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A Stochastic Approach for Modeling Lane-Change Trajectories

  • Yoshihiro Nishiwaki
  • Chiyomi Miyajima
  • Norihide Kitaoka
  • Kazuya Takeda
Chapter

Abstract

A signal-processing approach for modeling vehicle trajectory during lane changes while driving is discussed. Since individual driving habits are not a deterministic process, we develop a stochastic method to model them. The proposed model consists of two parts: a dynamic system represented by a hidden Markov model and a cognitive distance space represented with a hazard-map function. The first part models the local dynamics of vehicular movements and generates a set of probable trajectories. The second part selects an optimal trajectory by stochastically evaluating the distances from surrounding vehicles. Through experimental evaluation, we show that the model can predict vehicle trajectory in given traffic conditions with a prediction error of 17.6m.

Keywords

Driving behavior Generation Hazard map Hidden Markov model (HMM) Lane change Prediction Sampling Stochastic modeling 

Notes

Acknowledgments

This work was supported by the Strategic Information and Communications R&D Promotion Program (SCOPE) of the Ministry of Internal Affairs and Communications of Japan and by the Core Research for Evolutional Science and Technology (CREST) of the Japan Science and Technology Agency. We are also grateful to the members of these projects for their valuable comments.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Yoshihiro Nishiwaki
    • 1
  • Chiyomi Miyajima
    • 1
  • Norihide Kitaoka
    • 1
  • Kazuya Takeda
    • 1
  1. 1.Graduate School of Information ScienceNagoya UniversityNagoyaJapan

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