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Merging trajectory generation method using real-time optimization with enhanced robustness against sensor noise

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Abstract

To reduce drivers’ mental load and traffic congestion caused by merging maneuver, a merging trajectory generation method aiming for practical automatic driving was proposed in the past research by the authors. In this paper, the robustness of the method against sensor noises is enhanced. The robustness is improved by the dummy optimization variables that relax the equality constraints and the barrier functions. The stage costs composed by these introduced dummy variables are designed to generate safe and smooth merging maneuver. The effectiveness of the proposed method for a typical case is observed in the simulation results. To check if the proposed method works well under different initial conditions, 116 initial conditions are generated randomly. The proposed method solves all the cases of merging problem, while the conventional method fails in 80% of the cases.

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Authors and Affiliations

  1. Department of Engineering and Applied Sciences, Faculty of Science and Technology, Sophia University, Tokyo, Japan

    Wenjing Cao

  2. Department of Electrical and Electronic Engineering, Faculty of Engineering, Kogakuin University, Tokyo, Japan

    Masakazu Mukai

  3. Department of Electrical Engineering, Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

    Taketoshi Kawabe

Authors
  1. Wenjing Cao
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  2. Masakazu Mukai
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  3. Taketoshi Kawabe
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Correspondence to Wenjing Cao.

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Cao, W., Mukai, M. & Kawabe, T. Merging trajectory generation method using real-time optimization with enhanced robustness against sensor noise. Artif Life Robotics 24, 527–533 (2019). https://doi.org/10.1007/s10015-019-00546-w

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  • DOI: https://doi.org/10.1007/s10015-019-00546-w

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