• DocumentCode
    3681848
  • Title

    Driving Maneuvers Analysis Using Naturalistic Highway Driving Data

  • Author

    Guofa Li;Shengbo Eben Li;Lijuan Jia;Wenjun Wang;Bo Cheng;Fang Chen

  • Author_Institution
    Dept. of Automotive Eng., Tsinghua Univ., Beijing, China
  • fYear
    2015
  • Firstpage
    1761
  • Lastpage
    1766
  • Abstract
    Accounting about 70% of vehicle miles on roadways, highway driving is a critical issue in traffic safety deployment. Of the various maneuvers that comprise the highly complex driving task, each one requires understanding on the connections between driving states, vehicle performance and drivers´ actions. This paper attempts to flesh out a complete picture of driving maneuvers on highways. Eighteen drivers participated in this study. They drove an instrumented vehicle on highways to accumulate 2,600 km naturalistic driving data. The data were segmented and classified into 11 maneuver groups manually. Analysis on the maneuvers revealed that: 1) A maneuver transition probabilities model was proposed. According to this model, 7 typical driving patterns were drawn based on the transition probabilities. Transition events pertaining to approaching/following/lane changing accounted for 95% of all the highway transition events. 2) The durations were 7.6/6.6 s and 7.1/7.0 s for free left/right lane changes and overtake from left/right lane changes, respectively. The numbers were 22.5, 21.4 and 16.3 s for far, middle and near following maneuvers, respectively. Statistical significances were found within both groups. 3) How drivers behave in each maneuver was analyzed. Drivers drove faster in free lane changes than did in overtake lane changes. For overtake lane changes, two driving patterns were observed: accelerate to change lane and decelerate to change lane.
  • Keywords
    "Vehicles","Lead","Brakes","Cameras","Roads","Safety"
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on
  • ISSN
    2153-0009
  • Electronic_ISBN
    2153-0017
  • Type

    conf

  • DOI
    10.1109/ITSC.2015.286
  • Filename
    7313378