab. Phase 1 involved a participatory design activity with 20 older adults (aged 66–85) to identify driving challenges and co-create prototype AR icons. Participants identified low-visibility environments as primary challenges and selected a bright yellow, opaque pedestrian symbol as the most effective alert. Phase 2 focused on technical development, converting the lab’s autonomous vehicle simulator into a manual driving simulator. The virtual environment was modeled on a high-risk section of Stillwater Avenue in Bangor, Maine, featuring realistic weather and lighting conditions. Phase 3 consisted of a behavioral driving study with 12 older adult drivers (aged 66–82). Participants completed six simulated trials varying by lighting (daytime, nighttime, nighttime with rain) and CVAR status (on or off). The study measured pedestrian detection distance, collision rates, and self-reported cognitive load and confidence. The results from Phase 3 demonstrated that the CVAR system significantly improved safety outcomes. Notably, zero pedestrian collisions occurred when the AR icon was active, compared to five collisions when it was off. The system particularly enhanced pedestrian detection during nighttime and rainy conditions, where visibility is lowest. Additionally, participants reported increased driving confidence and reduced self-reported cognitive load when using the CVAR indicator. The design preferences established in Phase 1, specifically the use of a high-contrast, world-fixed icon positioned directly over the pedestrian, proved effective in capturing attention without causing distraction. These findings suggest that AR-based driver assistance systems hold significant promise for improving pedestrian safety, especially for older adults who struggle with hazard detection in challenging environments. The study establishes a foundation for developing inclusive, user-centered AR interfaces that compensate for age-related perceptual declines. While further refinement and real-world testing are required, the results support the potential for collaboration between researchers, transportation agencies, and technology developers to deploy such safety innovations. The work highlights the importance of participatory design in creating technologies that meet the specific needs of older drivers, thereby extending their independence and reducing crash risks.

Key finding

The augmented reality indicator eliminated pedestrian collisions in the simulation and improved detection rates, particularly during nighttime and rainy conditions, while also increasing driver confidence and reducing cognitive load.

Methodology

simulator

Sample size: 12

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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• hud ar windshield
• ehmi external hmi
• vru facing ehmi
• vru conspicuity

Information type

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• Methodological Resource: tool software, validation psychometrics