4D radar provides geometric and motion cues that complement visual semantics, but integrating it into vision-language-action (VLA) models requires both radar--language alignment for semantic reasoning and explicit use of radar measurements for trajectory refinement and selection. To support these capabilities, we const...
Lian-Qing Zheng, Xiao-Kai Bai, Yi-Xuan Luo et al.· 0 citations
Fixed roadside cameras repeatedly observe a stable scene overlaid by sparse moving traffic. Temporal memory can recover weak observations, but reusing moving evidence at stale locations can corrupt occupancy predictions. Motion compensation addresses displacement, while reliance on the resulting history remains a separ...
Xiao-Kai Bai, Lei Yang, Song Wang et al.· 0 citations
4D radar complements dense image semantics with long-range geometry and radial motion, but existing radar--camera detectors largely solve \emph{where} to align the modalities while leaving \emph{whether} a piece of evidence supports an evolving object hypothesis implicit. An image token may describe an occluder, a near...
Xiao-Kai Bai, Zhen-Yu Fan, Lian-Qing Zheng et al.· 0 citations
The Active Cross-View Object Geo-Localization (ActiveGeo), where an agent sequentially selects new viewpoints and determines when to stop, aiming to improve localization with minimal observations is introduced, and an ActiveGeo framework with three-stage training is proposed.
Shun-Yu Yao, Xiao-Han Zhang, Zhuo-Ran Yang et al.· 0 citations
A 4D radar-camera framework for 360 full-scene perception, which models semantic occupancy as a persistent scene state rather than a terminal output, and follows a cross-modal state reasoning paradigm.
This study will provide reliable fundamental support for the training and verification of 4D imaging radar perception algorithms, vehicle decision-making and planning in complex scenarios, and multi-sensor fusion technologies.
Zhuanzhuan Zhao, Xin Zhang, Shengyu Yan et al.· Italian National Conference...· 0 citations
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