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

Wenjing Cao, Masakazu Mukai, Taketoshi Kawabe

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
JournalArtificial Life and Robotics
DOIs
Publication statusPublished - Jan 1 2019

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence

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