• DocumentCode
    574519
  • Title

    Driving course prediction for vehicle handling maneuvers

  • Author

    Ruoqian Liu ; Hai Yu ; McGee, Ryan ; Murphey, Yi L.

  • Author_Institution
    Dept. of Electr. Eng., Univ. of Michigan-Dearborn, Dearborn, MI, USA
  • fYear
    2012
  • fDate
    27-29 June 2012
  • Firstpage
    2096
  • Lastpage
    2101
  • Abstract
    This paper aims at predicting the future driving course, which we define as a combination of two bifurcating channels - future speed and steering action that in turn derive a future driving trajectory during a curve. In defining the relation of these two channels, human factors, such as the stressfulness, comfort level, and skillfulness of the driver, are paid particular attention to. While the modeling and forecast of speed and steering angle are to some extent separated, a hidden Markov model (HMM) that´s designed to mimic driver´s intention integrates them by making subjective corrections. The proposed algorithm has been proved effective on realistic driving data based on a prototype vehicle at Ford.
  • Keywords
    hidden Markov models; steering systems; vehicle dynamics; bifurcating channel; comfort level; driving course prediction; future driving trajectory; hidden Markov model; prototype vehicle; realistic driving data; skillfulness; steering action; steering angle; vehicle handling maneuver; Acceleration; Biological system modeling; Hidden Markov models; Predictive models; Roads; Time series analysis; Vehicles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference (ACC), 2012
  • Conference_Location
    Montreal, QC
  • ISSN
    0743-1619
  • Print_ISBN
    978-1-4577-1095-7
  • Electronic_ISBN
    0743-1619
  • Type

    conf

  • DOI
    10.1109/ACC.2012.6315104
  • Filename
    6315104