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app/(default)/(research)/AutonomousDriving/page.tsx

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@@ -92,45 +92,6 @@ export default function Home() {
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{
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publication.timeline.includes("highlight") && (
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<div
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className={`w-full flex gap-3 flex-wrap items-center ${
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index % 2 === 1 ? "md:justify-start" : "md:justify-end"
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}`}
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>
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{
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publication.note != 'arXiv' && (
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publication.noteoption ?
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<Link href={publication.noteoption} target={publication.noteoption.startsWith('http') ? '_blank' : '_self'} className="text-xs text-white bg-gradient-to-br from-o-light-blue via-o-blue to-o-light-blue rounded-sm px-2 py-1.5 hover:scale-105 transition delay-100 duration-200">
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{publication.note}
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</Link>
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:
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<span className="text-xs text-white bg-gradient-to-br from-o-light-blue via-o-blue to-o-light-blue rounded-sm px-2 py-1.5">
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{publication.note}
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</span>
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)
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}
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{
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publication.keys.includes('survey') && (
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<span className="text-xs text-white bg-black rounded-sm px-2 py-1.5">
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Survey
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</span>
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)
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}
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{
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publication.keys.includes('position') && (
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<span className="text-xs text-white bg-black rounded-sm px-2 py-1.5">
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Position Paper
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</span>
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)
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}
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</div>
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)
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}
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{
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publication.timeline.includes("highlight") && (
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<div className="px-20 md:px-0 xl:px-20 w-full flex justify-center items-center relative mt-3">

data/publications.tsx

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@@ -166,7 +166,7 @@ export const publications: {
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description: `🏗️ A scalable simulation pipeline that synthesizes diverse and high-fidelity reactive driving scenarios with pseudo-expert.\n🚀 An effective sim-real co-training strategy that improves robustness and generalization across end-to-end planners.\n🔬 A comprehensive recipe that reveals crucial insights into the underlying scaling properties of sim-real learning systems.`,
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keys: ['end_to_end_ad'],
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time: '2025.11.28',
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timeline:['te2e'],
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timeline:[],
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},
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{
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title: "Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving",
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description: "",
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keys: ['end_to_end_ad'],
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time: '2025.06.11',
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timeline:['te2e'],
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timeline:[],
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},
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{
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title: "UniVLA: Learning to Act Anywhere with Task-centric Latent Actions",
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description: "HDGT formulates the driving scene as a heterogeneous graph with different types of nodes and edges.",
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keys: ['ad_algorithm'],
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time: '2022.04.30',
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timeline:['te2e', 'highlight'],
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timeline:['te2e'],
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},
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{
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title: "Distilling Focal Knowledge from Imperfect Expert for 3D Object Detection",
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description: "We investigate on how to distill the knowledge from an imperfect expert. We propose FD3D, a Focal Distiller for 3D object detection.",
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keys: ['ad_algorithm'],
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time: '2023.06.18',
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timeline:['te2e'],
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timeline:[],
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},
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{
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title: "BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision",
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description: "A novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and better suits modern image backbones.",
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keys: ['ad_algorithm'],
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time: '2022.11.18',
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timeline:['te2e'],
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timeline:[],
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},
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{
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title: "Graph-based Topology Reasoning for Driving Scenes",
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description: "",
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keys: ['end_to_end_ad'],
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time: '2025.06.09',
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timeline:['te2e'],
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timeline:[],
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},
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{
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title: "Decoupled Diffusion Sparks Adaptive Scene Generation",
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description: "",
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keys: ['end_to_end_ad'],
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time: '2025.04.14',
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timeline:['te2e'],
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timeline:[],
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},
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{
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title: "Centaur: Robust End-to-End Autonomous Driving with Test-Time Training",
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description: "",
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keys: ['end_to_end_ad'],
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time: '2025.03.14',
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timeline:['te2e'],
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timeline:[],
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},
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{
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title: "Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability",
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description: "",
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keys: ['end_to_end_ad'],
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time: '2024.09.26',
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timeline:['te2e'],
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timeline:[],
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},
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{
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title: "NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking",
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description: "Revive driving scene understanding by delving into the embodiment philosophy.",
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keys: ['end_to_end_ad'],
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time: '2024.03.07',
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timeline:['te2e'],
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timeline:[],
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},
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{
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title: "DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving",

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