• DocumentCode
    181660
  • Title

    Assessment and prediction of older drivers´ driving performance

  • Author

    Nakano, Yoshiaki ; Sano, Shumpei ; Yamakage, Yuzuru ; Kojima, T. ; Kishi, Chika ; Takahasi, Chisa ; Iribe, Yurie ; Kawanaka, Haruki ; Oguri, Koji

  • Author_Institution
    Fujitsu Labs. Ltd., Kawasaki, Japan
  • fYear
    2014
  • fDate
    8-11 June 2014
  • Firstpage
    164
  • Lastpage
    169
  • Abstract
    Traffic accidents involving older drivers have been increasing all over the world. In order to assess elder driving performance and predict the risk of traffic accidents, we analyzed data from specific license renewal tests that are obligatory for Japanese drivers aged 70 years old or older, which includes a driving simulator test and an on-road test. As a result of the analysis, we found that aging affects several test results, such as the percentage of correct answers and the reaction times in multiple judgment tasks tests. In order to be able to classify a driver as a high accident risk, we performed an outlier analysis using a one-class SVM to investigate performance characteristics, and also performed a logistics regression analysis. Using parameters strongly related to cognitive decline, we found a viable way to classify impaired drivers. Driving is a complex task requiring integration of cognition, judgment, and operation skills. Deterioration of these skills is likely to increase the risk of traffic accidents. Although our final objective was to support elderly drivers suffering such deterioration, we initially studied a measurement method to detect the area and extent of deterioration effectively.
  • Keywords
    behavioural sciences computing; cognition; regression analysis; risk analysis; road accidents; support vector machines; traffic engineering computing; Japanese drivers; SVM; cognition integration; driving simulator test; elder driving performance; license renewal tests; logistic regression analysis; multiple judgment task tests; older driver; on-road test; traffic accidents; Aging; Licenses; Logistics; Mathematical model; Senior citizens; Support vector machines; Vehicles; Alzheimer´s disease; Driver Performance; Driving simulator test; Logistic regression analysis; Older Driver; On-road Driving test; One-class SVM;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Vehicles Symposium Proceedings, 2014 IEEE
  • Conference_Location
    Dearborn, MI
  • Type

    conf

  • DOI
    10.1109/IVS.2014.6856448
  • Filename
    6856448