VeriDrive introduces a verifiable counterfactual supervision framework using a Perception-Evaluation-Revision chain and validator-guided correction to generate cost-efficient structured data for vision-language driving models, showing metric gains on nuScenes.
In: Computer Vision – ECCV 2022
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GOLD-BEV learns dense BEV semantic maps including dynamic agents from ego-centric sensors by using synchronized aerial imagery for training supervision and pseudo-label generation.
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VeriDrive: Verifiable Counterfactual Supervision for Cost-Efficient Vision-Language Planning
VeriDrive introduces a verifiable counterfactual supervision framework using a Perception-Evaluation-Revision chain and validator-guided correction to generate cost-efficient structured data for vision-language driving models, showing metric gains on nuScenes.
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GOLD-BEV: GrOund and aeriaL Data for Dense Semantic BEV Mapping of Dynamic Scenes
GOLD-BEV learns dense BEV semantic maps including dynamic agents from ego-centric sensors by using synchronized aerial imagery for training supervision and pseudo-label generation.