• DocumentCode
    3240515
  • Title

    MANFIS-based overtaking maneuver modeling and prediction of a Driver-Vehicle-Unit in real traffic flow

  • Author

    Ghaffari, A. ; Khodayari, A. ; Alimardani, F. ; Sadati, H.

  • Author_Institution
    Mech. Eng. Dept., K.N. Toosi Univ. of Technol., Tehran, Iran
  • fYear
    2012
  • fDate
    24-27 July 2012
  • Firstpage
    387
  • Lastpage
    392
  • Abstract
    The purpose of this study is to design multiple-input multiple-output ANFIS (MANFIS) models to simulate and predict the future state of the overtaking maneuver in real traffic flow for four different time steps ahead. These models are designed to predict the behavior for 1, 2, 4 and 6 time steps ahead. Each time step is equal to 0.1 second. In these models, important factors such as distance, velocity, acceleration and the movement angle of the overtaking vehicle are considered. In these models, for all the variables, instantaneous values are used and none of them is considered constant. The presented models predict the future value of the acceleration and the movement angle of the overtaking vehicle. These models are designed based on the real traffic data and validated at the microscopic level. The results show very close agreement between field data and models outputs. The proposed models can be employed ITS applications and the like.
  • Keywords
    automated highways; fuzzy neural nets; fuzzy reasoning; ITS applications; MANFIS models; MANFIS-based overtaking maneuver modeling; adaptive neuro-fuzzy inference systems; driver-vehicle-unit; movement angle; multiple-input multiple-output ANFIS; overtaking vehicle; real traffic flow; Acceleration; Adaptation models; Data models; Mathematical model; Predictive models; Trajectory; Vehicles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Vehicular Electronics and Safety (ICVES), 2012 IEEE International Conference on
  • Conference_Location
    Istanbul
  • Print_ISBN
    978-1-4673-0992-9
  • Type

    conf

  • DOI
    10.1109/ICVES.2012.6294269
  • Filename
    6294269