<strong>We present KITScenes Multimodal, a European dataset built around a high-fidelity sensor suite together with the most complete HD maps for autonomous driving ever released.</strong></p>\n<h2 class=\"relative group flex items-baseline\">\n\t<a id=\"highlights\" class=\"block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full\" href=\"#highlights\" rel=\"nofollow\">\n\t\t<span class=\"header-link\"><svg class=\"text-gray-500 hover:text-black dark:hover:text-gray-200 w-4\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" aria-hidden=\"true\" role=\"img\" width=\"1em\" height=\"1em\" preserveAspectRatio=\"xMidYMid meet\" viewBox=\"0 0 256 256\"><path d=\"M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z\" fill=\"currentColor\"></path></svg></span>\n\t</a>\n\t<span>\n\t\tHighlights\n\t</span>\n</h2>\n<ul>\n<li><strong>European urban focus</strong> — recordings from Karlsruhe, Frankfurt, and Sindelfingen</li>\n<li><strong>High-fidelity sensor suite</strong> — 72.5 MPix global-shutter surround cameras, 7 LiDARs (~906k points/frame on average), 3 Continental ARS548 4D imaging radars, and redundant GNSS/INS</li>\n<li><strong>Long-range sensing</strong> — effective LiDAR range beyond 400 m with substantially higher return density than common public driving datasets</li>\n<li><strong>Production-grade HD maps</strong> — Lanelet2 maps with lane topology, regulatory elements, 29 road-feature classes, 120 traffic-sign classes (GTSIGN-220 taxonomy), and 3D-localized signs, traffic lights, and poles</li>\n<li><strong>Research benchmarks</strong> — relational HD map perception, long-range monocular depth estimation, novel view synthesis, and end-to-end / world-model research</li>\n</ul>\n<p><video src=\"https://cdn-uploads.huggingface.co/production/uploads/67d308b6bf75e7929a799d40/IWs7AX_7mxRdwe31NaXXb.mp4\" controls=\"\" class=\"max-w-full!\"></video></p>\n","updatedAt":"2026-06-05T19:36:34.148Z","author":{"_id":"67d308b6bf75e7929a799d40","avatarUrl":"/avatars/c3cfc3f5df71ee23dd0b41377c2eb457.svg","fullname":"Fabian 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Karlsruhe, Frankfurt, and Sindelfingen\n- **High-fidelity sensor suite** — 72.5 MPix global-shutter surround cameras, 7 LiDARs (~906k points/frame on average), 3 Continental ARS548 4D imaging radars, and redundant GNSS/INS\n- **Long-range sensing** — effective LiDAR range beyond 400 m with substantially higher return density than common public driving datasets\n- **Production-grade HD maps** — Lanelet2 maps with lane topology, regulatory elements, 29 road-feature classes, 120 traffic-sign classes (GTSIGN-220 taxonomy), and 3D-localized signs, traffic lights, and poles\n- **Research benchmarks** — relational HD map perception, long-range monocular depth estimation, novel view synthesis, and end-to-end / world-model research","html":"<p><strong>We present KITScenes Multimodal, a European dataset built around a high-fidelity sensor suite together with the most complete HD maps for autonomous driving ever released.</strong></p>\n<h2 class=\"relative group flex items-baseline\">\n\t<a id=\"highlights\" class=\"block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full\" href=\"#highlights\" rel=\"nofollow\">\n\t\t<span class=\"header-link\"><svg class=\"text-gray-500 hover:text-black dark:hover:text-gray-200 w-4\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" aria-hidden=\"true\" role=\"img\" width=\"1em\" height=\"1em\" preserveAspectRatio=\"xMidYMid meet\" viewBox=\"0 0 256 256\"><path d=\"M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z\" fill=\"currentColor\"></path></svg></span>\n\t</a>\n\t<span>\n\t\tHighlights\n\t</span>\n</h2>\n<ul>\n<li><strong>European urban focus</strong> — recordings from Karlsruhe, Frankfurt, and Sindelfingen</li>\n<li><strong>High-fidelity sensor suite</strong> — 72.5 MPix global-shutter surround cameras, 7 LiDARs (~906k points/frame on average), 3 Continental ARS548 4D imaging radars, and redundant GNSS/INS</li>\n<li><strong>Long-range sensing</strong> — effective LiDAR range beyond 400 m with substantially higher return density than common public driving datasets</li>\n<li><strong>Production-grade HD maps</strong> — Lanelet2 maps with lane topology, regulatory elements, 29 road-feature classes, 120 traffic-sign classes (GTSIGN-220 taxonomy), and 3D-localized signs, traffic lights, and poles</li>\n<li><strong>Research benchmarks</strong> — relational HD map perception, long-range monocular depth estimation, novel view synthesis, and end-to-end / world-model research</li>\n</ul>\n","updatedAt":"2026-06-05T19:38:42.419Z","author":{"_id":"67d308b6bf75e7929a799d40","avatarUrl":"/avatars/c3cfc3f5df71ee23dd0b41377c2eb457.svg","fullname":"Fabian Immel","name":"immel-f","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7375171184539795},"editors":["immel-f"],"editorAvatarUrls":["/avatars/c3cfc3f5df71ee23dd0b41377c2eb457.svg"],"reactions":[],"isReport":false}},{"id":"6a237c0d3da15f2b696638da","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot 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I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [CARD: A Multi-Modal Automotive Dataset for Dense 3D Reconstruction in Challenging Road Topography](https://huggingface.co/papers/2605.05014) (2026)\n* [4DLidarOpen: An Open 4D FMCW Lidar Dataset for Motion-Aware Autonomous Driving](https://huggingface.co/papers/2605.18074) (2026)\n* [123D: Unifying Multi-Modal Autonomous Driving Data at Scale](https://huggingface.co/papers/2605.08084) (2026)\n* [MapATM: Enhancing HD Map Construction through Actor Trajectory Modeling](https://huggingface.co/papers/2604.11081) (2026)\n* [Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving](https://huggingface.co/papers/2605.22809) (2026)\n* [nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving](https://huggingface.co/papers/2605.31572) (2026)\n* [EagleVision: A Multi-Task Benchmark for Cross-Domain Perception in High-Speed Autonomous Racing](https://huggingface.co/papers/2604.11400) (2026)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"<p>This is an automated message from the <a href=\"https://huggingface.co/librarian-bots\">Librarian Bot</a>. I found the following papers similar to this paper. </p>\n<p>The following papers were recommended by the Semantic Scholar API </p>\n<ul>\n<li><a href=\"https://huggingface.co/papers/2605.05014\">CARD: A Multi-Modal Automotive Dataset for Dense 3D Reconstruction in Challenging Road Topography</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.18074\">4DLidarOpen: An Open 4D FMCW Lidar Dataset for Motion-Aware Autonomous Driving</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.08084\">123D: Unifying Multi-Modal Autonomous Driving Data at Scale</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.11081\">MapATM: Enhancing HD Map Construction through Actor Trajectory Modeling</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.22809\">Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.31572\">nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.11400\">EagleVision: A Multi-Task Benchmark for Cross-Domain Perception in High-Speed Autonomous Racing</a> (2026)</li>\n</ul>\n<p> Please give a thumbs up to this comment if you found it helpful!</p>\n<p> If you want recommendations for any Paper on Hugging Face checkout <a href=\"https://huggingface.co/spaces/librarian-bots/recommend_similar_papers\">this</a> Space</p>\n<p> You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: <code><span class=\"SVELTE_PARTIAL_HYDRATER contents\" data-target=\"UserMention\" data-props=\"{&quot;user&quot;:&quot;librarian-bot&quot;}\"><span class=\"inline-block\"><span class=\"contents\"><a href=\"/librarian-bot\">@<span class=\"underline\">librarian-bot</span></a></span> </span></span> recommend</code></p>\n","updatedAt":"2026-06-06T01:46:53.857Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":362,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7075566649436951},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.02956","authors":[{"_id":"6a20279c15100c5272a83fef","user":{"_id":"63722445e8891da06b2a2e6e","avatarUrl":"/avatars/8e1fddd6c012e58867332b972d6d783c.svg","isPro":false,"fullname":"Richard Schwarzkopf","user":"R-Fehler","type":"user","name":"R-Fehler"},"name":"Richard 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We present KITScenes Multimodal, a European dataset built around high-fidelity sensors and maps. Our fully synchronized sensor suite combines high-resolution global-shutter cameras, long-range lidar beyond 400m, 4D imaging radar, and redundant GNSS/INS localization. Our HD maps are, to our knowledge, the most complete of any sensor dataset, validated through autonomous driving trials on open-source software. For the first time in a public dataset, all driving-relevant traffic elements, such as traffic lights, are mapped in 3D to a reprojection-accurate level with full topological connectivity. Recorded in cities with irregular street layouts and mixed traffic modes, our dataset complements existing datasets by broadening the available geographic diversity. We also introduce four benchmarks, each advancing spatial learning for embodied AI: online HD map construction, long-range depth estimation, novel view synthesis, and end-to-end driving. 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