We present RISE (Roadside Infrastructure Sequence Understanding and Evaluation), a framework spanning metric 3D tracking and structured vision-language reasoning in roadside sequences. For metric tracking, our image-only method combines SAM3 video identities with calibration-guided mask agreement for multi-view identit...
Yan-Bo Jiang, Haotian Zheng, Jia-Hao Wang et al.· 0 citations
Driving decisions depend on both where to focus and how to act on what is seen. Effective driving reasoning must establish which objects matter, where they are, and how they inform the intended action. Text-based rationales can describe a driving response while leaving its correspondence to specific visual evidence imp...
Zhi-Yuan Liu, Ze-Hong Ke, Yuan-Xin Tian et al.· 0 citations
Vault is a framework that couples one-step latent generation with sample-based reinforcement learning guidance, and achieves state-of-the-art performance at real-time inference cost, reaching state-of-the-art performance at real-time inference cost.
Yi-Ning Xing, Ze-Hong Ke, Zhi-Yuan Liu et al.· 2 citations
DRIFT, a fixed-depth planner that combines one-step drifting in a compact trajectory latent space with scene-aware proposal aggregation, is presented, showing that one-step latent proposal generation and direct aggregation provide an efficient design for multi-hypothesis motion planning.
Yi-Ning Xing, Zhi-Yuan Liu, Ze-Hong Ke et al.· arXiv.org· 1 citation
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