UniUncer is a plug-and-play uncertainty framework that jointly models static and dynamic scene uncertainty inside end-to-end planners, cutting L2 trajectory error 7% on nuScenes and raising EPDMS 10.8% on NavsimV2.
nuscenes: A multimodal dataset for autonomous driving,
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
UMPE fuses any subset of HD/SD vector maps, raster SD maps, and satellite imagery into BEV features via alignment-aware vector and raster branches, raising mapping mAP by 5.3-5.9 points and cutting planning L2 error by 0.30 m on nuScenes.
ROVR is a new diverse depth dataset for autonomous driving with 200K frames, released pipelines, and ablations showing sparse ground truth supports model training.
citing papers explorer
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UniUncer: Unified Dynamic Static Uncertainty for End to End Driving
UniUncer is a plug-and-play uncertainty framework that jointly models static and dynamic scene uncertainty inside end-to-end planners, cutting L2 trajectory error 7% on nuScenes and raising EPDMS 10.8% on NavsimV2.
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Unified Map Prior Encoder for Mapping and Planning
UMPE fuses any subset of HD/SD vector maps, raster SD maps, and satellite imagery into BEV features via alignment-aware vector and raster branches, raising mapping mAP by 5.3-5.9 points and cutting planning L2 error by 0.30 m on nuScenes.
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ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Driving
ROVR is a new diverse depth dataset for autonomous driving with 200K frames, released pipelines, and ablations showing sparse ground truth supports model training.