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VehDyn provides a systematic foundation for developing driving world models that are physically consistent and visually realistic, and benchmarks 12 state-of-the-art video world models.
To address the inherent limitations of Vision-Language Models in long-tail object retrieval for autonomous driving, this paper proposes a Dual-Granularity Structured Scene Retrieval (DG-SSR) architecture. By decoupling text queries and visual features, we introduce a parameter-free mechanism that fuses local semantic s...
Vision-Language Models (VLMs) perform well on commonsense reasoning tasks but struggle with visual spatial reasoning. Most existing solutions introduce extra 3D prior inputs or external spatial encoders, which increase complexity and degrade the underlying VLMs'general-purpose capabilities after spatial fine-tuning. To...
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