{"id":754,"date":"2020-10-17T12:00:31","date_gmt":"2020-10-17T10:00:31","guid":{"rendered":"https:\/\/project.inria.fr\/crowdscience\/?p=754"},"modified":"2020-11-12T14:02:51","modified_gmt":"2020-11-12T13:02:51","slug":"publication-mig-2020-extreme-density-crowd-simulation","status":"publish","type":"post","link":"https:\/\/project.inria.fr\/crowdscience\/publication-mig-2020-extreme-density-crowd-simulation\/","title":{"rendered":"Publication (MIG 2020): Extreme-Density Crowd Simulation"},"content":{"rendered":"<p>(This paper has been published in the 2020 <em>ACM SIGGRAPH Conference on Motion, Interaction and Games<\/em>.)<\/p>\n<h4>Abstract:<\/h4>\n<p>In <strong>highly dense crowds of humans<\/strong>, collisions between people occur often. It is common to simulate such a crowd as one fluid-like entity (macroscopic), and not as a set of individuals (microscopic, agent-based). Agent-based simulations are preferred for lower densities because they preserve the properties of individual people. However, their collision handling is too simplistic for extreme-density crowds. Therefore, neither paradigm is ideal for all possible densities.<\/p>\n<p>In this paper, we combine agent-based crowd simulation with the concept of <strong>Smoothed Particle Hydrodynamics (SPH)<\/strong>, a particle-based method that is popular for fluid simulation. Our combination augments the usual agent-collision handling with fluid dynamics when the crowd density is sufficiently high. A novel component of our method is a <strong>dynamic rest density per agent<\/strong>, which intuitively controls the crowd density that an agent is willing to accept.<\/p>\n<p>Experiments show that SPH improves agent-based simulation in several ways: better <strong>stability<\/strong> at high densities, more intuitive <strong>control over the crowd density<\/strong>, and easier replication of <strong>wave-propagation effects<\/strong>. Our implementation can simulate tens of thousands of agents in real-time. As such, this work successfully prepares the agent-based paradigm for crowd simulation at all densities.<\/p>\n<h4>Video:<\/h4>\n<p><iframe loading=\"lazy\" width=\"900\" height=\"506\" src=\"https:\/\/www.youtube.com\/embed\/f-4JrPq-jn4?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen><\/iframe><\/p>\n<h4>Details:<\/h4>\n<ul>\n<li>Title: <strong>Extreme-Density Crowd Simulation: Combining Agents with Smoothed Particle Hydrodynamics<\/strong><\/li>\n<li>Authors: Wouter van Toll, C\u00e9dric Braga, Barbara Solenthaler, Julien Pettr\u00e9<\/li>\n<li>Download the full article:\u00a0 <a href=\"https:\/\/hal.inria.fr\/hal-02964064\">https:\/\/hal.inria.fr\/hal-02964064<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>(This paper has been published in the 2020 ACM SIGGRAPH Conference on Motion, Interaction and Games.) Abstract: In highly dense crowds of humans, collisions between people occur often. It is common to simulate such a crowd as one fluid-like entity (macroscopic), and not as a set of individuals (microscopic, agent-based).\u2026<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/project.inria.fr\/crowdscience\/publication-mig-2020-extreme-density-crowd-simulation\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":1604,"featured_media":756,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_members_access_role":[],"_members_access_error":""},"categories":[14,5],"tags":[],"class_list":["post-754","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-publications"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/crowdscience\/wp-json\/wp\/v2\/posts\/754","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/crowdscience\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/project.inria.fr\/crowdscience\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/crowdscience\/wp-json\/wp\/v2\/users\/1604"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/crowdscience\/wp-json\/wp\/v2\/comments?post=754"}],"version-history":[{"count":2,"href":"https:\/\/project.inria.fr\/crowdscience\/wp-json\/wp\/v2\/posts\/754\/revisions"}],"predecessor-version":[{"id":758,"href":"https:\/\/project.inria.fr\/crowdscience\/wp-json\/wp\/v2\/posts\/754\/revisions\/758"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/crowdscience\/wp-json\/wp\/v2\/media\/756"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/crowdscience\/wp-json\/wp\/v2\/media?parent=754"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/project.inria.fr\/crowdscience\/wp-json\/wp\/v2\/categories?post=754"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/project.inria.fr\/crowdscience\/wp-json\/wp\/v2\/tags?post=754"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}