A Pedestrian Path-planning Model in Accordance with Obstacle's Danger with Reinforcement Learning

Thanh Trung Trinh, Dinh Minh Vu, Masaomi Kimura

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Most microscopic pedestrian navigation models use the concept of "forces"applied to the pedestrian agents to replicate the navigation environment. While the approach could provide believable results in regular situations, it does not always resemble natural pedestrian navigation behaviour in many typical settings. In our research, we proposed a novel approach using reinforcement learning for simulation of pedestrian agent path planning and collision avoidance problem. The primary focus of this approach is using human perception of the environment and danger awareness of interferences. The implementation of our model has shown that the path planned by the agent shares many similarities with a human pedestrian in several aspects such as following common walking conventions and human behaviours.

本文言語English
ホスト出版物のタイトルProceedings of the 2020 3rd International Conference on Information Science and System, ICISS 2020
出版社Association for Computing Machinery
ページ115-120
ページ数6
ISBN(電子版)9781450377256
DOI
出版ステータスPublished - 2020 3 19
イベント3rd International Conference on Information Science and System, ICISS 2020 - Virtual, Online, United Kingdom
継続期間: 2020 3 192020 3 22

出版物シリーズ

名前ACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Information Science and System, ICISS 2020
国/地域United Kingdom
CityVirtual, Online
Period20/3/1920/3/22

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識
  • ソフトウェア

フィンガープリント

「A Pedestrian Path-planning Model in Accordance with Obstacle's Danger with Reinforcement Learning」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル