The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
Overview
This paper introduces KITScenes Multimodal, a European autonomous driving dataset built around high-fidelity sensors and maps.
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Event evidence and corrections
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Dataset · modalities: high-resolution global-shutter cameras · Differing source assertions
“arXiv:2606.02956v2 Announce Type: replace-cross Abstract: Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. 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 400 m, 4D im”
Exact source · revision 1Source owner not reported
Dataset · modalities: long-range lidar beyond 400 m · Differing source assertions
“arXiv:2606.02956v2 Announce Type: replace-cross Abstract: Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. 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 400 m, 4D im”
Exact source · revision 1Source owner not reported
Dataset · modalities: aging radar · Differing source assertions
“aging 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 st”
Exact source · revision 1Source owner not reported
Dataset · modalities: redundant GNSS/INS localization · Differing source assertions
“aging 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 st”
Exact source · revision 1Source owner not reported
Dataset · tasks: online HD map construction · Differing source assertions
“reet 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. Project page: https://kitscenes.com/multimodal/”
Exact source · revision 1Source owner not reported
Dataset · tasks: long-range depth estimation · Differing source assertions
“reet 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. Project page: https://kitscenes.com/multimodal/”
Exact source · revision 1Source owner not reported
Dataset · tasks: novel view synthesis · Differing source assertions
“reet 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. Project page: https://kitscenes.com/multimodal/”
Exact source · revision 1Source owner not reported
Dataset · tasks: end-to-end driving · Differing source assertions
“reet 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. Project page: https://kitscenes.com/multimodal/”
Exact source · revision 1Source owner not reported
Dataset · embodiments: European dataset built around high-fidelity sensors and maps
“arXiv:2606.02956v2 Announce Type: replace-cross Abstract: Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. 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 400 m, 4D im”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalThe Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
This paper introduces KITScenes Multimodal, a European autonomous driving dataset built around high-fidelity sensors and maps.
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