Vision language models are used in zero-shot mode to infer vehicle make/model/generation and accurate 3D dimensions from image crops, improving label quality and reducing manual effort especially under occlusion.
Determining absence of unreasonable risk: Ap- proval guidelines for an automated driving system deployment,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
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UNVERDICTED 2representative citing papers
Introduces a requirement-based safety argumentation life cycle to promote co-development of automated vehicle systems and their safety arguments from the start.
citing papers explorer
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Improving 3D Labeling in Self-Driving by Inferring Vehicle Information using Vision Language Models
Vision language models are used in zero-shot mode to infer vehicle make/model/generation and accurate 3D dimensions from image crops, improving label quality and reducing manual effort especially under occlusion.
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Approaching Safety-Argumentation-by-Design: A Requirement-based Safety Argumentation Life Cycle for Automated Vehicles
Introduces a requirement-based safety argumentation life cycle to promote co-development of automated vehicle systems and their safety arguments from the start.