ABSTRACT Problem As our driving population continues to age, it is becoming increasingly important to find a small set of easily administered fitness metrics that can meaningfully and reliably identify at-risk seniors requiring more in-depth evaluation of their driving skills and weaknesses. Method Sixty driver assessment metrics related to fitness-to-drive were examined for 20 seniors who were followed for a year using the naturalistic driving paradigm. Principal component analysis and negative binomial regression modeling approaches were used to develop parsimonious models relating the most highly predictive of the driver assessment metrics to the safety-related outcomes observed in the naturalistic driving data. Results This study provides important confirmation using naturalistic driving methods of the relationship between contrast sensitivity and crash-related events. Practical applications The results of this study provide crucial information on the continuing journey to identify metrics and protocols that could be applied to determine seniors' fitness to drive.

    HIGHLIGHTS Fitness-to-drive (FD) and driving data were collected from a sample of seniors. Driving data were collected via the naturalistic driving observation approach. FD metrics sampled cognitive, perceptual, psychomotor, and physical dimensions. Principal component analysis and negative binomial regression approaches were used to model the data. Contrast sensitivity was significantly associated with safety-related event risk.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:

    Published in:

    Publication date :

    2015-06-24




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English







    Driver Head Pose Detection From Naturalistic Driving Data

    Chai, Weiheng / Chen, Jiajing / Wang, Jiyang et al. | IEEE | 2023


    Using SHRP2 naturalistic driving data to examine driver speeding behavior

    Richard, Christian M. / Lee, Joonbum / Atkins, Randolph et al. | Elsevier | 2020