The broad goal of this project is to create a publicly accessible naturalistic driving database, using novel methods to identify unsafe driving events, with the expectation that this methodology could also be adapted for more widespread clinical and experimental use. This project will leverage the wealth of knowledge and experience that has been gained from two previous projects examining real-world driving in normal aging and dementia, one employing serial road tests in a 3-year longitudinal study, the other involving naturalistic recordings of drivers with in-car video cameras studied over one year. We will use the large volume of baseline and one-year follow up digital video collection of naturalistic drivin from the second study (N=103), NIA 2R01 #AG016335, """"""""Naturalistic assessment of driving in cognitively impaired elders,"""""""" which could not be manually reviewed completely. The data will be re-analyzed in totality using new and highly innovative methods of computerized analysis of lane deviation and near miss events, and then scored by safety rating methods. This research project will also include secondary analyses of naturalistic driving data on older drivers with and without cognitive impairment comparing naturalistic driving to prior history of motor vehicle accidents and road test scores. The resulting bookmarked video and tabular data, including demographic, road test, and cognitive test information, will be a valuable naturalistic driving resource that will be archived and disseminated for other researchers to use in their own studies of older drivers. We will disseminate the archived naturalistic data through the National Archive of Computerized Data on Aging (NACDA) as well as notify the public of data availability on the research teams'websites and publications of secondary analyses. By proper and thorough de-identification of the video recordings, this data archive will facilitate the use of data that woul otherwise not be available due to the difficulty in obtaining such data as well as the sensitive nature of the data obtained.
As the baby boomer generation comes of age and is expected to demand and require a vigorous and mobile lifestyle in their advancing years, the public significance of this research is major. This study will provide researchers with a large naturalistic driving database of normal and cognitively impaired elders including bookmarked video adverse driving events that will be analyzed in detail. Results from this study will assist future researchers in targeting the real life problems of unsafe driving encountered in this group through direct observations. DISCLAIMER: Please note that the following critiques were prepared by the reviewers prior to the Study Section meeting and are provided in an essentially unedited form. While there is opportunity for the reviewers to update or revise their written evaluation, based upon the group's discussion, there is no guarantee that individual critiques have been updated subsequent to the discussion at the meeting. Therefore, the critiques may not fully reflect the final opinions of the individual reviewers at the close of group discussion or the final majority opinion of the group. Thus, the Resume and Summary of Discussion is the final word on what the reviewers actually considered critical at the meeting.
| Wang, Shuhang; Ott, Brian R; Luo, Gang (2018) Detection of Lane-Change Events in Naturalistic Driving Videos. Intern J Pattern Recognit Artif Intell 32: |
| Davis, Jennifer D; Wang, Shuhang; Festa, Elena K et al. (2018) Detection of Risky Driving Behaviors in the Naturalistic Environment in Healthy Older Adults and Mild Alzheimer's Disease. Geriatrics (Basel) 3: |
| Roe, Catherine M; Barco, Peggy P; Head, Denise M et al. (2017) Amyloid Imaging, Cerebrospinal Fluid Biomarkers Predict Driving Performance Among Cognitively Normal Individuals. Alzheimer Dis Assoc Disord 31:69-72 |
| Ott, Brian R; Davis, Jennifer D; Bixby, Kimberly (2017) Video Feedback Intervention to Enhance the Safety of Older Drivers With Cognitive Impairment. Am J Occup Ther 71:7102260020p1-7102260020p7 |
| Ott, Brian R; Jones, Richard N; Noto, Richard B et al. (2017) Brain amyloid in preclinical Alzheimer's disease is associated with increased driving risk. Alzheimers Dement (Amst) 6:136-142 |
