Human-like motion planning model for driving in signalized intersections

Yanlei Gu, Yoriyoshi Hashimoto, Li Ta Hsu, Miho Iryo-Asano, Shunsuke Kamijo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

44 Citations (Scopus)


Highly automated and fully autonomous vehicles are much more likely to be accepted if they react in the same way as human drivers do, especially in a hybrid traffic situation, which allows autonomous vehicles and human-driven vehicles to share the same road. This paper proposes a human-like motion planning model to represent how human drivers assess environments and operate vehicles in signalized intersections. The developed model consists of a pedestrian intention detection model, gap detection model, and vehicle control model. These three submodels are individually responsible for situation assessment, decision making, and action, and also depend on each other in the process of motion planning. In addition, these submodels are constructed and learned on the basis of human drivers' data collected from real traffic environments. To verify the effectiveness of the proposed motion planning model, we compared the proposed model with actual human driver and pedestrian data. The experimental results showed that our proposed model and actual human driver behaviors are highly similar with respect to gap acceptance in intersections.

Original languageEnglish
Pages (from-to)129-139
Number of pages11
JournalIATSS Research
Issue number3
Publication statusPublished - 1 Oct 2017
Externally publishedYes


  • Autonomous vehicle
  • Gap acceptance
  • Motion planning
  • Pedestrian behavior
  • Signalized intersection

ASJC Scopus subject areas

  • Transportation
  • Safety Research
  • Urban Studies
  • General Engineering


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