Modeling Collision Avoidance Field for Overtaking Moving Obstacles

Mohammed Mahmod Shuaib
{"title":"Modeling Collision Avoidance Field for Overtaking Moving Obstacles","authors":"Mohammed Mahmod Shuaib","doi":"10.1109/ACIT47987.2019.8991016","DOIUrl":null,"url":null,"abstract":"The capability of overtaking moving obstacles is an essential factor for accomplishing several aspects of pedestrian walking flow. In the Hajj area, for example, overtaking dynamic structures constituted by groups, barriers, and other moving obstacles is a vital phenomenon emerged while performing Hajj rituals. In emergency situation, the awareness of the dynamic behavior of moving obstacles is indispensable for achieving typical evacuation. This article proposes an essential intelligence approach to performing further realistic evacuation simulations. We provide each agent with the capability of selecting intermediate destination that enables him reaching his preferred destination; the agent continuously adapts his own trajectory that enables him to overtake such dynamic obstacles by selecting intermediate destinations to pass through. A collision avoidance field which composes of two-dimension grid of cells is proposed to cover the floor of the physical environment. The agent selects the optimal cells which achieve less potential of collision and minimize the distance to the original destination. The proposed model is integrated in a microscopic crowd dynamics model, and simulations are performed to examine the impact of the extended model on introducing further realistic and efficient evacuation.","PeriodicalId":314091,"journal":{"name":"2019 International Arab Conference on Information Technology (ACIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Arab Conference on Information Technology (ACIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACIT47987.2019.8991016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The capability of overtaking moving obstacles is an essential factor for accomplishing several aspects of pedestrian walking flow. In the Hajj area, for example, overtaking dynamic structures constituted by groups, barriers, and other moving obstacles is a vital phenomenon emerged while performing Hajj rituals. In emergency situation, the awareness of the dynamic behavior of moving obstacles is indispensable for achieving typical evacuation. This article proposes an essential intelligence approach to performing further realistic evacuation simulations. We provide each agent with the capability of selecting intermediate destination that enables him reaching his preferred destination; the agent continuously adapts his own trajectory that enables him to overtake such dynamic obstacles by selecting intermediate destinations to pass through. A collision avoidance field which composes of two-dimension grid of cells is proposed to cover the floor of the physical environment. The agent selects the optimal cells which achieve less potential of collision and minimize the distance to the original destination. The proposed model is integrated in a microscopic crowd dynamics model, and simulations are performed to examine the impact of the extended model on introducing further realistic and efficient evacuation.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
超车运动障碍物避碰场建模
超车能力是实现行人行走流的几个方面的关键因素。例如,在朝觐地区,超越由群体、障碍和其他移动障碍构成的动态结构是朝觐仪式中出现的重要现象。在紧急情况下,了解移动障碍物的动态行为是实现典型疏散的必要条件。本文提出了一种基本的智能方法来执行进一步的真实疏散模拟。我们为每个agent提供选择中间目的地的能力,使其能够到达自己的首选目的地;智能体不断调整自己的轨迹,通过选择中间目的地来超越这些动态障碍物。提出了一种由二维网格单元构成的避碰场,以覆盖整个物理环境。智能体选择碰撞可能性较小、到原目的地距离最小的最优单元。将该模型集成到微观人群动力学模型中,并进行了仿真,以检验扩展模型对引入更现实、更有效的疏散的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Loan Default Prediction Model Improvement through Comprehensive Preprocessing and Features Selection Privacy Preserving of Shared Data in Deep Learning Does Social Media Affects Users’ Well-Being [Copyright notice] Image Caption Generation Using A Deep Architecture
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1