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-<!doctype html>
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width,initial-scale=1" />
-    <title>Demo</title>
-    <style>
-      html, body { height: 100%; margin: 0; background: #0b0b0b; }
-      .wrap { height: 100%; display: grid; place-items: center; }
-      video { max-width: 100%; max-height: 100vh; outline: none; }
-      h1 { position: absolute; top: 12px; left: 16px; margin: 0; font: 600 16px/1.2 system-ui, sans-serif; color: #cfcfcf; opacity: .7; }
-    </style>
-  </head>
-  <body>
-    <h1>Demo</h1>
-    <div class="wrap">
-      <video
-        src="demo.mp4"
-        autoplay
-        muted
-        loop
-        playsinline
-        controls
-        preload="metadata">
-        Your browser does not support the video tag.
-      </video>
-    </div>
-  </body>
 </html>

+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1" />
+  <title>AUTOMOTIVE-ENV: Benchmarking Multimodal Agents in Vehicle Interface Systems</title>
+  <meta name="description" content="AUTOMOTIVE-ENV: Benchmarking Multimodal Agents in Vehicle Interface Systems" />
+  <link rel="preconnect" href="https://fonts.googleapis.com">
+  <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+  <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">
+  <style>
+    :root {
+      --bg: #ffffff;
+      --fg: #0a0a0a;
+      --muted: #555;
+      --border: #e6e6e6;
+      --accent: #111;
+      --maxw: 960px;
+      --radius: 14px;
+      --shadow: 0 1px 2px rgba(0,0,0,0.05), 0 6px 20px rgba(0,0,0,0.06);
+    }
+    * { box-sizing: border-box; }
+    html, body { margin: 0; padding: 0; background: var(--bg); color: var(--fg); font-family: Inter, system-ui, -apple-system, Segoe UI, Roboto, Helvetica, Arial, "Apple Color Emoji", "Segoe UI Emoji"; }
+    a { color: var(--accent); text-decoration: none; border-bottom: 1px solid rgba(0,0,0,0.1); }
+    a:hover { border-bottom-color: rgba(0,0,0,0.3); }
+    .wrap { max-width: var(--maxw); margin: 0 auto; padding: 32px 20px 80px; }
+    header { text-align: center; padding: 40px 0 24px; }
+    h1 { font-size: clamp(28px, 4.5vw, 40px); line-height: 1.15; margin: 0 0 16px; letter-spacing: -0.02em; }
+    .lead { color: var(--muted); margin: 8px auto 18px; font-size: clamp(16px, 2vw, 18px); max-width: 840px; }
+    .authors, .affils { margin: 10px auto 0; color: var(--muted); font-size: 15px; }
+    .authors a { border-bottom: 1px dashed rgba(0,0,0,0.2); }
+    .badgebar { display: inline-flex; gap: 10px; margin-top: 18px; }
+    .badge { display: inline-block; font-size: 14px; padding: 8px 12px; border: 1px solid var(--border); border-radius: 999px; box-shadow: var(--shadow); background: #fff; }
+    .section { margin: 30px 0; }
+    .card { border: 1px solid var(--border); border-radius: var(--radius); box-shadow: var(--shadow); background: #fff; padding: 20px; }
+    .card h2 { margin: 0 0 12px; font-size: 22px; }
+    .video { overflow: hidden; }
+    .video video, .video iframe { width: 100%; height: auto; display: block; border-radius: 12px; }
+    .grid { display: grid; grid-template-columns: 1fr; gap: 16px; }
+    @media (min-width: 900px) { .grid.two { grid-template-columns: 1fr 1fr; } }
+    footer { margin-top: 40px; padding-top: 20px; border-top: 1px solid var(--border); color: var(--muted); text-align: center; font-size: 14px; }
+    .mono { font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; font-size: 13px; white-space: pre-wrap; word-break: break-word; background: #fafafa; border: 1px solid var(--border); border-radius: 8px; padding: 12px; }
+  </style>
+</head>
+<body>
+  <div class="wrap">
+    <!-- header -->
+    <header>
+      <h1>AUTOMOTIVE-ENV: BENCHMARKING MULTIMODAL AGENTS IN VEHICLE INTERFACE SYSTEMS</h1>
+      <div class="authors">
+        <strong>Junfeng Yan</strong><sup>*1</sup>, <strong>Biao Wu</strong><sup>*1</sup>, <strong>Meng Fang</strong><sup>2</sup>, <strong>Ling Chen</strong><sup>1</sup>
+      </div>
+      <div class="affils">
+        <sup>1</sup>Australian Artificial Intelligence Institute, Sydney, Australia &nbsp;&nbsp;|&nbsp;&nbsp; <sup>2</sup>University of Liverpool, Liverpool, United Kingdom
+      </div>
+      <p class="lead">
+        Multimodal agents have shown strong general GUI abilities, but in-vehicle systems impose unique constraints: limited driver attention, strict safety, and location-aware interaction. <em>Automotive-ENV</em> is a high-fidelity benchmark and interaction environment for vehicle GUIs with 185 parameterized tasks and reproducible checks. We further propose <em>ASURADA</em>, a geo-aware agent that leverages GPS context for safer decisions.
+      </p>
+      <div class="badgebar">
+        <a class="badge" href="https://arxiv.org/abs/2509.21143" target="_blank" rel="noopener">Paper</a>
+        <a class="badge" href="#" target="_blank" rel="noopener">Code: Release soon</a>
+      </div>
+    </header>
+    <!-- demo video -->
+    <section class="section">
+      <div class="card video" aria-label="demo video">
+        <!-- Place demo.mp4 at the repo root (same folder as this index.html) -->
+        <video src="demo.mp4" autoplay muted loop playsinline controls></video>
+      </div>
+    </section>
+    <!-- abstract + quick highlights -->
+    <section class="section grid two">
+      <div class="card">
+        <h2>Abstract</h2>
+        <p>
+          In-vehicle GUIs present distinct challenges: drivers’ limited attention, strict safety
+          requirements, and complex location-based interaction patterns. We introduce
+          <strong>Automotive-ENV</strong>, the first high-fidelity benchmark and interaction
+          environment tailored for vehicle GUIs. The platform defines <strong>185 parameterized tasks</strong>
+          spanning explicit control, implicit intent, and safety-aware tasks, and provides structured
+          multimodal observations with precise programmatic checks for reproducible evaluation.
+        </p>
+        <p>
+          Building on this benchmark, we propose <strong>ASURADA</strong>, a geo-aware multimodal agent that
+          integrates GPS-informed context to adapt actions by location, environment, and regional norms.
+          Experiments show geo-awareness significantly improves safety-aware task success. We will release
+          Automotive-ENV, with tasks and tooling, to advance safe and adaptive in-vehicle agents.
+        </p>
+      </div>
+      <div class="card">
+        <h2>Highlights</h2>
+        <ul>
+          <li>High-fidelity vehicle GUI environment with reproducible checks.</li>
+          <li>185 parameterized tasks across control, intent, and safety categories.</li>
+          <li>Structured multimodal observations and programmatic success criteria.</li>
+          <li>ASURADA: GPS/geo-aware planning boosts safety-aware task success.</li>
+        </ul>
+      </div>
+    </section>
+    <!-- tasks placeholder (you can expand later) -->
+    <section class="section">
+      <div class="card">
+        <h2>Tasks (preview)</h2>
+        <p>
+          This section is reserved for a compact task overview similar to os-world:
+          categories, difficulty tiers, and a few illustrative examples with thumbnails or short clips.
+        </p>
+        <div class="grid two">
+          <div>
+            <h3>Explicit Control</h3>
+            <ul>
+              <li>Climate, media, navigation, connectivity</li>
+              <li>Deterministic UI manipulations with constraints</li>
+            </ul>
+          </div>
+          <div>
+            <h3>Implicit Intent</h3>
+            <ul>
+              <li>Goal inference from short user context</li>
+              <li>Minimal UI steps with preference awareness</li>
+            </ul>
+          </div>
+          <div>
+            <h3>Safety-Aware</h3>
+            <ul>
+              <li>Sensor + context classification (danger vs. do-nothing)</li>
+              <li>Strict action gating and escalation logic</li>
+            </ul>
+          </div>
+          <div>
+            <h3>Evaluation</h3>
+            <ul>
+              <li>Programmatic checks, success/failure traces</li>
+              <li>Generalization splits and ablations</li>
+            </ul>
+          </div>
+        </div>
+      </div>
+    </section>
+    <!-- bibtex -->
+    <section class="section">
+      <div class="card">
+        <h2>Citation</h2>
+        <pre class="mono">@article{yan2025automotive_env,
+  title   = {AUTOMOTIVE-ENV: Benchmarking Multimodal Agents in Vehicle Interface Systems},
+  author  = {Yan, Junfeng and Wu, Biao and Fang, Meng and Chen, Ling},
+  journal = {arXiv preprint arXiv:2509.21143},
+  year    = {2025}
+}</pre>
+      </div>
+    </section>
+    <footer>
+      © 2025 automotive-env • hosted on GitHub Pages
+    </footer>
+  </div>
+</body>
 </html>