The impact of obstacle’s risk in pedestrian agent’s local path-planning

Thanh Trung Trinh, Masaomi Kimura

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


While the risk from the obstacle could significantly alter the navigation path of a pedestrian, this problem is often disregarded by many studies in pedestrian simulation, or is hindered by a simplistic simulation approach. To address this problem, we proposed a novel simulation model for the local path-planning process of the pedestrian agent, adopting reinforcement learning to replicate the navigation path. We also addressed the problem of assessing the obstacle’s risk by determining its probability of collision with the obstacle, combining with the danger from the obstacle. This process is subsequently incorporated with our prediction model to provide an accurate navigation path similar to the human thinking process. Our proposed model’s implementation demonstrates a more favorable result than other simulation models, especially in the case of the obstacle’s appearance. The pedestrian agent is capable of assessing the risk from the obstacle in different situations and adapting the navigation path correspondingly.

Original languageEnglish
Article number5442
JournalApplied Sciences (Switzerland)
Issue number12
Publication statusPublished - 2021 Jun 2


  • Agent
  • Path planning
  • Pedestrian
  • Reinforcement learning
  • Risk assessment
  • Simulation

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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