{"id":74,"date":"2024-08-08T10:18:42","date_gmt":"2024-08-08T02:18:42","guid":{"rendered":"http:\/\/110.42.248.92\/onsite-learning-center\/?page_id=74"},"modified":"2024-09-05T21:10:01","modified_gmt":"2024-09-05T13:10:01","slug":"%e6%95%b0%e6%8d%ae%e9%9b%86%e8%af%a6%e6%83%85-%e6%9c%ba%e5%8a%a8%e8%bd%a6%e9%a2%84%e6%b5%8b%e4%bb%bb%e5%8a%a1","status":"publish","type":"page","link":"http:\/\/110.42.248.92\/onsite-learning-center\/en\/%e8%b5%84%e6%ba%90%e5%ba%93\/%e6%95%b0%e6%8d%ae%e9%9b%86%e6%a6%82%e8%bf%b0\/%e6%95%b0%e6%8d%ae%e9%9b%86%e8%af%a6%e6%83%85-%e9%a2%84%e6%b5%8b\/%e6%95%b0%e6%8d%ae%e9%9b%86%e8%af%a6%e6%83%85-%e6%9c%ba%e5%8a%a8%e8%bd%a6%e9%a2%84%e6%b5%8b%e4%bb%bb%e5%8a%a1\/","title":{"rendered":"Dataset Details\u2014Motor Vehicle Prediction Task"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container has-pattern-background has-mask-background nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" 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fusion-content-layout-column\"><nav class=\"awb-menu awb-menu_column awb-menu_em-click mobile-mode-collapse-to-button awb-menu_icons-left awb-menu_dc-yes mobile-trigger-fullwidth-off awb-menu_mobile-toggle mobile-size-full-absolute loading mega-menu-loading awb-menu_desktop awb-menu_v-stacked awb-menu_em-always\" 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aria-label=\"Datasets\" data-breakpoint=\"640\" data-count=\"0\" data-transition-type=\"fade\" data-transition-time=\"300\"><button type=\"button\" class=\"awb-menu__m-toggle\" aria-expanded=\"false\" aria-controls=\"menu-%e6%95%b0%e6%8d%ae%e9%9b%86\"><span class=\"awb-menu__m-toggle-inner\"><span class=\"collapsed-nav-text\">Menu<\/span><span class=\"awb-menu__m-collapse-icon\"><span class=\"awb-menu__m-collapse-icon-open fa-bars fas\"><\/span><span class=\"awb-menu__m-collapse-icon-close fa-times fas\"><\/span><\/span><\/span><\/button><ul id=\"menu-%e6%95%b0%e6%8d%ae%e9%9b%86\" class=\"fusion-menu awb-menu__main-ul awb-menu__main-ul_column\"><li  id=\"menu-item-160\"  class=\"menu-item menu-item-type-post_type menu-item-object-page menu-item-160 awb-menu__li awb-menu__main-li awb-menu__main-li_regular\"  data-item-id=\"160\"><span class=\"awb-menu__main-background-default awb-menu__main-background-default_fade\"><\/span><span class=\"awb-menu__main-background-active awb-menu__main-background-active_fade\"><\/span><a  href=\"http:\/\/110.42.248.92\/onsite-learning-center\/en\/%e8%b5%84%e6%ba%90%e5%ba%93\/%e6%95%b0%e6%8d%ae%e9%9b%86%e6%a6%82%e8%bf%b0\/%e6%95%b0%e6%8d%ae%e9%9b%86%e6%a6%82%e8%bf%b0-%e6%84%9f%e7%9f%a5%e9%97%ae%e9%a2%98\/\" class=\"awb-menu__main-a awb-menu__main-a_regular\"><span class=\"menu-text\">Perception<\/span><\/a><\/li><li  id=\"menu-item-117\"  class=\"menu-item menu-item-type-post_type menu-item-object-page menu-item-117 awb-menu__li awb-menu__main-li awb-menu__main-li_regular\"  data-item-id=\"117\"><span class=\"awb-menu__main-background-default awb-menu__main-background-default_fade\"><\/span><span class=\"awb-menu__main-background-active awb-menu__main-background-active_fade\"><\/span><a  href=\"http:\/\/110.42.248.92\/onsite-learning-center\/en\/%e8%b5%84%e6%ba%90%e5%ba%93\/%e6%95%b0%e6%8d%ae%e9%9b%86%e6%a6%82%e8%bf%b0\/%e6%95%b0%e6%8d%ae%e9%9b%86%e8%af%a6%e6%83%85-%e9%a2%84%e6%b5%8b\/\" class=\"awb-menu__main-a awb-menu__main-a_regular\"><span class=\"menu-text\">Prediction<\/span><\/a><\/li><li  id=\"menu-item-159\"  class=\"menu-item menu-item-type-post_type menu-item-object-page menu-item-159 awb-menu__li awb-menu__main-li awb-menu__main-li_regular\"  data-item-id=\"159\"><span class=\"awb-menu__main-background-default awb-menu__main-background-default_fade\"><\/span><span class=\"awb-menu__main-background-active awb-menu__main-background-active_fade\"><\/span><a  href=\"http:\/\/110.42.248.92\/onsite-learning-center\/en\/%e8%b5%84%e6%ba%90%e5%ba%93\/%e6%95%b0%e6%8d%ae%e9%9b%86%e6%a6%82%e8%bf%b0\/%e6%95%b0%e6%8d%ae%e9%9b%86%e6%a6%82%e8%bf%b0-%e5%86%b3%e7%ad%96%e8%a7%84%e5%88%92%e9%97%ae%e9%a2%98\/\" class=\"awb-menu__main-a awb-menu__main-a_regular\"><span class=\"menu-text\">Decision-Making and Planning<\/span><\/a><\/li><li  id=\"menu-item-158\"  class=\"menu-item menu-item-type-post_type menu-item-object-page menu-item-158 awb-menu__li awb-menu__main-li awb-menu__main-li_regular\"  data-item-id=\"158\"><span class=\"awb-menu__main-background-default awb-menu__main-background-default_fade\"><\/span><span class=\"awb-menu__main-background-active awb-menu__main-background-active_fade\"><\/span><a  href=\"http:\/\/110.42.248.92\/onsite-learning-center\/en\/%e8%b5%84%e6%ba%90%e5%ba%93\/%e6%95%b0%e6%8d%ae%e9%9b%86%e6%a6%82%e8%bf%b0\/%e6%95%b0%e6%8d%ae%e9%9b%86%e6%a6%82%e8%bf%b0-%e6%8e%a7%e5%88%b6%e9%97%ae%e9%a2%98\/\" class=\"awb-menu__main-a awb-menu__main-a_regular\"><span class=\"menu-text\">Control<\/span><\/a><\/li><\/ul><\/nav><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-1 fusion_builder_column_4_5 4_5 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:80%;--awb-margin-top-large:0px;--awb-spacing-right-large:2.4%;--awb-margin-bottom-large:20px;--awb-spacing-left-large:2.4%;--awb-width-medium:80%;--awb-order-medium:0;--awb-spacing-right-medium:2.4%;--awb-spacing-left-medium:2.4%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-title title fusion-title-1 fusion-sep-none fusion-title-text fusion-title-size-three\"><h3 class=\"fusion-title-heading title-heading-left\" style=\"margin:0;\">Motor-Vehicle Trajectory Prediction Task<\/h3><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"align-self: flex-start;margin-right:auto;width:100%;\"><div class=\"fusion-separator-border sep-wavy\" style=\"--awb-height:20px;--awb-amount:20px;--awb-separator-pattern-url:url(&quot;data:image\/svg+xml;utf8,%3Csvg%20xmlns%3D%22http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%22%20preserveAspectRatio%3D%22none%22%20overflow%3D%22visible%22%20height%3D%22100%25%22%20viewBox%3D%220%200%2024%2024%22%20fill%3D%22none%22%20stroke%3D%22black%22%20stroke-width%3D%221%22%20stroke-linecap%3D%22square%22%20stroke-miterlimit%3D%2210%22%3E%3Cpath%20d%3D%22M0%2C6c6%2C0%2C0.9%2C11.1%2C6.9%2C11.1S18%2C6%2C24%2C6%22%2F%3E%3C%2Fsvg%3E&quot;);border-color:var(--awb-color3);\"><\/div><\/div><ul style=\"--awb-odd-row-bgcolor:rgba(173,216,230,0);--awb-line-height:27.2px;--awb-icon-width:27.2px;--awb-icon-height:27.2px;--awb-icon-margin:11.2px;--awb-content-margin:38.4px;--awb-circlecolor:#add8e6;--awb-circle-yes-font-size:14.08px;\" class=\"fusion-checklist fusion-checklist-1 fusion-checklist-divider type-icons\"><li class=\"fusion-li-item\" style=\"\"><span class=\"icon-wrapper circle-yes\"><i class=\"fusion-li-icon fa-calendar-check far\" aria-hidden=\"true\"><\/i><\/span><div class=\"fusion-li-item-content\">\n<p style=\"line-height: 1.8;font-size: 18px\"><strong>Definition:<\/strong>The behavior of vehicles around an autonomous vehicle is uncertain. Motor-vehicle trajectory prediction aims to predict future trajectories using the historical motion trajectories of other road vehicles and map information.<\/p>\n<\/div><\/li><li class=\"fusion-li-item\" style=\"\"><span class=\"icon-wrapper circle-yes\"><i class=\"fusion-li-icon fa-laugh far\" aria-hidden=\"true\"><\/i><\/span><div class=\"fusion-li-item-content\">\n<p style=\"line-height: 1.8;font-size: 18px\"><strong>Impact:<\/strong>Inaccurate trajectory prediction may cause delayed or erroneous responses from autonomous driving systems, increasing the risk of traffic accidents. Especially in complex and changing traffic environments, accurate trajectory prediction can help autonomous vehicles plan their paths in advance, reduce unnecessary emergency braking or evasive maneuvers, and avoid potential collision risks. It can significantly improve the safety and reliability of autonomous driving while enhancing the passenger experience.<\/p>\n<\/div><\/li><li class=\"fusion-li-item\" style=\"\"><span class=\"icon-wrapper circle-yes\"><i class=\"fusion-li-icon fa-exchange-alt fas\" aria-hidden=\"true\"><\/i><\/span><div class=\"fusion-li-item-content\">\n<p style=\"line-height: 1.8;font-size: 18px\"><strong>Inputs and Outputs:<\/strong>Typically, motor vehicle trajectory prediction tasks take as input the recent state information (1 s or 3 s) of surrounding vehicles\u2014possibly including coordinates, speed, and heading\u2014as well as map information, which may include lanes, signs, markings, and traffic lights; they output vehicle state information for a future period (3 s or 5 s), such as trajectory coordinates or distributions.<\/p>\n<\/div><\/li><\/ul><div class=\"fusion-section-separator section-separator splash fusion-section-separator-1\" 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);height:65px;\"><\/div><\/div><div class=\"fusion-section-separator-spacer\"><div class=\"fusion-section-separator-spacer-height\"><\/div><\/div><\/div><style type='text\/css'>.reading-box-container-1 .element-bottomshadow:before,.reading-box-container-1 .element-bottomshadow:after{opacity:0.7;}<\/style><div class=\"fusion-reading-box-container reading-box-container-1\" style=\"--awb-title-color:var(--awb-color8);--awb-title-font-size:24px;--awb-title-line-height:1.8px;--awb-content-font-size:18px;--awb-margin-top:0px;--awb-margin-bottom:20px;--awb-title-font-family:&quot;Outfit&quot;;--awb-title-font-style:normal;--awb-title-font-weight:700;\"><div class=\"reading-box\" style=\"background-color:var(--awb-color2);border-width:1px;border-color:rgba(226,226,226,0);border-left-width:3px;border-left-color:var(--primary_color);border-style:solid;\"><h2>Available Datasets<\/h2><div class=\"reading-box-additional fusion-reading-box-additional\">\n<ul style=\"line-height: 1.8;font-size: 18px\">\n<li><a href=\"#nuScenes\">nuScenes<\/a><\/li>\n<li><a href=\"#Argoverse\">Argoverse<\/a><\/li>\n<li><a href=\"#highD\">highD<\/a><\/li>\n<li><a href=\"#Waymo\">Waymo<\/a><\/li>\n<li><a href=\"#int\">Interaction<\/a><\/li>\n<\/ul>\n<\/div><div class=\"fusion-clearfix\"><\/div><\/div><\/div><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-2 fusion_builder_column_1_5 1_5 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:20%;--awb-margin-top-large:0px;--awb-spacing-right-large:9.6%;--awb-margin-bottom-large:20px;--awb-spacing-left-large:9.6%;--awb-width-medium:20%;--awb-order-medium:0;--awb-spacing-right-medium:9.6%;--awb-spacing-left-medium:9.6%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><\/div><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-3 fusion_builder_column_4_5 4_5 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:80%;--awb-margin-top-large:0px;--awb-spacing-right-large:2.4%;--awb-margin-bottom-large:20px;--awb-spacing-left-large:2.4%;--awb-width-medium:80%;--awb-order-medium:0;--awb-spacing-right-medium:2.4%;--awb-spacing-left-medium:2.4%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-title title fusion-title-2 fusion-sep-none fusion-title-text fusion-title-size-three\" style=\"--awb-font-size:24px;\"><h3 class=\"fusion-title-heading title-heading-left\" style=\"font-family:&quot;Outfit&quot;;font-style:normal;font-weight:700;margin:0;font-size:1em;line-height:1;\">Dataset Details<\/h3><\/div><div class=\"fusion-text fusion-text-1\" style=\"--awb-text-color:var(--awb-color8);\" id=\"ng\"><ul style=\"line-height: 1.8;font-size: 18px\">\n<li><strong>NGSIM<\/strong><\/li>\n<\/ul>\n<p style=\"line-height: 1.8;font-size: 18px;padding-left: 80px\">The NGSIM (Next Generation Simulation) dataset consists of U.S. highway traffic data collected by the Federal Highway Administration (FHWA). It includes the driving conditions of all vehicles on roads such as US-101 during peak hours. The data are sampled at 5 Hz. Link:<a href=\"https:\/\/datahub.transportation.gov\/stories\/s\/Next-Generation-Simulation-NGSIM-Open-Data\/i5zb-xe34\/\">https:\/\/datahub.transportation.gov\/stories\/s\/Next-Generation-Simulation-NGSIM-Open-Data\/i5zb-xe34\/<\/a><\/p>\n<\/div><div class=\"fusion-text fusion-text-2\" id=\"highD\"><ul style=\"line-height: 1.8;font-size: 18px\">\n<li><strong>HighD<\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;line-height: 1.8;font-size: 18px\">The HighD dataset was released by the Institute for Automotive Engineering at RWTH Aachen University, Germany. It contains real vehicle trajectory data collected on German highways, including high-precision position, speed, and acceleration information for numerous vehicles, including cars and trucks, at six locations. The data are sampled at 10 Hz. Link:<a href=\"https:\/\/levelxdata.com\/highd-dataset\/\">https:\/\/levelxdata.com\/highd-dataset\/<\/a><\/p>\n<\/div><div class=\"fusion-text fusion-text-3\" id=\"Waymo\"><ul style=\"line-height: 1.8;font-size: 18px\">\n<li><strong>Waymo<\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;line-height: 1.8;font-size: 18px\">The Waymo dataset is an autonomous-vehicle dataset released by Waymo. Waymo Motion contains trajectory information for nearby motor vehicles, non-motor vehicles, and pedestrians while autonomous vehicles travel on urban roads, highways, rural roads, and other areas. The data are sampled at 10 Hz, with each segment averaging 20 s. Link:<a href=\"https:\/\/waymo.com\/open\">https:\/\/waymo.com\/open<\/a><\/p>\n<\/div><div class=\"fusion-text fusion-text-4\" id=\"nuScenes\"><ul style=\"line-height: 1.8;font-size: 18px\">\n<li><strong>nuScenes <\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;line-height: 1.8;font-size: 18px\">The nuScenes data come from 1,000 urban-road scenes in Boston and Singapore and record the trajectories of nearby motor vehicles, non-motor vehicles, and pedestrians. The dataset also provides high-definition maps. The data are sampled at 2 Hz, with each segment averaging 6 s. Link:<a href=\"https:\/\/www.nuscenes.org\/nuscenes#overview\">https:\/\/www.nuscenes.org\/nuscenes#overview<\/a><\/p>\n<\/div><div class=\"fusion-text fusion-text-5\" id=\"int\"><ul style=\"line-height: 1.8;font-size: 18px\">\n<li><strong>INTERACTION <\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;line-height: 1.8;font-size: 18px\">The INTERACTION dataset is an international dataset created by UC Berkeley's Mechanical Systems Control Laboratory and collaborators from other organizations. It contains extensive interactive behavior by road users, such as vehicles and pedestrians, in complex intersection and roundabout scenarios in different countries. The data are sampled at 10 Hz. Link:<a href=\"https:\/\/interaction-dataset.com\/\">https:\/\/interaction-dataset.com\/<\/a><\/p>\n<\/div><div class=\"fusion-text fusion-text-6\" id=\"Argoverse\"><ul style=\"line-height: 1.8;font-size: 18px\">\n<li><strong>Argoverse <\/strong><\/li>\n<\/ul>\n<p style=\"padding-left: 80px;line-height: 1.8;font-size: 18px\">The Argoverse dataset was released by Argo AI. It includes more than 1,000 hours of autonomous-vehicle driving data from urban roads in Miami and Pittsburgh. The data are sampled at 10 Hz, and each scene is 5 seconds long. Link:<a href=\"https:\/\/www.argoverse.org\/data.html#download-link\">https:\/\/www.argoverse.org\/data.html#download-link<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":5,"featured_media":0,"parent":70,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"100-width.php","meta":{"inline_featured_image":false,"iawp_total_views":0,"footnotes":""},"class_list":["post-74","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/110.42.248.92\/onsite-learning-center\/en\/wp-json\/wp\/v2\/pages\/74"}],"collection":[{"href":"http:\/\/110.42.248.92\/onsite-learning-center\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/110.42.248.92\/onsite-learning-center\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/110.42.248.92\/onsite-learning-center\/en\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"http:\/\/110.42.248.92\/onsite-learning-center\/en\/wp-json\/wp\/v2\/comments?post=74"}],"version-history":[{"count":20,"href":"http:\/\/110.42.248.92\/onsite-learning-center\/en\/wp-json\/wp\/v2\/pages\/74\/revisions"}],"predecessor-version":[{"id":1238,"href":"http:\/\/110.42.248.92\/onsite-learning-center\/en\/wp-json\/wp\/v2\/pages\/74\/revisions\/1238"}],"up":[{"embeddable":true,"href":"http:\/\/110.42.248.92\/onsite-learning-center\/en\/wp-json\/wp\/v2\/pages\/70"}],"wp:attachment":[{"href":"http:\/\/110.42.248.92\/onsite-learning-center\/en\/wp-json\/wp\/v2\/media?parent=74"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}