Older driver fitness-to-drive evaluation using naturalistic driving data

Guo, Feng; Fang, Youjia; Antin, Jonathan F. · 2015 · openalex

DOI: 10.1016/j.jsr.2015.06.013

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Summary

This study investigates the relationship between older drivers’ functional fitness profiles and their actual driving risk, utilizing data from a naturalistic driving study. The research addresses a longstanding challenge in transportation safety: determining which objective metrics can reliably predict driving safety for seniors, thereby aiding physicians and motor vehicle departments in fitness-to-drive evaluations. Building on prior work that distinguished drivers from non-drivers, this pilot study specifically links assessment metrics to safety outcomes, defined primarily by crash and near-crash (CNC) rates and secondarily by high g-force (HGF) event rates, which serve as proxies for risky driving maneuvers. The analysis focused on 20 primary drivers who completed both functional assessments and naturalistic data collection. Researchers evaluated 48 fitness metrics covering physical, visual, health, and cognitive abilities. Due to the small sample size and high multicollinearity among metrics, Principal Component Analysis (PCA) was employed to reduce dimensionality, grouping correlated metrics into uncorrelated components. Negative binomial regression models were then used to predict CNC and HGF rates, accounting for over-dispersed count data. HGF events were identified using accelerometers with a smoothed threshold of ±0.45g to filter noise, while CNC events were identified through automated triggers and expert video review. The results demonstrated that contrast sensitivity measures were significantly associated with CNC rates; higher contrast sensitivity correlated with lower crash risk. In the analysis of HGF events, CNC rate was positively related to HGF rate, indicating that drivers involved in more crashes also exhibited more risky maneuvers. Furthermore, contrast sensitivity was also linked to HGF rates. Notably, two metacognitive metrics—self-rated cognitive status and the disparity between self-rating and objective cognitive performance—were associated with HGF event rates. Higher HGF rates were linked to both higher self-ratings of cognitive status and larger gaps between self-perception and objective ability, suggesting that overconfidence or lack of insight into cognitive decline may contribute to risky driving behavior. The study concludes that specific visual and metacognitive metrics are crucial indicators of driving risk in older adults. These findings provide evidence-based tools for professionals involved in driving rehabilitation and licensing decisions. The authors recommend validating these results using larger datasets, such as those from the SHRP 2 Naturalistic Driving Study, to refine protocols for assessing senior driver fitness.

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Topics

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• fitness to drive assessment
• cognitive capacity variation
• exposure measurement
• older drivers
• telematics crash prediction
• naturalistic crash near crash

Information type

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• Methodological Resource: validation psychometrics, dataset resource
• Theoretical Contribution: computational model