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A hybrid model for lane change prediction with V2X-based driver assistance

Author

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  • Xu, Ting
  • Jiang, Ruisen
  • Wen, Changlei
  • Liu, Meijun
  • Zhou, Jiehan

Abstract

This paper proposes a hybrid model for lane-change prediction, with V2X-based driver assistance (HM4LCP) for improving driving safety. We carried out experiments in the testbed of Chang’an University to investigate driving behaviors on an urban road. 412 lane-changing units and 824 lane-keeping units were obtained from the vehicle trajectories. The HM4LCP integrates the Gaussian Mixture Model and the Continuous Hidden Markov Model to identify and predict lane-changing behaviors within a V2X environment. We collected 412 lane-changing samples and randomly chose 140, in order to test the HM4LCP’s identification accuracy. The results show that the HM4LCP results in an accuracy as high as 93.6% for lane-changing and 90.4% for lane-keeping, respectively. The proposed HM4LCP method can be applied to the driving assistant systems for lane-change warnings, in the initial stage.

Suggested Citation

  • Xu, Ting & Jiang, Ruisen & Wen, Changlei & Liu, Meijun & Zhou, Jiehan, 2019. "A hybrid model for lane change prediction with V2X-based driver assistance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    • Handle: RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119311707
    DOI: 10.1016/j.physa.2019.122033
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    References listed on IDEAS

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    Cited by:

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