{"version":"1.0","provider_name":"2025 Group 13 Project (P13)","provider_url":"https:\/\/mscvprojects.ri.cmu.edu\/2025team6","title":"Introduction - 2025 Group 13 Project (P13)","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"wD5mmY7rUC\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team6\/\">Introduction<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team6\/embed\/#?secret=wD5mmY7rUC\" width=\"600\" height=\"338\" title=\"&#8220;Introduction&#8221; &#8212; 2025 Group 13 Project (P13)\" data-secret=\"wD5mmY7rUC\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/mscvprojects.ri.cmu.edu\/2025team6\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","description":"Overview This project investigates vision\u2013language\u2013augmented multi-modal, multi-agent motion prediction for autonomous driving. We integrate agent history, inter-agent relationships, camera and LiDAR perception, and explicit traffic-rule reasoning distilled from vision\u2013language models (VLMs) to improve safety and robustness in complex traffic scenes. Unlike traditional motion prediction methods that rely primarily on historical trajectories or visual appearance, our [&hellip;]","thumbnail_url":"https:\/\/mscvprojects.ri.cmu.edu\/2025team6\/wp-content\/uploads\/sites\/129\/2025\/12\/mult-agent_motion_prediction.png","thumbnail_width":1736,"thumbnail_height":998}