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justify-center"},n.createElement(M,{url:"https://arxiv.org/pdf/2403.04593",icon:n.createElement(r.yRW,null)},"Paper"),n.createElement(M,{url:"https://github.com/OpenDriveLab/ELM",icon:n.createElement(o.RrF,null)},"Code")),n.createElement("img",{src:l,alt:"teaser"}),n.createElement("div",{className:"flex justify-center"},n.createElement("p",{className:"text-justify text-xl !mt-0 !mb-2 font-medium max-w-[100%] md:max-w-[100%]"},"ELM is an embodied language model for understanding the long-horizon driving scenarios in space and time. Compared to the vanilla vision-language model (VLM) being confined to the scene description task, we expand a wide spectrum of new tasks to fully leverage the capability of large language models in an embodiment setting. ELM achieves significant improvements in various applications."))),n.createElement("div",{className:"my-6 pt-6 pb-4 bg-gradient-to-r from-pink-100/70 via-indigo-100/70 to-emerald-100/70"},n.createElement("div",{className:"mx-auto w-full max-w-[97.5%] lg:max-w-7xl py-2 md:py-4 px-2 md:px-4"},n.createElement("p",{className:"text-center text-lg md:text-xl md:max-w-[85%] mx-auto"},n.createElement(T,null,"Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded upon Vision-Language Models (VLMs). Nevertheless, existing VLMs are restricted to the 2D domain, devoid of spatial awareness and long-horizon extrapolation proficiencies. We revisit the key aspects of autonomous driving and formulate appropriate rubrics. Hereby, we introduce the Embodied Language Model (ELM), a comprehensive framework tailored for agents' understanding of driving scenes with large spatial and temporal spans. ELM incorporates space-aware pre-training to endow the agent with robust spatial localization capabilities. Besides, the model employs time-aware token selection to accurately inquire about temporal cues. We instantiate ELM on the reformulated multi-faced benchmark, and it surpasses previous state-of-the-art approaches in all aspects.")))),n.createElement(P,null,n.createElement("h2",{className:"font-semibold border-b-[1px] !mb-4"},"Methodology"),n.createElement("h3",{id:"language-guided",className:"!mt-4"},"Model Overview"),n.createElement("img",{src:c,alt:"Description of the image"}),n.createElement("p",null," ",n.createElement("b",null,"Systematic Pipeline of ELM.")," It consists of Pre-training by open-world data corpus and Fine-tuning on diverse tasks. To initialize the Space-aware Pre-training, we collect extensive image-text pairs from the world, empowering ELM with spatial localization while preserving the description ability in driving scenarios. In the fine-tuning process, the inputs to ELM are videos, timestamps, and text prompts. After encoding the inputs into tokens, ELM leverages the proposed Time-aware Token Selection to gather the appropriate tokens as instructed by prompts. Finally, the tokens are sent to the language model to generate output texts."),n.createElement("h3",{id:"language-guided",className:"!mt-4"},"Space-aware Pre-training"),n.createElement("img",{src:u,alt:"Description of the image"}),n.createElement("p",null," ",n.createElement("b",null,"Annotation workflow with human quality check in the loop. For location labeling:")," we first select diverse templates from the GPT generated candidates. Pixel-point pairs as annotated in the nuScenes are then sampled and filled into the templates to form our location pre-training data.",n.createElement("b",null,"For description labeling:")," Node 4 utilizes LLaMA-Adapter V2 to obtain diverse labels on nuScenes, Waymo, YouTube, and Ego4D with predefined prompts. Two rounds of quality check are conducted in Node 3 and 7 by inspectors to guarantee the image and caption quality."),n.createElement("img",{src:d,alt:"Description of the image"}),n.createElement("p",null," ",n.createElement("b",null,"Statistics of pre-training data and comparison of data collection with other VLMs. "),"Our pre-train data surpasses that in general vision (top) and autonomous driving (middle) in terms of quantity and diversity. Anno: the type of annotations; Des: description; Loc: localization."),n.createElement("h2",{className:"font-semibold border-b-[1px] !mb-4"},"Experiments")),n.createElement("div",{className:"my-6 pt-6 pb-4 bg-gradient-to-r from-pink-100/70 via-indigo-100/70 to-emerald-100/70"},n.createElement("div",{className:"relative pb-8 mb-3"},n.createElement(a.default,{responsive:N,infinite:!0,showDots:!0,renderDotsOutside:!0},n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:p,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:f,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:g,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:m,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:v,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:y,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:E,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:b,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:w,alt:"Description of the image",style:{maxHeight:"400px"}})))),n.createElement("p",{className:"text-center text-lg md:text-xl md:max-w-[85%] mx-auto"},n.createElement("b",null,"Visualization on the benchmark.")," We provide results for ten tasks and E2E planning through videos and corresponding QA pairs.")),n.createElement(P,null,n.createElement("h3",{id:"language-guided",className:"!mt-4"},"Results"),n.createElement("img",{src:h,alt:"Description of the image"}),n.createElement("p",null," ",n.createElement("b",null,"Comparison to State-of-the-arts. "),"All methods are fine-tuned on the corresponding tasks. The main metrics (%) are marked in gray. Bold emphasizes top method; underline marks the runner-up. 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justify-center"},n.createElement(M,{url:"https://arxiv.org/pdf/2403.04593",icon:n.createElement(r.yRW,null)},"Paper"),n.createElement(M,{url:"https://github.com/OpenDriveLab/ELM",icon:n.createElement(o.RrF,null)},"Code")),n.createElement("img",{src:l,alt:"teaser"}),n.createElement("div",{className:"flex justify-center"},n.createElement("p",{className:"text-justify text-xl !mt-0 !mb-2 font-medium max-w-[100%] md:max-w-[100%]"},"ELM is an embodied language model for understanding the long-horizon driving scenarios in space and time. Compared to the vanilla vision-language model (VLM) being confined to the scene description task, we expand a wide spectrum of new tasks to fully leverage the capability of large language models in an embodiment setting. ELM achieves significant improvements in various applications."))),n.createElement("div",{className:"my-6 pt-6 pb-4 bg-gradient-to-r from-pink-100/70 via-indigo-100/70 to-emerald-100/70"},n.createElement("div",{className:"mx-auto w-full max-w-[97.5%] lg:max-w-7xl py-2 md:py-4 px-2 md:px-4"},n.createElement("p",{className:"text-center text-lg md:text-xl md:max-w-[85%] mx-auto"},n.createElement(T,null,"Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded upon Vision-Language Models (VLMs). Nevertheless, existing VLMs are restricted to the 2D domain, devoid of spatial awareness and long-horizon extrapolation proficiencies. We revisit the key aspects of autonomous driving and formulate appropriate rubrics. Hereby, we introduce the Embodied Language Model (ELM), a comprehensive framework tailored for agents' understanding of driving scenes with large spatial and temporal spans. ELM incorporates space-aware pre-training to endow the agent with robust spatial localization capabilities. Besides, the model employs time-aware token selection to accurately inquire about temporal cues. We instantiate ELM on the reformulated multi-faced benchmark, and it surpasses previous state-of-the-art approaches in all aspects.")))),n.createElement(P,null,n.createElement("h2",{className:"font-semibold border-b-[1px] !mb-4"},"Methodology"),n.createElement("h3",{id:"language-guided",className:"!mt-4"},"Model Overview"),n.createElement("img",{src:c,alt:"Description of the image"}),n.createElement("p",null," ",n.createElement("b",null,"Systematic Pipeline of ELM.")," It consists of Pre-training by open-world data corpus and Fine-tuning on diverse tasks. To initialize the Space-aware Pre-training, we collect extensive image-text pairs from the world, empowering ELM with spatial localization while preserving the description ability in driving scenarios. In the fine-tuning process, the inputs to ELM are videos, timestamps, and text prompts. After encoding the inputs into tokens, ELM leverages the proposed Time-aware Token Selection to gather the appropriate tokens as instructed by prompts. Finally, the tokens are sent to the language model to generate output texts."),n.createElement("h3",{id:"language-guided",className:"!mt-4"},"Space-aware Pre-training"),n.createElement("img",{src:u,alt:"Description of the image"}),n.createElement("p",null," ",n.createElement("b",null,"Annotation workflow with human quality check in the loop. For location labeling:")," we first select diverse templates from the GPT generated candidates. Pixel-point pairs as annotated in the nuScenes are then sampled and filled into the templates to form our location pre-training data.",n.createElement("b",null,"For description labeling:")," Node 4 utilizes LLaMA-Adapter V2 to obtain diverse labels on nuScenes, Waymo, YouTube, and Ego4D with predefined prompts. Two rounds of quality check are conducted in Node 3 and 7 by inspectors to guarantee the image and caption quality."),n.createElement("img",{src:d,alt:"Description of the image"}),n.createElement("p",null," ",n.createElement("b",null,"Statistics of pre-training data and comparison of data collection with other VLMs. "),"Our pre-train data surpasses that in general vision (top) and autonomous driving (middle) in terms of quantity and diversity. Anno: the type of annotations; Des: description; Loc: localization."),n.createElement("h2",{className:"font-semibold border-b-[1px] !mb-4"},"Experiments")),n.createElement("div",{className:"my-6 pt-6 pb-4 bg-gradient-to-r from-pink-100/70 via-indigo-100/70 to-emerald-100/70"},n.createElement("div",{className:"relative pb-8 mb-3"},n.createElement(a.default,{responsive:N,infinite:!0,showDots:!0,renderDotsOutside:!0},n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:p,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:f,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:g,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:m,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:v,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:y,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:E,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:b,alt:"Description of the image",style:{maxHeight:"400px"}})),n.createElement("div",{style:{display:"flex",justifyContent:"center"}},n.createElement("img",{src:w,alt:"Description of the image",style:{maxHeight:"400px"}})))),n.createElement("p",{className:"text-center text-lg md:text-xl md:max-w-[85%] mx-auto"},n.createElement("b",null,"Visualization on the benchmark.")," We provide results for ten tasks and E2E planning through videos and corresponding QA pairs.")),n.createElement(P,null,n.createElement("h3",{id:"language-guided",className:"!mt-4"},"Results"),n.createElement("img",{src:h,alt:"Description of the image"}),n.createElement("p",null," ",n.createElement("b",null,"Comparison to State-of-the-arts. "),"All methods are fine-tuned on the corresponding tasks. The main metrics (%) are marked in gray. Bold emphasizes top method; underline marks the runner-up. 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var IconsManifest = [\n {\n \"id\": \"ci\",\n \"name\": \"Circum Icons\",\n \"projectUrl\": \"https://circumicons.com/\",\n \"license\": \"MPL-2.0 license\",\n \"licenseUrl\": \"https://github.com/Klarr-Agency/Circum-Icons/blob/main/LICENSE\"\n },\n {\n \"id\": \"fa\",\n \"name\": \"Font Awesome 5\",\n \"projectUrl\": \"https://fontawesome.com/\",\n \"license\": \"CC BY 4.0 License\",\n \"licenseUrl\": \"https://creativecommons.org/licenses/by/4.0/\"\n },\n {\n \"id\": \"fa6\",\n \"name\": \"Font Awesome 6\",\n \"projectUrl\": \"https://fontawesome.com/\",\n \"license\": \"CC BY 4.0 License\",\n \"licenseUrl\": \"https://creativecommons.org/licenses/by/4.0/\"\n },\n {\n \"id\": \"io\",\n \"name\": \"Ionicons 4\",\n \"projectUrl\": \"https://ionicons.com/\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/ionic-team/ionicons/blob/master/LICENSE\"\n },\n {\n \"id\": \"io5\",\n \"name\": \"Ionicons 5\",\n \"projectUrl\": \"https://ionicons.com/\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/ionic-team/ionicons/blob/master/LICENSE\"\n },\n {\n \"id\": \"md\",\n \"name\": \"Material Design icons\",\n \"projectUrl\": \"http://google.github.io/material-design-icons/\",\n \"license\": \"Apache License Version 2.0\",\n \"licenseUrl\": \"https://github.com/google/material-design-icons/blob/master/LICENSE\"\n },\n {\n \"id\": \"ti\",\n \"name\": \"Typicons\",\n \"projectUrl\": \"http://s-ings.com/typicons/\",\n \"license\": \"CC BY-SA 3.0\",\n \"licenseUrl\": \"https://creativecommons.org/licenses/by-sa/3.0/\"\n },\n {\n \"id\": \"go\",\n \"name\": \"Github Octicons icons\",\n \"projectUrl\": \"https://octicons.github.com/\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/primer/octicons/blob/master/LICENSE\"\n },\n {\n \"id\": \"fi\",\n \"name\": \"Feather\",\n \"projectUrl\": \"https://feathericons.com/\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/feathericons/feather/blob/master/LICENSE\"\n },\n {\n \"id\": \"lu\",\n \"name\": \"Lucide\",\n \"projectUrl\": \"https://lucide.dev/\",\n \"license\": \"ISC\",\n \"licenseUrl\": \"https://github.com/lucide-icons/lucide/blob/main/LICENSE\"\n },\n {\n \"id\": \"gi\",\n \"name\": \"Game Icons\",\n \"projectUrl\": \"https://game-icons.net/\",\n \"license\": \"CC BY 3.0\",\n \"licenseUrl\": \"https://creativecommons.org/licenses/by/3.0/\"\n },\n {\n \"id\": \"wi\",\n \"name\": \"Weather Icons\",\n \"projectUrl\": \"https://erikflowers.github.io/weather-icons/\",\n \"license\": \"SIL OFL 1.1\",\n \"licenseUrl\": \"http://scripts.sil.org/OFL\"\n },\n {\n \"id\": \"di\",\n \"name\": \"Devicons\",\n \"projectUrl\": \"https://vorillaz.github.io/devicons/\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"ai\",\n \"name\": \"Ant Design Icons\",\n \"projectUrl\": \"https://github.com/ant-design/ant-design-icons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"bs\",\n \"name\": \"Bootstrap Icons\",\n \"projectUrl\": \"https://github.com/twbs/icons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"ri\",\n \"name\": \"Remix Icon\",\n \"projectUrl\": \"https://github.com/Remix-Design/RemixIcon\",\n \"license\": \"Apache License Version 2.0\",\n \"licenseUrl\": \"http://www.apache.org/licenses/\"\n },\n {\n \"id\": \"fc\",\n \"name\": \"Flat Color Icons\",\n \"projectUrl\": \"https://github.com/icons8/flat-color-icons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"gr\",\n \"name\": \"Grommet-Icons\",\n \"projectUrl\": \"https://github.com/grommet/grommet-icons\",\n \"license\": \"Apache License Version 2.0\",\n \"licenseUrl\": \"http://www.apache.org/licenses/\"\n },\n {\n \"id\": \"hi\",\n \"name\": \"Heroicons\",\n \"projectUrl\": \"https://github.com/tailwindlabs/heroicons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"hi2\",\n \"name\": \"Heroicons 2\",\n \"projectUrl\": \"https://github.com/tailwindlabs/heroicons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"si\",\n \"name\": \"Simple Icons\",\n \"projectUrl\": \"https://simpleicons.org/\",\n \"license\": \"CC0 1.0 Universal\",\n \"licenseUrl\": \"https://creativecommons.org/publicdomain/zero/1.0/\"\n },\n {\n \"id\": \"sl\",\n \"name\": \"Simple Line Icons\",\n \"projectUrl\": \"https://thesabbir.github.io/simple-line-icons/\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"im\",\n \"name\": \"IcoMoon Free\",\n \"projectUrl\": \"https://github.com/Keyamoon/IcoMoon-Free\",\n \"license\": \"CC BY 4.0 License\",\n \"licenseUrl\": \"https://github.com/Keyamoon/IcoMoon-Free/blob/master/License.txt\"\n },\n {\n \"id\": \"bi\",\n \"name\": \"BoxIcons\",\n \"projectUrl\": \"https://github.com/atisawd/boxicons\",\n \"license\": \"CC BY 4.0 License\",\n \"licenseUrl\": \"https://github.com/atisawd/boxicons/blob/master/LICENSE\"\n },\n {\n \"id\": \"cg\",\n \"name\": \"css.gg\",\n \"projectUrl\": \"https://github.com/astrit/css.gg\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"vsc\",\n \"name\": \"VS Code Icons\",\n \"projectUrl\": \"https://github.com/microsoft/vscode-codicons\",\n \"license\": \"CC BY 4.0\",\n \"licenseUrl\": \"https://creativecommons.org/licenses/by/4.0/\"\n },\n {\n \"id\": \"tb\",\n \"name\": \"Tabler Icons\",\n \"projectUrl\": \"https://github.com/tabler/tabler-icons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"tfi\",\n \"name\": \"Themify Icons\",\n \"projectUrl\": \"https://github.com/lykmapipo/themify-icons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/thecreation/standard-icons/blob/master/modules/themify-icons/LICENSE\"\n },\n {\n \"id\": \"rx\",\n \"name\": \"Radix Icons\",\n \"projectUrl\": \"https://icons.radix-ui.com\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/radix-ui/icons/blob/master/LICENSE\"\n },\n {\n \"id\": \"pi\",\n \"name\": \"Phosphor Icons\",\n \"projectUrl\": \"https://github.com/phosphor-icons/core\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/phosphor-icons/core/blob/main/LICENSE\"\n },\n {\n \"id\": \"lia\",\n \"name\": \"Icons8 Line Awesome\",\n \"projectUrl\": \"https://icons8.com/line-awesome\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/icons8/line-awesome/blob/master/LICENSE.md\"\n }\n]","import React from \"react\";\nexport var DefaultContext = {\n color: undefined,\n size: undefined,\n className: undefined,\n style: undefined,\n attr: undefined\n};\nexport var IconContext = React.createContext && React.createContext(DefaultContext);","var __assign = this && this.__assign || function () {\n __assign = Object.assign || function (t) {\n for (var s, i = 1, n = arguments.length; i < n; i++) {\n s = arguments[i];\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\n }\n return t;\n };\n return __assign.apply(this, arguments);\n};\nvar __rest = this && this.__rest || function (s, e) {\n var t = {};\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0) t[p] = s[p];\n if (s != null && typeof Object.getOwnPropertySymbols === \"function\") for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i])) t[p[i]] = s[p[i]];\n }\n return t;\n};\nimport React from \"react\";\nimport { IconContext, DefaultContext } from \"./iconContext\";\nfunction Tree2Element(tree) {\n return tree && tree.map(function (node, i) {\n return React.createElement(node.tag, __assign({\n key: i\n }, node.attr), Tree2Element(node.child));\n });\n}\nexport function GenIcon(data) {\n // eslint-disable-next-line react/display-name\n return function (props) {\n return React.createElement(IconBase, __assign({\n attr: __assign({}, data.attr)\n }, props), Tree2Element(data.child));\n };\n}\nexport function IconBase(props) {\n var elem = function (conf) {\n var attr = props.attr,\n size = props.size,\n title = props.title,\n svgProps = __rest(props, [\"attr\", \"size\", \"title\"]);\n var computedSize = size || conf.size || \"1em\";\n var className;\n if (conf.className) className = conf.className;\n if (props.className) className = (className ? className + \" \" : \"\") + props.className;\n return React.createElement(\"svg\", __assign({\n stroke: \"currentColor\",\n fill: \"currentColor\",\n strokeWidth: \"0\"\n }, conf.attr, attr, svgProps, {\n className: className,\n style: __assign(__assign({\n color: props.color || conf.color\n }, conf.style), props.style),\n height: computedSize,\n width: computedSize,\n xmlns: \"http://www.w3.org/2000/svg\"\n }), title && React.createElement(\"title\", null, title), props.children);\n };\n return IconContext !== undefined ? React.createElement(IconContext.Consumer, null, function (conf) {\n return elem(conf);\n }) : elem(DefaultContext);\n}","module.exports = require('./lib');\n","\"use strict\";Object.defineProperty(exports,\"__esModule\",{value:!0});var React=require(\"react\"),LeftArrow=function(_a){var customLeftArrow=_a.customLeftArrow,getState=_a.getState,previous=_a.previous,disabled=_a.disabled,rtl=_a.rtl;if(customLeftArrow)return React.cloneElement(customLeftArrow,{onClick:function(){return previous()},carouselState:getState(),disabled:disabled,rtl:rtl});var rtlClassName=rtl?\"rtl\":\"\";return React.createElement(\"button\",{\"aria-label\":\"Go to previous slide\",className:\"react-multiple-carousel__arrow react-multiple-carousel__arrow--left \"+rtlClassName,onClick:function(){return previous()},type:\"button\",disabled:disabled})};exports.LeftArrow=LeftArrow;var RightArrow=function(_a){var customRightArrow=_a.customRightArrow,getState=_a.getState,next=_a.next,disabled=_a.disabled,rtl=_a.rtl;if(customRightArrow)return React.cloneElement(customRightArrow,{onClick:function(){return next()},carouselState:getState(),disabled:disabled,rtl:rtl});var rtlClassName=rtl?\"rtl\":\"\";return React.createElement(\"button\",{\"aria-label\":\"Go to next slide\",className:\"react-multiple-carousel__arrow react-multiple-carousel__arrow--right \"+rtlClassName,onClick:function(){return next()},type:\"button\",disabled:disabled})};exports.RightArrow=RightArrow;","\"use strict\";var __extends=this&&this.__extends||function(){var extendStatics=function(d,b){return(extendStatics=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(d,b){d.__proto__=b}||function(d,b){for(var p in b)b.hasOwnProperty(p)&&(d[p]=b[p])})(d,b)};return function(d,b){function __(){this.constructor=d}extendStatics(d,b),d.prototype=null===b?Object.create(b):(__.prototype=b.prototype,new __)}}();Object.defineProperty(exports,\"__esModule\",{value:!0});var React=require(\"react\"),utils_1=require(\"./utils\"),types_1=require(\"./types\"),Dots_1=require(\"./Dots\"),Arrows_1=require(\"./Arrows\"),CarouselItems_1=require(\"./CarouselItems\"),common_1=require(\"./utils/common\"),defaultTransitionDuration=400,defaultTransition=\"transform 400ms ease-in-out\",Carousel=function(_super){function Carousel(props){var _this=_super.call(this,props)||this;return _this.containerRef=React.createRef(),_this.listRef=React.createRef(),_this.state={itemWidth:0,slidesToShow:0,currentSlide:0,totalItems:React.Children.count(props.children),deviceType:\"\",domLoaded:!1,transform:0,containerWidth:0},_this.onResize=_this.onResize.bind(_this),_this.handleDown=_this.handleDown.bind(_this),_this.handleMove=_this.handleMove.bind(_this),_this.handleOut=_this.handleOut.bind(_this),_this.onKeyUp=_this.onKeyUp.bind(_this),_this.handleEnter=_this.handleEnter.bind(_this),_this.setIsInThrottle=_this.setIsInThrottle.bind(_this),_this.next=utils_1.throttle(_this.next.bind(_this),props.transitionDuration||defaultTransitionDuration,_this.setIsInThrottle),_this.previous=utils_1.throttle(_this.previous.bind(_this),props.transitionDuration||defaultTransitionDuration,_this.setIsInThrottle),_this.goToSlide=utils_1.throttle(_this.goToSlide.bind(_this),props.transitionDuration||defaultTransitionDuration,_this.setIsInThrottle),_this.onMove=!1,_this.initialX=0,_this.lastX=0,_this.isAnimationAllowed=!1,_this.direction=\"\",_this.initialY=0,_this.isInThrottle=!1,_this.transformPlaceHolder=0,_this}return __extends(Carousel,_super),Carousel.prototype.resetTotalItems=function(){var _this=this,totalItems=React.Children.count(this.props.children),currentSlide=utils_1.notEnoughChildren(this.state)?0:Math.max(0,Math.min(this.state.currentSlide,totalItems));this.setState({totalItems:totalItems,currentSlide:currentSlide},function(){_this.setContainerAndItemWidth(_this.state.slidesToShow,!0)})},Carousel.prototype.setIsInThrottle=function(isInThrottle){void 0===isInThrottle&&(isInThrottle=!1),this.isInThrottle=isInThrottle},Carousel.prototype.setTransformDirectly=function(position,withAnimation){var additionalTransfrom=this.props.additionalTransfrom;this.transformPlaceHolder=position;var currentTransform=common_1.getTransform(this.state,this.props,this.transformPlaceHolder);this.listRef&&this.listRef.current&&(this.setAnimationDirectly(withAnimation),this.listRef.current.style.transform=\"translate3d(\"+(currentTransform+additionalTransfrom)+\"px,0,0)\")},Carousel.prototype.setAnimationDirectly=function(animationAllowed){this.listRef&&this.listRef.current&&(this.listRef.current.style.transition=animationAllowed?this.props.customTransition||defaultTransition:\"none\")},Carousel.prototype.componentDidMount=function(){this.setState({domLoaded:!0}),this.setItemsToShow(),window.addEventListener(\"resize\",this.onResize),this.onResize(!0),this.props.keyBoardControl&&window.addEventListener(\"keyup\",this.onKeyUp),this.props.autoPlay&&(this.autoPlay=setInterval(this.next,this.props.autoPlaySpeed))},Carousel.prototype.setClones=function(slidesToShow,itemWidth,forResizing,resetCurrentSlide){var _this=this;void 0===resetCurrentSlide&&(resetCurrentSlide=!1),this.isAnimationAllowed=!1;var childrenArr=React.Children.toArray(this.props.children),initialSlide=utils_1.getInitialSlideInInfiniteMode(slidesToShow||this.state.slidesToShow,childrenArr),clones=utils_1.getClones(this.state.slidesToShow,childrenArr),currentSlide=childrenArr.lengthMath.abs(diffY)))return;var _b=utils_1.populateSlidesOnMouseTouchMove(this.state,this.props,this.initialX,this.lastX,clientX,this.transformPlaceHolder),direction=_b.direction,nextPosition=_b.nextPosition,canContinue=_b.canContinue;direction&&(this.direction=direction,canContinue&&void 0!==nextPosition&&this.setTransformDirectly(nextPosition)),this.lastX=clientX}}},Carousel.prototype.handleOut=function(e){this.props.autoPlay&&!this.autoPlay&&(this.autoPlay=setInterval(this.next,this.props.autoPlaySpeed));var shouldDisableOnMobile=\"touchend\"===e.type&&!this.props.swipeable,shouldDisableOnDesktop=(\"mouseleave\"===e.type||\"mouseup\"===e.type)&&!this.props.draggable;if(!shouldDisableOnMobile&&!shouldDisableOnDesktop&&this.onMove){if(this.setAnimationDirectly(!0),\"right\"===this.direction)if(this.initialX-this.lastX>=this.props.minimumTouchDrag){var slidesHavePassed=Math.round((this.initialX-this.lastX)/this.state.itemWidth);this.next(slidesHavePassed)}else this.correctItemsPosition(this.state.itemWidth,!0,!0);if(\"left\"===this.direction)if(this.lastX-this.initialX>this.props.minimumTouchDrag){slidesHavePassed=Math.round((this.lastX-this.initialX)/this.state.itemWidth);this.previous(slidesHavePassed)}else this.correctItemsPosition(this.state.itemWidth,!0,!0);this.resetMoveStatus()}},Carousel.prototype.isInViewport=function(el){var _a=el.getBoundingClientRect(),_b=_a.top,top=void 0===_b?0:_b,_c=_a.left,left=void 0===_c?0:_c,_d=_a.bottom,bottom=void 0===_d?0:_d,_e=_a.right,right=void 0===_e?0:_e;return 0<=top&&0<=left&&bottom<=(window.innerHeight||document.documentElement.clientHeight)&&right<=(window.innerWidth||document.documentElement.clientWidth)},Carousel.prototype.isChildOfCarousel=function(el){return!!(el instanceof Element&&this.listRef&&this.listRef.current)&&this.listRef.current.contains(el)},Carousel.prototype.onKeyUp=function(e){var target=e.target;switch(e.keyCode){case 37:if(this.isChildOfCarousel(target))return this.previous();break;case 39:if(this.isChildOfCarousel(target))return this.next();break;case 9:if(this.isChildOfCarousel(target)&&target instanceof HTMLInputElement&&this.isInViewport(target))return this.next()}},Carousel.prototype.handleEnter=function(e){types_1.isMouseMoveEvent(e)&&this.autoPlay&&this.props.autoPlay&&this.props.pauseOnHover&&(clearInterval(this.autoPlay),this.autoPlay=void 0)},Carousel.prototype.goToSlide=function(slide,skipCallbacks,animationAllowed){var _this=this;if(void 0===animationAllowed&&(animationAllowed=!0),!this.isInThrottle){var itemWidth=this.state.itemWidth,_a=this.props,afterChange=_a.afterChange,beforeChange=_a.beforeChange,previousSlide=this.state.currentSlide;\"function\"!=typeof beforeChange||skipCallbacks&&(\"object\"!=typeof skipCallbacks||skipCallbacks.skipBeforeChange)||beforeChange(slide,this.getState()),this.isAnimationAllowed=animationAllowed,this.props.shouldResetAutoplay&&this.resetAutoplayInterval(),this.setState({currentSlide:slide,transform:-itemWidth*slide},function(){_this.props.infinite&&_this.correctClonesPosition({domLoaded:!0}),\"function\"!=typeof afterChange||skipCallbacks&&(\"object\"!=typeof skipCallbacks||skipCallbacks.skipAfterChange)||(Carousel.afterChangeTimeout3=setTimeout(function(){afterChange(previousSlide,_this.getState())},_this.props.transitionDuration||defaultTransitionDuration))})}},Carousel.prototype.getState=function(){return this.state},Carousel.prototype.renderLeftArrow=function(disbaled){var _this=this,_a=this.props,customLeftArrow=_a.customLeftArrow,rtl=_a.rtl;return React.createElement(Arrows_1.LeftArrow,{customLeftArrow:customLeftArrow,getState:function(){return _this.getState()},previous:this.previous,disabled:disbaled,rtl:rtl})},Carousel.prototype.renderRightArrow=function(disbaled){var _this=this,_a=this.props,customRightArrow=_a.customRightArrow,rtl=_a.rtl;return React.createElement(Arrows_1.RightArrow,{customRightArrow:customRightArrow,getState:function(){return _this.getState()},next:this.next,disabled:disbaled,rtl:rtl})},Carousel.prototype.renderButtonGroups=function(){var _this=this,customButtonGroup=this.props.customButtonGroup;return customButtonGroup?React.cloneElement(customButtonGroup,{previous:function(){return _this.previous()},next:function(){return _this.next()},goToSlide:function(slideIndex,skipCallbacks){return _this.goToSlide(slideIndex,skipCallbacks)},carouselState:this.getState()}):null},Carousel.prototype.renderDotsList=function(){var _this=this;return React.createElement(Dots_1.default,{state:this.state,props:this.props,goToSlide:this.goToSlide,getState:function(){return _this.getState()}})},Carousel.prototype.renderCarouselItems=function(){var clones=[];if(this.props.infinite){var childrenArr=React.Children.toArray(this.props.children);clones=utils_1.getClones(this.state.slidesToShow,childrenArr)}return React.createElement(CarouselItems_1.default,{clones:clones,goToSlide:this.goToSlide,state:this.state,notEnoughChildren:utils_1.notEnoughChildren(this.state),props:this.props})},Carousel.prototype.render=function(){var _a=this.props,deviceType=_a.deviceType,arrows=_a.arrows,renderArrowsWhenDisabled=_a.renderArrowsWhenDisabled,removeArrowOnDeviceType=_a.removeArrowOnDeviceType,infinite=_a.infinite,containerClass=_a.containerClass,sliderClass=_a.sliderClass,customTransition=_a.customTransition,additionalTransfrom=_a.additionalTransfrom,renderDotsOutside=_a.renderDotsOutside,renderButtonGroupOutside=_a.renderButtonGroupOutside,className=_a.className,rtl=_a.rtl;\"production\"!==process.env.NODE_ENV&&utils_1.throwError(this.state,this.props);var _b=utils_1.getInitialState(this.state,this.props),shouldRenderOnSSR=_b.shouldRenderOnSSR,shouldRenderAtAll=_b.shouldRenderAtAll,isLeftEndReach=utils_1.isInLeftEnd(this.state),isRightEndReach=utils_1.isInRightEnd(this.state),shouldShowArrows=arrows&&!(removeArrowOnDeviceType&&(deviceType&&-12*slidesToShow?index+2*slidesToShow:currentSlide>=childrenArr.length?childrenArr.length+index:index}function getOriginalIndexLookupTableByClones(slidesToShow,childrenArr){if(childrenArr.length>2*slidesToShow){for(var table={},firstBeginningOfClones=childrenArr.length-2*slidesToShow,firstEndOfClones=childrenArr.length-firstBeginningOfClones,firstCount=firstBeginningOfClones,i=0;i2*slidesToShow?childrenArr.slice(childrenArr.length-2*slidesToShow,childrenArr.length).concat(childrenArr,childrenArr.slice(0,2*slidesToShow)):childrenArr.concat(childrenArr,childrenArr)}function getInitialSlideInInfiniteMode(slidesToShow,childrenArr){return childrenArr.length>2*slidesToShow?2*slidesToShow:childrenArr.length}function checkClonesPosition(_a,childrenArr,props){var isReachingTheEnd,currentSlide=_a.currentSlide,slidesToShow=_a.slidesToShow,itemWidth=_a.itemWidth,totalItems=_a.totalItems,nextSlide=0,nextPosition=0,isReachingTheStart=0===currentSlide,originalFirstSlide=childrenArr.length-(childrenArr.length-2*slidesToShow);return childrenArr.length2*slidesToShow?((isReachingTheEnd=currentSlide>=originalFirstSlide+childrenArr.length)&&(nextPosition=-itemWidth*(nextSlide=currentSlide-childrenArr.length)),isReachingTheStart&&(nextPosition=-itemWidth*(nextSlide=originalFirstSlide+(childrenArr.length-2*slidesToShow)))):((isReachingTheEnd=currentSlide>=2*childrenArr.length)&&(nextPosition=-itemWidth*(nextSlide=currentSlide-childrenArr.length)),isReachingTheStart&&(nextPosition=props.showDots?-itemWidth*(nextSlide=childrenArr.length):-itemWidth*(nextSlide=totalItems/3))),{isReachingTheEnd:isReachingTheEnd,isReachingTheStart:isReachingTheStart,nextSlide:nextSlide,nextPosition:nextPosition}}Object.defineProperty(exports,\"__esModule\",{value:!0}),exports.getOriginalCounterPart=getOriginalCounterPart,exports.getOriginalIndexLookupTableByClones=getOriginalIndexLookupTableByClones,exports.getClones=getClones,exports.getInitialSlideInInfiniteMode=getInitialSlideInInfiniteMode,exports.checkClonesPosition=checkClonesPosition;","\"use strict\";Object.defineProperty(exports,\"__esModule\",{value:!0});var elementWidth_1=require(\"./elementWidth\");function notEnoughChildren(state){var slidesToShow=state.slidesToShow;return state.totalItems=min&&window.innerWidth<=max&&(slidesToScroll=slidesToSlide)}),slidesToScroll}exports.notEnoughChildren=notEnoughChildren,exports.getInitialState=getInitialState,exports.getIfSlideIsVisbile=getIfSlideIsVisbile,exports.getTransformForCenterMode=getTransformForCenterMode,exports.isInLeftEnd=isInLeftEnd,exports.isInRightEnd=isInRightEnd,exports.getTransformForPartialVsibile=getTransformForPartialVsibile,exports.parsePosition=parsePosition,exports.getTransform=getTransform,exports.getSlidesToSlide=getSlidesToSlide;","\"use strict\";Object.defineProperty(exports,\"__esModule\",{value:!0});var clones_1=require(\"./clones\"),common_1=require(\"./common\");function getLookupTableForNextSlides(numberOfDotsToShow,state,props,childrenArr){var table={},slidesToSlide=common_1.getSlidesToSlide(state,props);return Array(numberOfDotsToShow).fill(0).forEach(function(_,i){var nextSlide=clones_1.getOriginalCounterPart(i,state,childrenArr);if(0===i)table[0]=nextSlide;else{var now=table[i-1]+slidesToSlide;table[i]=now}}),table}exports.getLookupTableForNextSlides=getLookupTableForNextSlides;","\"use strict\";Object.defineProperty(exports,\"__esModule\",{value:!0});var hasWarnAboutTypo=!1;function getPartialVisibilityGutter(responsive,partialVisible,serverSideDeviceType,clientSideDeviceType){var gutter=0,deviceType=clientSideDeviceType||serverSideDeviceType;return partialVisible&&deviceType&&(!hasWarnAboutTypo&&\"production\"!==process.env.NODE_ENV&&responsive[deviceType].paritialVisibilityGutter&&(hasWarnAboutTypo=!0,console.warn(\"You appear to be using paritialVisibilityGutter instead of partialVisibilityGutter which will be moved to partialVisibilityGutter in the future completely\")),gutter=responsive[deviceType].partialVisibilityGutter||responsive[deviceType].paritialVisibilityGutter),gutter}function getWidthFromDeviceType(deviceType,responsive){var itemWidth;responsive[deviceType]&&(itemWidth=(100/responsive[deviceType].items).toFixed(1));return itemWidth}function getItemClientSideWidth(props,slidesToShow,containerWidth){return Math.round(containerWidth/(slidesToShow+(props.centerMode?1:0)))}exports.getPartialVisibilityGutter=getPartialVisibilityGutter,exports.getWidthFromDeviceType=getWidthFromDeviceType,exports.getItemClientSideWidth=getItemClientSideWidth;","\"use strict\";Object.defineProperty(exports,\"__esModule\",{value:!0});var clones_1=require(\"./clones\");exports.getOriginalCounterPart=clones_1.getOriginalCounterPart,exports.getClones=clones_1.getClones,exports.checkClonesPosition=clones_1.checkClonesPosition,exports.getInitialSlideInInfiniteMode=clones_1.getInitialSlideInInfiniteMode;var elementWidth_1=require(\"./elementWidth\");exports.getWidthFromDeviceType=elementWidth_1.getWidthFromDeviceType,exports.getPartialVisibilityGutter=elementWidth_1.getPartialVisibilityGutter,exports.getItemClientSideWidth=elementWidth_1.getItemClientSideWidth;var common_1=require(\"./common\");exports.getInitialState=common_1.getInitialState,exports.getIfSlideIsVisbile=common_1.getIfSlideIsVisbile,exports.getTransformForCenterMode=common_1.getTransformForCenterMode,exports.getTransformForPartialVsibile=common_1.getTransformForPartialVsibile,exports.isInLeftEnd=common_1.isInLeftEnd,exports.isInRightEnd=common_1.isInRightEnd,exports.notEnoughChildren=common_1.notEnoughChildren,exports.getSlidesToSlide=common_1.getSlidesToSlide;var throttle_1=require(\"./throttle\");exports.throttle=throttle_1.default;var throwError_1=require(\"./throwError\");exports.throwError=throwError_1.default;var next_1=require(\"./next\");exports.populateNextSlides=next_1.populateNextSlides;var previous_1=require(\"./previous\");exports.populatePreviousSlides=previous_1.populatePreviousSlides;var mouseOrTouchMove_1=require(\"./mouseOrTouchMove\");exports.populateSlidesOnMouseTouchMove=mouseOrTouchMove_1.populateSlidesOnMouseTouchMove;","\"use strict\";function populateSlidesOnMouseTouchMove(state,props,initialX,lastX,clientX,transformPlaceHolder){var direction,nextPosition,itemWidth=state.itemWidth,slidesToShow=state.slidesToShow,totalItems=state.totalItems,currentSlide=state.currentSlide,infinite=props.infinite,canContinue=!1,slidesHavePassedRight=Math.round((initialX-lastX)/itemWidth),slidesHavePassedLeft=Math.round((lastX-initialX)/itemWidth),isMovingLeft=initialX {\n // Paper title\n return (\n

{children}

\n )\n}\n\nconst Venue: React.FC = ({website, children}) => {\n return (\n \n )\n}\n\nconst Abstract: React.FC = ({children}) => {\n return (\n
\n
\n

Abstract

\n
\n
\n

{\n children\n }

\n
\n
\n )\n}\n\nconst Author: React.FC = ({children, website, firstAuthor, affiliations, lastAuthor}) => {\n return (\n \n \n {children}\n \n {firstAuthor || affiliations ?\n {firstAuthor ?\n * : null}{affiliations ? affiliations : null}\n : null}\n {lastAuthor ? null : <>, }\n \n )\n}\n\nconst Affiliation: React.FC = ({children, website, number}) => {\n return (\n \n {number}\n \n {children}\n \n \n )\n}\n\nconst ActionLink: React.FC = ({children, url, icon}) => {\n return (\n \n \n {icon} \n {children}\n \n \n )\n}\n\nconst Article: React.FC = ({children}) => {\n return (\n \n {children}\n \n )\n}\n\nconst Main: React.FC = ({children}) => {\n return (\n
\n {children}\n
\n )\n}\n\n\nconst FewShotResult: React.FC = ({children, id, demos, demos_label, video, hidden}) => {\n // Result for Few-Shot Manipulation with Demos on left, and video on right\n return (\n // add hidden if hide is true\n
\n
\n

{children}

\n {children}\n

{demos_label}

\n
\n
\n \n
\n
\n )\n}\n\n\nexport const Head: HeadFC = () => ELM: Embodied Understanding of Driving Scenarios\n\nconst carouselResponsive = {\n desktop: {\n breakpoint: {max: 3000, min: 1024},\n items: 3,\n },\n tablet: {\n breakpoint: {max: 1024, min: 464},\n items: 2,\n },\n mobile: {\n breakpoint: {max: 464, min: 0},\n items: 1,\n }\n};\n\n\nconst CarouselItem: React.FC = ({children, video}) => {\n return (\n
\n \n {/*

{children}

*/}\n
\n )\n}\n\nconst IndexPage: React.FC = () => {\n return (\n <>\n
\n
\n \n <span className=\"font-extrabold text-transparent bg-clip-text\n bg-gradient-to-r from-pink-500 via-indigo-600 to-emerald-400\">\n ELM:\n </span>\n  \n <span className=\"text-stone-800\">Embodied Understanding of Driving Scenarios</span>\n \n\n \n ECCV 2024\n \n\n {/* Authors */}\n
\n Yunsong Zhou\n Linyan Huang\n Qingwen Bu\n Jia Zeng\n Tianyu Li
\n Hang Qiu\n Hongzi Zhu\n Minyi Guo\n Yu Qiao\n Hongyang Li\n
\n\n {/* Affilations */}\n
\n OpenDriveLab at Shanghai AI Lab\n Shanghai Jiao Tong University\n University of California, Riverside\n
\n
\n *Indicates equal contribution.\n
\n\n {/* Action Links */}\n

\n }>Paper\n }>Code\n

\n\n \"teaser\"\n
\n

\n ELM is an embodied language model for understanding the long-horizon driving scenarios in space and time. \n Compared to the vanilla vision-language model (VLM) being confined to the scene description task, we expand a wide spectrum of new tasks to fully leverage the capability of large language models in an embodiment setting.\n ELM achieves significant improvements in various applications.\n

\n
\n\n
\n\n {/* Abstract */}\n
\n \n

\n \n Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded upon Vision-Language Models (VLMs). \n Nevertheless, existing VLMs are restricted to the 2D domain, devoid of spatial awareness and long-horizon extrapolation proficiencies. We revisit the key aspects of autonomous driving and formulate appropriate rubrics. \n Hereby, we introduce the Embodied Language Model (ELM), a comprehensive framework tailored for agents' understanding of driving scenes with large spatial and temporal spans. \n ELM incorporates space-aware pre-training to endow the agent with robust spatial localization capabilities. \n Besides, the model employs time-aware token selection to accurately inquire about temporal cues. \n We instantiate ELM on the reformulated multi-faced benchmark, and it surpasses previous state-of-the-art approaches in all aspects.\n \n

\n
\n \n\n
\n\n {/* model */}\n

Methodology

\n

Model Overview

\n \"Description\n

Systematic Pipeline of ELM. It consists of Pre-training by open-world data corpus and Fine-tuning on diverse tasks. \n To initialize the Space-aware Pre-training, we collect extensive image-text pairs from the world, empowering ELM with spatial localization while preserving the description ability in driving scenarios. \n In the fine-tuning process, the inputs to ELM are videos, timestamps, and text prompts. \n After encoding the inputs into tokens, ELM leverages the proposed Time-aware Token Selection to gather the appropriate tokens as instructed by prompts. \n Finally, the tokens are sent to the language model to generate output texts.

\n\n

Space-aware Pre-training

\n \"Description\n

Annotation workflow with human quality check in the loop. \n For location labeling: we first select diverse templates from the GPT generated candidates.\n Pixel-point pairs as annotated in the nuScenes are then sampled and filled into the templates to form our location pre-training data. \n For description labeling: Node 4 utilizes LLaMA-Adapter V2 to obtain diverse labels on nuScenes, Waymo, YouTube, and Ego4D with predefined prompts. \n Two rounds of quality check are conducted in Node 3 and 7 by inspectors to guarantee the image and caption quality.

\n \n \"Description\n

Statistics of pre-training data and comparison of data collection with other VLMs. \n Our pre-train data surpasses that in general vision (top) and autonomous driving (middle) in terms of quantity and diversity. \n Anno: the type of annotations; Des: description; Loc: localization.

\n\n {/* results */}\n

Experiments

\n
\n\n
\n {/* */}\n
\n \n
\"Description
\n
\"Description
\n
\"Description
\n
\"Description
\n
\"Description
\n
\"Description
\n
\"Description
\n
\"Description
\n
\"Description
\n {/* \"Description */}\n \n
\n

\n Visualization on the benchmark. We provide results for ten tasks and E2E planning through videos and corresponding QA pairs.\n

\n {/*
*/}\n \n
\n \n

Results

\n \"Description\n

Comparison to State-of-the-arts. \n All methods are fine-tuned on the corresponding tasks. The main metrics (%) are marked in gray. \n Bold emphasizes top method; underline marks the runner-up. C: CIDEr; R: ROUGE-L; B: BLEU.

\n\n

Citation

\n
\n 
\n                            {\n                                `@article{zhou2024embodied,\n    title={Embodied Understanding of Driving Scenarios},\n    author={Zhou, Yunsong and Huang, Linyan and Bu, Qingwen and Zeng, Jia and Li, Tianyu and Qiu, Hang and Zhu, Hongzi and Guo, Minyi and Qiao, Yu and Li, Hongyang},\n    journal={arXiv preprint arXiv:2403.04593},\n    year={2024}\n}`}\n                            \n
\n
\n \n
\n
\n
\n\n \n
\n \n )\n}\n\nexport default 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var IconsManifest = [\n {\n \"id\": \"ci\",\n \"name\": \"Circum Icons\",\n \"projectUrl\": \"https://circumicons.com/\",\n \"license\": \"MPL-2.0 license\",\n \"licenseUrl\": \"https://github.com/Klarr-Agency/Circum-Icons/blob/main/LICENSE\"\n },\n {\n \"id\": \"fa\",\n \"name\": \"Font Awesome 5\",\n \"projectUrl\": \"https://fontawesome.com/\",\n \"license\": \"CC BY 4.0 License\",\n \"licenseUrl\": \"https://creativecommons.org/licenses/by/4.0/\"\n },\n {\n \"id\": \"fa6\",\n \"name\": \"Font Awesome 6\",\n \"projectUrl\": \"https://fontawesome.com/\",\n \"license\": \"CC BY 4.0 License\",\n \"licenseUrl\": \"https://creativecommons.org/licenses/by/4.0/\"\n },\n {\n \"id\": \"io\",\n \"name\": \"Ionicons 4\",\n \"projectUrl\": \"https://ionicons.com/\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/ionic-team/ionicons/blob/master/LICENSE\"\n },\n {\n \"id\": \"io5\",\n \"name\": \"Ionicons 5\",\n \"projectUrl\": \"https://ionicons.com/\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/ionic-team/ionicons/blob/master/LICENSE\"\n },\n {\n \"id\": \"md\",\n \"name\": \"Material Design icons\",\n \"projectUrl\": \"http://google.github.io/material-design-icons/\",\n \"license\": \"Apache License Version 2.0\",\n \"licenseUrl\": \"https://github.com/google/material-design-icons/blob/master/LICENSE\"\n },\n {\n \"id\": \"ti\",\n \"name\": \"Typicons\",\n \"projectUrl\": \"http://s-ings.com/typicons/\",\n \"license\": \"CC BY-SA 3.0\",\n \"licenseUrl\": \"https://creativecommons.org/licenses/by-sa/3.0/\"\n },\n {\n \"id\": \"go\",\n \"name\": \"Github Octicons icons\",\n \"projectUrl\": \"https://octicons.github.com/\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/primer/octicons/blob/master/LICENSE\"\n },\n {\n \"id\": \"fi\",\n \"name\": \"Feather\",\n \"projectUrl\": \"https://feathericons.com/\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/feathericons/feather/blob/master/LICENSE\"\n },\n {\n \"id\": \"lu\",\n \"name\": \"Lucide\",\n \"projectUrl\": \"https://lucide.dev/\",\n \"license\": \"ISC\",\n \"licenseUrl\": \"https://github.com/lucide-icons/lucide/blob/main/LICENSE\"\n },\n {\n \"id\": \"gi\",\n \"name\": \"Game Icons\",\n \"projectUrl\": \"https://game-icons.net/\",\n \"license\": \"CC BY 3.0\",\n \"licenseUrl\": \"https://creativecommons.org/licenses/by/3.0/\"\n },\n {\n \"id\": \"wi\",\n \"name\": \"Weather Icons\",\n \"projectUrl\": \"https://erikflowers.github.io/weather-icons/\",\n \"license\": \"SIL OFL 1.1\",\n \"licenseUrl\": \"http://scripts.sil.org/OFL\"\n },\n {\n \"id\": \"di\",\n \"name\": \"Devicons\",\n \"projectUrl\": \"https://vorillaz.github.io/devicons/\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"ai\",\n \"name\": \"Ant Design Icons\",\n \"projectUrl\": \"https://github.com/ant-design/ant-design-icons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"bs\",\n \"name\": \"Bootstrap Icons\",\n \"projectUrl\": \"https://github.com/twbs/icons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"ri\",\n \"name\": \"Remix Icon\",\n \"projectUrl\": \"https://github.com/Remix-Design/RemixIcon\",\n \"license\": \"Apache License Version 2.0\",\n \"licenseUrl\": \"http://www.apache.org/licenses/\"\n },\n {\n \"id\": \"fc\",\n \"name\": \"Flat Color Icons\",\n \"projectUrl\": \"https://github.com/icons8/flat-color-icons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"gr\",\n \"name\": \"Grommet-Icons\",\n \"projectUrl\": \"https://github.com/grommet/grommet-icons\",\n \"license\": \"Apache License Version 2.0\",\n \"licenseUrl\": \"http://www.apache.org/licenses/\"\n },\n {\n \"id\": \"hi\",\n \"name\": \"Heroicons\",\n \"projectUrl\": \"https://github.com/tailwindlabs/heroicons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"hi2\",\n \"name\": \"Heroicons 2\",\n \"projectUrl\": \"https://github.com/tailwindlabs/heroicons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"si\",\n \"name\": \"Simple Icons\",\n \"projectUrl\": \"https://simpleicons.org/\",\n \"license\": \"CC0 1.0 Universal\",\n \"licenseUrl\": \"https://creativecommons.org/publicdomain/zero/1.0/\"\n },\n {\n \"id\": \"sl\",\n \"name\": \"Simple Line Icons\",\n \"projectUrl\": \"https://thesabbir.github.io/simple-line-icons/\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"im\",\n \"name\": \"IcoMoon Free\",\n \"projectUrl\": \"https://github.com/Keyamoon/IcoMoon-Free\",\n \"license\": \"CC BY 4.0 License\",\n \"licenseUrl\": \"https://github.com/Keyamoon/IcoMoon-Free/blob/master/License.txt\"\n },\n {\n \"id\": \"bi\",\n \"name\": \"BoxIcons\",\n \"projectUrl\": \"https://github.com/atisawd/boxicons\",\n \"license\": \"CC BY 4.0 License\",\n \"licenseUrl\": \"https://github.com/atisawd/boxicons/blob/master/LICENSE\"\n },\n {\n \"id\": \"cg\",\n \"name\": \"css.gg\",\n \"projectUrl\": \"https://github.com/astrit/css.gg\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"vsc\",\n \"name\": \"VS Code Icons\",\n \"projectUrl\": \"https://github.com/microsoft/vscode-codicons\",\n \"license\": \"CC BY 4.0\",\n \"licenseUrl\": \"https://creativecommons.org/licenses/by/4.0/\"\n },\n {\n \"id\": \"tb\",\n \"name\": \"Tabler Icons\",\n \"projectUrl\": \"https://github.com/tabler/tabler-icons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://opensource.org/licenses/MIT\"\n },\n {\n \"id\": \"tfi\",\n \"name\": \"Themify Icons\",\n \"projectUrl\": \"https://github.com/lykmapipo/themify-icons\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/thecreation/standard-icons/blob/master/modules/themify-icons/LICENSE\"\n },\n {\n \"id\": \"rx\",\n \"name\": \"Radix Icons\",\n \"projectUrl\": \"https://icons.radix-ui.com\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/radix-ui/icons/blob/master/LICENSE\"\n },\n {\n \"id\": \"pi\",\n \"name\": \"Phosphor Icons\",\n \"projectUrl\": \"https://github.com/phosphor-icons/core\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/phosphor-icons/core/blob/main/LICENSE\"\n },\n {\n \"id\": \"lia\",\n \"name\": \"Icons8 Line Awesome\",\n \"projectUrl\": \"https://icons8.com/line-awesome\",\n \"license\": \"MIT\",\n \"licenseUrl\": \"https://github.com/icons8/line-awesome/blob/master/LICENSE.md\"\n }\n]","import React from \"react\";\nexport var DefaultContext = {\n color: undefined,\n size: undefined,\n className: undefined,\n style: undefined,\n attr: undefined\n};\nexport var IconContext = React.createContext && React.createContext(DefaultContext);","var __assign = this && this.__assign || function () {\n __assign = Object.assign || function (t) {\n for (var s, i = 1, n = arguments.length; i < n; i++) {\n s = arguments[i];\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\n }\n return t;\n };\n return __assign.apply(this, arguments);\n};\nvar __rest = this && this.__rest || function (s, e) {\n var t = {};\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0) t[p] = s[p];\n if (s != null && typeof Object.getOwnPropertySymbols === \"function\") for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i])) t[p[i]] = s[p[i]];\n }\n return t;\n};\nimport React from \"react\";\nimport { IconContext, DefaultContext } from \"./iconContext\";\nfunction Tree2Element(tree) {\n return tree && tree.map(function (node, i) {\n return React.createElement(node.tag, __assign({\n key: i\n }, node.attr), Tree2Element(node.child));\n });\n}\nexport function GenIcon(data) {\n // eslint-disable-next-line react/display-name\n return function (props) {\n return React.createElement(IconBase, __assign({\n attr: __assign({}, data.attr)\n }, props), Tree2Element(data.child));\n };\n}\nexport function IconBase(props) {\n var elem = function (conf) {\n var attr = props.attr,\n size = props.size,\n title = props.title,\n svgProps = __rest(props, [\"attr\", \"size\", \"title\"]);\n var computedSize = size || conf.size || \"1em\";\n var className;\n if (conf.className) className = conf.className;\n if (props.className) className = (className ? className + \" \" : \"\") + props.className;\n return React.createElement(\"svg\", __assign({\n stroke: \"currentColor\",\n fill: \"currentColor\",\n strokeWidth: \"0\"\n }, conf.attr, attr, svgProps, {\n className: className,\n style: __assign(__assign({\n color: props.color || conf.color\n }, conf.style), props.style),\n height: computedSize,\n width: computedSize,\n xmlns: \"http://www.w3.org/2000/svg\"\n }), title && React.createElement(\"title\", null, title), props.children);\n };\n return IconContext !== undefined ? React.createElement(IconContext.Consumer, null, function (conf) {\n return elem(conf);\n }) : elem(DefaultContext);\n}","module.exports = require('./lib');\n","\"use strict\";Object.defineProperty(exports,\"__esModule\",{value:!0});var React=require(\"react\"),LeftArrow=function(_a){var customLeftArrow=_a.customLeftArrow,getState=_a.getState,previous=_a.previous,disabled=_a.disabled,rtl=_a.rtl;if(customLeftArrow)return React.cloneElement(customLeftArrow,{onClick:function(){return previous()},carouselState:getState(),disabled:disabled,rtl:rtl});var rtlClassName=rtl?\"rtl\":\"\";return React.createElement(\"button\",{\"aria-label\":\"Go to previous slide\",className:\"react-multiple-carousel__arrow react-multiple-carousel__arrow--left \"+rtlClassName,onClick:function(){return previous()},type:\"button\",disabled:disabled})};exports.LeftArrow=LeftArrow;var RightArrow=function(_a){var customRightArrow=_a.customRightArrow,getState=_a.getState,next=_a.next,disabled=_a.disabled,rtl=_a.rtl;if(customRightArrow)return React.cloneElement(customRightArrow,{onClick:function(){return next()},carouselState:getState(),disabled:disabled,rtl:rtl});var rtlClassName=rtl?\"rtl\":\"\";return React.createElement(\"button\",{\"aria-label\":\"Go to next slide\",className:\"react-multiple-carousel__arrow react-multiple-carousel__arrow--right \"+rtlClassName,onClick:function(){return next()},type:\"button\",disabled:disabled})};exports.RightArrow=RightArrow;","\"use strict\";var __extends=this&&this.__extends||function(){var extendStatics=function(d,b){return(extendStatics=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(d,b){d.__proto__=b}||function(d,b){for(var p in b)b.hasOwnProperty(p)&&(d[p]=b[p])})(d,b)};return function(d,b){function __(){this.constructor=d}extendStatics(d,b),d.prototype=null===b?Object.create(b):(__.prototype=b.prototype,new __)}}();Object.defineProperty(exports,\"__esModule\",{value:!0});var React=require(\"react\"),utils_1=require(\"./utils\"),types_1=require(\"./types\"),Dots_1=require(\"./Dots\"),Arrows_1=require(\"./Arrows\"),CarouselItems_1=require(\"./CarouselItems\"),common_1=require(\"./utils/common\"),defaultTransitionDuration=400,defaultTransition=\"transform 400ms ease-in-out\",Carousel=function(_super){function Carousel(props){var _this=_super.call(this,props)||this;return _this.containerRef=React.createRef(),_this.listRef=React.createRef(),_this.state={itemWidth:0,slidesToShow:0,currentSlide:0,totalItems:React.Children.count(props.children),deviceType:\"\",domLoaded:!1,transform:0,containerWidth:0},_this.onResize=_this.onResize.bind(_this),_this.handleDown=_this.handleDown.bind(_this),_this.handleMove=_this.handleMove.bind(_this),_this.handleOut=_this.handleOut.bind(_this),_this.onKeyUp=_this.onKeyUp.bind(_this),_this.handleEnter=_this.handleEnter.bind(_this),_this.setIsInThrottle=_this.setIsInThrottle.bind(_this),_this.next=utils_1.throttle(_this.next.bind(_this),props.transitionDuration||defaultTransitionDuration,_this.setIsInThrottle),_this.previous=utils_1.throttle(_this.previous.bind(_this),props.transitionDuration||defaultTransitionDuration,_this.setIsInThrottle),_this.goToSlide=utils_1.throttle(_this.goToSlide.bind(_this),props.transitionDuration||defaultTransitionDuration,_this.setIsInThrottle),_this.onMove=!1,_this.initialX=0,_this.lastX=0,_this.isAnimationAllowed=!1,_this.direction=\"\",_this.initialY=0,_this.isInThrottle=!1,_this.transformPlaceHolder=0,_this}return __extends(Carousel,_super),Carousel.prototype.resetTotalItems=function(){var _this=this,totalItems=React.Children.count(this.props.children),currentSlide=utils_1.notEnoughChildren(this.state)?0:Math.max(0,Math.min(this.state.currentSlide,totalItems));this.setState({totalItems:totalItems,currentSlide:currentSlide},function(){_this.setContainerAndItemWidth(_this.state.slidesToShow,!0)})},Carousel.prototype.setIsInThrottle=function(isInThrottle){void 0===isInThrottle&&(isInThrottle=!1),this.isInThrottle=isInThrottle},Carousel.prototype.setTransformDirectly=function(position,withAnimation){var additionalTransfrom=this.props.additionalTransfrom;this.transformPlaceHolder=position;var currentTransform=common_1.getTransform(this.state,this.props,this.transformPlaceHolder);this.listRef&&this.listRef.current&&(this.setAnimationDirectly(withAnimation),this.listRef.current.style.transform=\"translate3d(\"+(currentTransform+additionalTransfrom)+\"px,0,0)\")},Carousel.prototype.setAnimationDirectly=function(animationAllowed){this.listRef&&this.listRef.current&&(this.listRef.current.style.transition=animationAllowed?this.props.customTransition||defaultTransition:\"none\")},Carousel.prototype.componentDidMount=function(){this.setState({domLoaded:!0}),this.setItemsToShow(),window.addEventListener(\"resize\",this.onResize),this.onResize(!0),this.props.keyBoardControl&&window.addEventListener(\"keyup\",this.onKeyUp),this.props.autoPlay&&(this.autoPlay=setInterval(this.next,this.props.autoPlaySpeed))},Carousel.prototype.setClones=function(slidesToShow,itemWidth,forResizing,resetCurrentSlide){var _this=this;void 0===resetCurrentSlide&&(resetCurrentSlide=!1),this.isAnimationAllowed=!1;var childrenArr=React.Children.toArray(this.props.children),initialSlide=utils_1.getInitialSlideInInfiniteMode(slidesToShow||this.state.slidesToShow,childrenArr),clones=utils_1.getClones(this.state.slidesToShow,childrenArr),currentSlide=childrenArr.lengthMath.abs(diffY)))return;var _b=utils_1.populateSlidesOnMouseTouchMove(this.state,this.props,this.initialX,this.lastX,clientX,this.transformPlaceHolder),direction=_b.direction,nextPosition=_b.nextPosition,canContinue=_b.canContinue;direction&&(this.direction=direction,canContinue&&void 0!==nextPosition&&this.setTransformDirectly(nextPosition)),this.lastX=clientX}}},Carousel.prototype.handleOut=function(e){this.props.autoPlay&&!this.autoPlay&&(this.autoPlay=setInterval(this.next,this.props.autoPlaySpeed));var shouldDisableOnMobile=\"touchend\"===e.type&&!this.props.swipeable,shouldDisableOnDesktop=(\"mouseleave\"===e.type||\"mouseup\"===e.type)&&!this.props.draggable;if(!shouldDisableOnMobile&&!shouldDisableOnDesktop&&this.onMove){if(this.setAnimationDirectly(!0),\"right\"===this.direction)if(this.initialX-this.lastX>=this.props.minimumTouchDrag){var slidesHavePassed=Math.round((this.initialX-this.lastX)/this.state.itemWidth);this.next(slidesHavePassed)}else this.correctItemsPosition(this.state.itemWidth,!0,!0);if(\"left\"===this.direction)if(this.lastX-this.initialX>this.props.minimumTouchDrag){slidesHavePassed=Math.round((this.lastX-this.initialX)/this.state.itemWidth);this.previous(slidesHavePassed)}else this.correctItemsPosition(this.state.itemWidth,!0,!0);this.resetMoveStatus()}},Carousel.prototype.isInViewport=function(el){var _a=el.getBoundingClientRect(),_b=_a.top,top=void 0===_b?0:_b,_c=_a.left,left=void 0===_c?0:_c,_d=_a.bottom,bottom=void 0===_d?0:_d,_e=_a.right,right=void 0===_e?0:_e;return 0<=top&&0<=left&&bottom<=(window.innerHeight||document.documentElement.clientHeight)&&right<=(window.innerWidth||document.documentElement.clientWidth)},Carousel.prototype.isChildOfCarousel=function(el){return!!(el instanceof Element&&this.listRef&&this.listRef.current)&&this.listRef.current.contains(el)},Carousel.prototype.onKeyUp=function(e){var target=e.target;switch(e.keyCode){case 37:if(this.isChildOfCarousel(target))return this.previous();break;case 39:if(this.isChildOfCarousel(target))return this.next();break;case 9:if(this.isChildOfCarousel(target)&&target instanceof HTMLInputElement&&this.isInViewport(target))return this.next()}},Carousel.prototype.handleEnter=function(e){types_1.isMouseMoveEvent(e)&&this.autoPlay&&this.props.autoPlay&&this.props.pauseOnHover&&(clearInterval(this.autoPlay),this.autoPlay=void 0)},Carousel.prototype.goToSlide=function(slide,skipCallbacks,animationAllowed){var _this=this;if(void 0===animationAllowed&&(animationAllowed=!0),!this.isInThrottle){var itemWidth=this.state.itemWidth,_a=this.props,afterChange=_a.afterChange,beforeChange=_a.beforeChange,previousSlide=this.state.currentSlide;\"function\"!=typeof beforeChange||skipCallbacks&&(\"object\"!=typeof skipCallbacks||skipCallbacks.skipBeforeChange)||beforeChange(slide,this.getState()),this.isAnimationAllowed=animationAllowed,this.props.shouldResetAutoplay&&this.resetAutoplayInterval(),this.setState({currentSlide:slide,transform:-itemWidth*slide},function(){_this.props.infinite&&_this.correctClonesPosition({domLoaded:!0}),\"function\"!=typeof afterChange||skipCallbacks&&(\"object\"!=typeof skipCallbacks||skipCallbacks.skipAfterChange)||(Carousel.afterChangeTimeout3=setTimeout(function(){afterChange(previousSlide,_this.getState())},_this.props.transitionDuration||defaultTransitionDuration))})}},Carousel.prototype.getState=function(){return this.state},Carousel.prototype.renderLeftArrow=function(disbaled){var _this=this,_a=this.props,customLeftArrow=_a.customLeftArrow,rtl=_a.rtl;return React.createElement(Arrows_1.LeftArrow,{customLeftArrow:customLeftArrow,getState:function(){return _this.getState()},previous:this.previous,disabled:disbaled,rtl:rtl})},Carousel.prototype.renderRightArrow=function(disbaled){var _this=this,_a=this.props,customRightArrow=_a.customRightArrow,rtl=_a.rtl;return React.createElement(Arrows_1.RightArrow,{customRightArrow:customRightArrow,getState:function(){return _this.getState()},next:this.next,disabled:disbaled,rtl:rtl})},Carousel.prototype.renderButtonGroups=function(){var _this=this,customButtonGroup=this.props.customButtonGroup;return customButtonGroup?React.cloneElement(customButtonGroup,{previous:function(){return _this.previous()},next:function(){return _this.next()},goToSlide:function(slideIndex,skipCallbacks){return _this.goToSlide(slideIndex,skipCallbacks)},carouselState:this.getState()}):null},Carousel.prototype.renderDotsList=function(){var _this=this;return React.createElement(Dots_1.default,{state:this.state,props:this.props,goToSlide:this.goToSlide,getState:function(){return _this.getState()}})},Carousel.prototype.renderCarouselItems=function(){var clones=[];if(this.props.infinite){var childrenArr=React.Children.toArray(this.props.children);clones=utils_1.getClones(this.state.slidesToShow,childrenArr)}return React.createElement(CarouselItems_1.default,{clones:clones,goToSlide:this.goToSlide,state:this.state,notEnoughChildren:utils_1.notEnoughChildren(this.state),props:this.props})},Carousel.prototype.render=function(){var _a=this.props,deviceType=_a.deviceType,arrows=_a.arrows,renderArrowsWhenDisabled=_a.renderArrowsWhenDisabled,removeArrowOnDeviceType=_a.removeArrowOnDeviceType,infinite=_a.infinite,containerClass=_a.containerClass,sliderClass=_a.sliderClass,customTransition=_a.customTransition,additionalTransfrom=_a.additionalTransfrom,renderDotsOutside=_a.renderDotsOutside,renderButtonGroupOutside=_a.renderButtonGroupOutside,className=_a.className,rtl=_a.rtl;\"production\"!==process.env.NODE_ENV&&utils_1.throwError(this.state,this.props);var _b=utils_1.getInitialState(this.state,this.props),shouldRenderOnSSR=_b.shouldRenderOnSSR,shouldRenderAtAll=_b.shouldRenderAtAll,isLeftEndReach=utils_1.isInLeftEnd(this.state),isRightEndReach=utils_1.isInRightEnd(this.state),shouldShowArrows=arrows&&!(removeArrowOnDeviceType&&(deviceType&&-12*slidesToShow?index+2*slidesToShow:currentSlide>=childrenArr.length?childrenArr.length+index:index}function getOriginalIndexLookupTableByClones(slidesToShow,childrenArr){if(childrenArr.length>2*slidesToShow){for(var table={},firstBeginningOfClones=childrenArr.length-2*slidesToShow,firstEndOfClones=childrenArr.length-firstBeginningOfClones,firstCount=firstBeginningOfClones,i=0;i2*slidesToShow?childrenArr.slice(childrenArr.length-2*slidesToShow,childrenArr.length).concat(childrenArr,childrenArr.slice(0,2*slidesToShow)):childrenArr.concat(childrenArr,childrenArr)}function getInitialSlideInInfiniteMode(slidesToShow,childrenArr){return childrenArr.length>2*slidesToShow?2*slidesToShow:childrenArr.length}function checkClonesPosition(_a,childrenArr,props){var isReachingTheEnd,currentSlide=_a.currentSlide,slidesToShow=_a.slidesToShow,itemWidth=_a.itemWidth,totalItems=_a.totalItems,nextSlide=0,nextPosition=0,isReachingTheStart=0===currentSlide,originalFirstSlide=childrenArr.length-(childrenArr.length-2*slidesToShow);return childrenArr.length2*slidesToShow?((isReachingTheEnd=currentSlide>=originalFirstSlide+childrenArr.length)&&(nextPosition=-itemWidth*(nextSlide=currentSlide-childrenArr.length)),isReachingTheStart&&(nextPosition=-itemWidth*(nextSlide=originalFirstSlide+(childrenArr.length-2*slidesToShow)))):((isReachingTheEnd=currentSlide>=2*childrenArr.length)&&(nextPosition=-itemWidth*(nextSlide=currentSlide-childrenArr.length)),isReachingTheStart&&(nextPosition=props.showDots?-itemWidth*(nextSlide=childrenArr.length):-itemWidth*(nextSlide=totalItems/3))),{isReachingTheEnd:isReachingTheEnd,isReachingTheStart:isReachingTheStart,nextSlide:nextSlide,nextPosition:nextPosition}}Object.defineProperty(exports,\"__esModule\",{value:!0}),exports.getOriginalCounterPart=getOriginalCounterPart,exports.getOriginalIndexLookupTableByClones=getOriginalIndexLookupTableByClones,exports.getClones=getClones,exports.getInitialSlideInInfiniteMode=getInitialSlideInInfiniteMode,exports.checkClonesPosition=checkClonesPosition;","\"use strict\";Object.defineProperty(exports,\"__esModule\",{value:!0});var elementWidth_1=require(\"./elementWidth\");function notEnoughChildren(state){var slidesToShow=state.slidesToShow;return state.totalItems=min&&window.innerWidth<=max&&(slidesToScroll=slidesToSlide)}),slidesToScroll}exports.notEnoughChildren=notEnoughChildren,exports.getInitialState=getInitialState,exports.getIfSlideIsVisbile=getIfSlideIsVisbile,exports.getTransformForCenterMode=getTransformForCenterMode,exports.isInLeftEnd=isInLeftEnd,exports.isInRightEnd=isInRightEnd,exports.getTransformForPartialVsibile=getTransformForPartialVsibile,exports.parsePosition=parsePosition,exports.getTransform=getTransform,exports.getSlidesToSlide=getSlidesToSlide;","\"use strict\";Object.defineProperty(exports,\"__esModule\",{value:!0});var clones_1=require(\"./clones\"),common_1=require(\"./common\");function getLookupTableForNextSlides(numberOfDotsToShow,state,props,childrenArr){var table={},slidesToSlide=common_1.getSlidesToSlide(state,props);return Array(numberOfDotsToShow).fill(0).forEach(function(_,i){var nextSlide=clones_1.getOriginalCounterPart(i,state,childrenArr);if(0===i)table[0]=nextSlide;else{var now=table[i-1]+slidesToSlide;table[i]=now}}),table}exports.getLookupTableForNextSlides=getLookupTableForNextSlides;","\"use strict\";Object.defineProperty(exports,\"__esModule\",{value:!0});var hasWarnAboutTypo=!1;function getPartialVisibilityGutter(responsive,partialVisible,serverSideDeviceType,clientSideDeviceType){var gutter=0,deviceType=clientSideDeviceType||serverSideDeviceType;return partialVisible&&deviceType&&(!hasWarnAboutTypo&&\"production\"!==process.env.NODE_ENV&&responsive[deviceType].paritialVisibilityGutter&&(hasWarnAboutTypo=!0,console.warn(\"You appear to be using paritialVisibilityGutter instead of partialVisibilityGutter which will be moved to partialVisibilityGutter in the future completely\")),gutter=responsive[deviceType].partialVisibilityGutter||responsive[deviceType].paritialVisibilityGutter),gutter}function getWidthFromDeviceType(deviceType,responsive){var itemWidth;responsive[deviceType]&&(itemWidth=(100/responsive[deviceType].items).toFixed(1));return itemWidth}function getItemClientSideWidth(props,slidesToShow,containerWidth){return Math.round(containerWidth/(slidesToShow+(props.centerMode?1:0)))}exports.getPartialVisibilityGutter=getPartialVisibilityGutter,exports.getWidthFromDeviceType=getWidthFromDeviceType,exports.getItemClientSideWidth=getItemClientSideWidth;","\"use strict\";Object.defineProperty(exports,\"__esModule\",{value:!0});var clones_1=require(\"./clones\");exports.getOriginalCounterPart=clones_1.getOriginalCounterPart,exports.getClones=clones_1.getClones,exports.checkClonesPosition=clones_1.checkClonesPosition,exports.getInitialSlideInInfiniteMode=clones_1.getInitialSlideInInfiniteMode;var elementWidth_1=require(\"./elementWidth\");exports.getWidthFromDeviceType=elementWidth_1.getWidthFromDeviceType,exports.getPartialVisibilityGutter=elementWidth_1.getPartialVisibilityGutter,exports.getItemClientSideWidth=elementWidth_1.getItemClientSideWidth;var common_1=require(\"./common\");exports.getInitialState=common_1.getInitialState,exports.getIfSlideIsVisbile=common_1.getIfSlideIsVisbile,exports.getTransformForCenterMode=common_1.getTransformForCenterMode,exports.getTransformForPartialVsibile=common_1.getTransformForPartialVsibile,exports.isInLeftEnd=common_1.isInLeftEnd,exports.isInRightEnd=common_1.isInRightEnd,exports.notEnoughChildren=common_1.notEnoughChildren,exports.getSlidesToSlide=common_1.getSlidesToSlide;var throttle_1=require(\"./throttle\");exports.throttle=throttle_1.default;var throwError_1=require(\"./throwError\");exports.throwError=throwError_1.default;var next_1=require(\"./next\");exports.populateNextSlides=next_1.populateNextSlides;var previous_1=require(\"./previous\");exports.populatePreviousSlides=previous_1.populatePreviousSlides;var mouseOrTouchMove_1=require(\"./mouseOrTouchMove\");exports.populateSlidesOnMouseTouchMove=mouseOrTouchMove_1.populateSlidesOnMouseTouchMove;","\"use strict\";function populateSlidesOnMouseTouchMove(state,props,initialX,lastX,clientX,transformPlaceHolder){var direction,nextPosition,itemWidth=state.itemWidth,slidesToShow=state.slidesToShow,totalItems=state.totalItems,currentSlide=state.currentSlide,infinite=props.infinite,canContinue=!1,slidesHavePassedRight=Math.round((initialX-lastX)/itemWidth),slidesHavePassedLeft=Math.round((lastX-initialX)/itemWidth),isMovingLeft=initialX {\n // Paper title\n return (\n

{children}

\n )\n}\n\nconst Venue: React.FC = ({website, children}) => {\n return (\n \n )\n}\n\nconst Abstract: React.FC = ({children}) => {\n return (\n
\n
\n

Abstract

\n
\n
\n

{\n children\n }

\n
\n
\n )\n}\n\nconst Author: React.FC = ({children, website, firstAuthor, affiliations, lastAuthor}) => {\n return (\n \n \n {children}\n \n {firstAuthor || affiliations ?\n {firstAuthor ?\n * : null}{affiliations ? affiliations : null}\n : null}\n {lastAuthor ? null : <>, }\n \n )\n}\n\nconst Affiliation: React.FC = ({children, website, number}) => {\n return (\n \n {number}\n \n {children}\n \n \n )\n}\n\nconst ActionLink: React.FC = ({children, url, icon}) => {\n return (\n \n \n {icon} \n {children}\n \n \n )\n}\n\nconst Article: React.FC = ({children}) => {\n return (\n \n {children}\n \n )\n}\n\nconst Main: React.FC = ({children}) => {\n return (\n
\n {children}\n
\n )\n}\n\n\nconst FewShotResult: React.FC = ({children, id, demos, demos_label, video, hidden}) => {\n // Result for Few-Shot Manipulation with Demos on left, and video on right\n return (\n // add hidden if hide is true\n
\n
\n

{children}

\n {children}\n

{demos_label}

\n
\n
\n \n
\n
\n )\n}\n\n\nexport const Head: HeadFC = () => ELM: Embodied Understanding of Driving Scenarios\n\nconst carouselResponsive = {\n desktop: {\n breakpoint: {max: 3000, min: 1024},\n items: 3,\n },\n tablet: {\n breakpoint: {max: 1024, min: 464},\n items: 2,\n },\n mobile: {\n breakpoint: {max: 464, min: 0},\n items: 1,\n }\n};\n\n\nconst CarouselItem: React.FC = ({children, video}) => {\n return (\n
\n \n {/*

{children}

*/}\n
\n )\n}\n\nconst IndexPage: React.FC = () => {\n return (\n <>\n
\n
\n \n <span className=\"font-extrabold text-transparent bg-clip-text\n bg-gradient-to-r from-pink-500 via-indigo-600 to-emerald-400\">\n ELM:\n </span>\n  \n <span className=\"text-stone-800\">Embodied Understanding of Driving Scenarios</span>\n \n\n \n ECCV 2024\n \n\n {/* Authors */}\n
\n Yunsong Zhou\n Linyan Huang\n Qingwen Bu\n Jia Zeng\n Tianyu Li
\n Hang Qiu\n Hongzi Zhu\n Minyi Guo\n Yu Qiao\n Hongyang Li\n
\n\n {/* Affilations */}\n
\n OpenDriveLab at Shanghai AI Lab\n Shanghai Jiao Tong University\n University of California, Riverside\n
\n
\n *Indicates equal contribution.\n
\n\n {/* Action Links */}\n

\n }>Paper\n }>Code\n

\n\n \"teaser\"\n
\n

\n ELM is an embodied language model for understanding the long-horizon driving scenarios in space and time. \n Compared to the vanilla vision-language model (VLM) being confined to the scene description task, we expand a wide spectrum of new tasks to fully leverage the capability of large language models in an embodiment setting.\n ELM achieves significant improvements in various applications.\n

\n
\n\n
\n\n {/* Abstract */}\n
\n \n

\n \n Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded upon Vision-Language Models (VLMs). \n Nevertheless, existing VLMs are restricted to the 2D domain, devoid of spatial awareness and long-horizon extrapolation proficiencies. We revisit the key aspects of autonomous driving and formulate appropriate rubrics. \n Hereby, we introduce the Embodied Language Model (ELM), a comprehensive framework tailored for agents' understanding of driving scenes with large spatial and temporal spans. \n ELM incorporates space-aware pre-training to endow the agent with robust spatial localization capabilities. \n Besides, the model employs time-aware token selection to accurately inquire about temporal cues. \n We instantiate ELM on the reformulated multi-faced benchmark, and it surpasses previous state-of-the-art approaches in all aspects.\n \n

\n
\n \n\n
\n\n {/* model */}\n

Methodology

\n

Model Overview

\n \"Description\n

Systematic Pipeline of ELM. It consists of Pre-training by open-world data corpus and Fine-tuning on diverse tasks. \n To initialize the Space-aware Pre-training, we collect extensive image-text pairs from the world, empowering ELM with spatial localization while preserving the description ability in driving scenarios. \n In the fine-tuning process, the inputs to ELM are videos, timestamps, and text prompts. \n After encoding the inputs into tokens, ELM leverages the proposed Time-aware Token Selection to gather the appropriate tokens as instructed by prompts. \n Finally, the tokens are sent to the language model to generate output texts.

\n\n

Space-aware Pre-training

\n \"Description\n

Annotation workflow with human quality check in the loop. \n For location labeling: we first select diverse templates from the GPT generated candidates.\n Pixel-point pairs as annotated in the nuScenes are then sampled and filled into the templates to form our location pre-training data. \n For description labeling: Node 4 utilizes LLaMA-Adapter V2 to obtain diverse labels on nuScenes, Waymo, YouTube, and Ego4D with predefined prompts. \n Two rounds of quality check are conducted in Node 3 and 7 by inspectors to guarantee the image and caption quality.

\n \n \"Description\n

Statistics of pre-training data and comparison of data collection with other VLMs. \n Our pre-train data surpasses that in general vision (top) and autonomous driving (middle) in terms of quantity and diversity. \n Anno: the type of annotations; Des: description; Loc: localization.

\n\n {/* results */}\n

Experiments

\n
\n\n
\n {/* */}\n
\n \n
\"Description
\n
\"Description
\n
\"Description
\n
\"Description
\n
\"Description
\n
\"Description
\n
\"Description
\n
\"Description
\n
\"Description
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\n Visualization on the benchmark. We provide results for ten tasks and E2E planning through videos and corresponding QA pairs.\n

\n {/*
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\n \n

Results

\n \"Description\n

Comparison to State-of-the-arts. \n All methods are fine-tuned on the corresponding tasks. The main metrics (%) are marked in gray. \n Bold emphasizes top method; underline marks the runner-up. C: CIDEr; R: ROUGE-L; B: BLEU.

\n\n

Citation

\n
\n 
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\ No newline at end of file diff --git a/index.html b/index.html index 74ed6db..07ec0c3 100644 --- a/index.html +++ b/index.html @@ -1,6 +1,6 @@ ELM: Embodied Understanding of Driving Scenarios

ELM: Embodied Understanding of Driving Scenarios

ECCV 2024
Yunsong Zhou*1, 2, Linyan Huang*1, Qingwen Bu*1, 2, Jia Zeng1, Tianyu Li1, 
Hang Qiu3, Hongzi Zhu2, Minyi Guo2, Yu Qiao1, Hongyang Li1
1OpenDriveLab at Shanghai AI Lab2Shanghai Jiao Tong University3University of California, Riverside
*Indicates equal contribution.

 Paper Code

teaser

ELM is an embodied language model for understanding the long-horizon driving scenarios in space and time. Compared to the vanilla vision-language model (VLM) being confined to the scene description task, we expand a wide spectrum of new tasks to fully leverage the capability of large language models in an embodiment setting. ELM achieves significant improvements in various applications.

Abstract

Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded upon Vision-Language Models (VLMs). Nevertheless, existing VLMs are restricted to the 2D domain, devoid of spatial awareness and long-horizon extrapolation proficiencies. We revisit the key aspects of autonomous driving and formulate appropriate rubrics. Hereby, we introduce the Embodied Language Model (ELM), a comprehensive framework tailored for agents' understanding of driving scenes with large spatial and temporal spans. ELM incorporates space-aware pre-training to endow the agent with robust spatial localization capabilities. Besides, the model employs time-aware token selection to accurately inquire about temporal cues. We instantiate ELM on the reformulated multi-faced benchmark, and it surpasses previous state-of-the-art approaches in all aspects.

Methodology

Model Overview

Description of the image

Systematic Pipeline of ELM. It consists of Pre-training by open-world data corpus and Fine-tuning on diverse tasks. To initialize the Space-aware Pre-training, we collect extensive image-text pairs from the world, empowering ELM with spatial localization while preserving the description ability in driving scenarios. In the fine-tuning process, the inputs to ELM are videos, timestamps, and text prompts. After encoding the inputs into tokens, ELM leverages the proposed Time-aware Token Selection to gather the appropriate tokens as instructed by prompts. Finally, the tokens are sent to the language model to generate output texts.

Space-aware Pre-training

Description of the image

Annotation workflow with human quality check in the loop. For location labeling: we first select diverse templates from the GPT generated candidates. Pixel-point pairs as annotated in the nuScenes are then sampled and filled into the templates to form our location pre-training data.For description labeling: Node 4 utilizes LLaMA-Adapter V2 to obtain diverse labels on nuScenes, Waymo, YouTube, and Ego4D with predefined prompts. Two rounds of quality check are conducted in Node 3 and 7 by inspectors to guarantee the image and caption quality.

Description of the image

Statistics of pre-training data and comparison of data collection with other VLMs. Our pre-train data surpasses that in general vision (top) and autonomous driving (middle) in terms of quantity and diversity. Anno: the type of annotations; Des: description; Loc: localization.

Experiments

    Visualization on the benchmark. We provide results for ten tasks and E2E planning through videos and corresponding QA pairs.

    Results

    Description of the image

    Comparison to State-of-the-arts. All methods are fine-tuned on the corresponding tasks. The main metrics (%) are marked in gray. Bold emphasizes top method; underline marks the runner-up. C: CIDEr; R: ROUGE-L; B: BLEU.

    Citation

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button:hover:active,.react-multi-carousel-dot--active button{background:#080808}.react-multi-carousel-item{backface-visibility:hidden;transform-style:preserve-3d}@media (-ms-high-contrast:active),(-ms-high-contrast:none){.react-multi-carousel-item{flex-shrink:0!important}.react-multi-carousel-track{overflow:visible!important}}[dir=rtl].react-multi-carousel-list{direction:rtl}.rtl.react-multiple-carousel__arrow--right{left:calc(4% + 1px);right:auto}.rtl.react-multiple-carousel__arrow--right:before{content:"\e824"}.rtl.react-multiple-carousel__arrow--left{left:auto;right:calc(4% + 1px)}.rtl.react-multiple-carousel__arrow--left:before{content:"\e825"}ELM: Embodied Understanding of Driving Scenarios
    ELM: Embodied Understanding of Driving Scenarios
    ECCV 2024
    Yunsong Zhou*1, 2, Linyan Huang*1, Qingwen Bu*1, 2, Jia Zeng1, Tianyu Li1, 
    Hang Qiu3, Hongzi Zhu2, Minyi Guo2, Yu Qiao1, Hongyang Li1
    1OpenDriveLab at Shanghai AI Lab2Shanghai Jiao Tong University3University of California, Riverside
    *Indicates equal contribution.
     Paper Code
    teaser
    ELM is an embodied language model for understanding the long-horizon driving scenarios in space and time. Compared to the vanilla vision-language model (VLM) being confined to the scene description task, we expand a wide spectrum of new tasks to fully leverage the capability of large language models in an embodiment setting. ELM achieves significant improvements in various applications.
    Abstract
    Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded upon Vision-Language Models (VLMs). Nevertheless, existing VLMs are restricted to the 2D domain, devoid of spatial awareness and long-horizon extrapolation proficiencies. We revisit the key aspects of autonomous driving and formulate appropriate rubrics. Hereby, we introduce the Embodied Language Model (ELM), a comprehensive framework tailored for agents' understanding of driving scenes with large spatial and temporal spans. ELM incorporates space-aware pre-training to endow the agent with robust spatial localization capabilities. Besides, the model employs time-aware token selection to accurately inquire about temporal cues. We instantiate ELM on the reformulated multi-faced benchmark, and it surpasses previous state-of-the-art approaches in all aspects.
    Methodology
    Model Overview
    Description of the image
     Systematic Pipeline of ELM. It consists of Pre-training by open-world data corpus and Fine-tuning on diverse tasks. To initialize the Space-aware Pre-training, we collect extensive image-text pairs from the world, empowering ELM with spatial localization while preserving the description ability in driving scenarios. In the fine-tuning process, the inputs to ELM are videos, timestamps, and text prompts. After encoding the inputs into tokens, ELM leverages the proposed Time-aware Token Selection to gather the appropriate tokens as instructed by prompts. Finally, the tokens are sent to the language model to generate output texts.
    Space-aware Pre-training
    Description of the image
     Annotation workflow with human quality check in the loop. For location labeling: we first select diverse templates from the GPT generated candidates. Pixel-point pairs as annotated in the nuScenes are then sampled and filled into the templates to form our location pre-training data.For description labeling: Node 4 utilizes LLaMA-Adapter V2 to obtain diverse labels on nuScenes, Waymo, YouTube, and Ego4D with predefined prompts. Two rounds of quality check are conducted in Node 3 and 7 by inspectors to guarantee the image and caption quality.
    Description of the image
     Statistics of pre-training data and comparison of data collection with other VLMs. Our pre-train data surpasses that in general vision (top) and autonomous driving (middle) in terms of quantity and diversity. Anno: the type of annotations; Des: description; Loc: localization.
    Experiments
      Visualization on the benchmark. We provide results for ten tasks and E2E planning through videos and corresponding QA pairs.
      Results
      Description of the image
       Comparison to State-of-the-arts. All methods are fine-tuned on the corresponding tasks. The main metrics (%) are marked in gray. Bold emphasizes top method; underline marks the runner-up. C: CIDEr; R: ROUGE-L; B: BLEU.
      Citation
      @article{zhou2024embodied,
     title={Embodied Understanding of Driving Scenarios},
     author={Zhou, Yunsong and Huang, Linyan and Bu, Qingwen and Zeng, Jia and Li, Tianyu and Qiu, Hang and Zhu, Hongzi and Guo, Minyi and Qiao, Yu and Li, Hongyang},
     journal={arXiv preprint arXiv:2403.04593},
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diff --git a/webpack-runtime-d8ef33afe23228cd0152.js b/webpack-runtime-9f48d50bb3376aa26333.js
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rename from webpack-runtime-d8ef33afe23228cd0152.js
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