arXiv 2602.229232026-02-26cs.CVcs.RO

WaterVideoQA: 以ASV为中心的感知与符合规则的推理 via 多模态智能体

WaterVideoQA: ASV-Centric Perception and Rule-Compliant Reasoning via Multi-Modal Agents

Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（香港理工大学（广州）人工智能研究所）；Hubei Key Laboratory of Inland Shipping Technology (Wuhan University of Technology)（湖北内河航运技术重点实验室（武汉理工大学））；School of Navigation, Wuhan University of Technology（武汉理工大学航海学院）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学先进科技学院）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；School of Information Engineering, Yancheng Institute of Technology（盐城职业技术学院信息工程学院）；School of Engineering, Stanford University（斯坦福大学工程学院）；Centre for AI and Data Science Innovation and the School of Science and Engineering, James Cook University（詹姆斯库克大学人工智能与数据科学创新中心及科学与工程学院）

Runwei Guan, Shaofeng Liang, Ningwei Ouyang, Weichen Fei, Shanliang Yao, Wei Dai, Chenhao Ge, Penglei Sun, Xiaohui Zhu, Tao Huang, Ryan Wen Liu, Hui Xiong

备注：: 11 pages,8 figures

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AI总结：

WaterVideoQA通过多模态智能体系统，实现ASV在复杂水域环境中的感知与规则合规推理，提升自主航行的安全性和精确性。

摘要：

尽管自主导航在被动感知（例如物体检测和分割）方面取得了显著成功，但其本质上仍然受到知识驱动、互动环境认知的空白所限制。在高风险的海运领域，将原始视觉感知与复杂认知推理之间的差距不仅仅是一种增强，而是自主表面舰船执行安全精确操作的必要前提。为此，我们提出了WaterVideoQA，这是首个大规模、全面的视频问答基准，专门针对所有水域环境设计。该基准包含3,029个视频片段，涵盖六个不同的水域类别，整合了多方面的变量，如易变的光照和动态天气，以严格测试ASV在五级认知框架下的能力。此外，我们引入了NaviMind，一种开创性的多智能体神经符号系统，用于开放式的海运推理。通过结合自适应语义路由、情境感知的分层推理和自主的自我反思验证，NaviMind使ASV从表面的模式匹配过渡到符合规则的、可解释的决策。实验结果表明，我们的框架显著超越了现有基线，建立了在动态海运环境中的智能、可信交互的新范式。

英文摘要：

While autonomous navigation has achieved remarkable success in passive perception (e.g., object detection and segmentation), it remains fundamentally constrained by a void in knowledge-driven, interactive environmental cognition. In the high-stakes domain of maritime navigation, the ability to bridge the gap between raw visual perception and complex cognitive reasoning is not merely an enhancement but a critical prerequisite for Autonomous Surface Vessels to execute safe and precise maneuvers. To this end, we present WaterVideoQA, the first large-scale, comprehensive Video Question Answering benchmark specifically engineered for all-waterway environments. This benchmark encompasses 3,029 video clips across six distinct waterway categories, integrating multifaceted variables such as volatile lighting and dynamic weather to rigorously stress-test ASV capabilities across a five-tier hierarchical cognitive framework. Furthermore, we introduce NaviMind, a pioneering multi-agent neuro-symbolic system designed for open-ended maritime reasoning. By synergizing Adaptive Semantic Routing, Situation-Aware Hierarchical Reasoning, and Autonomous Self-Reflective Verification, NaviMind transitions ASVs from superficial pattern matching to regulation-compliant, interpretable decision-making. Experimental results demonstrate that our framework significantly transcends existing baselines, establishing a new paradigm for intelligent, trustworthy interaction in dynamic maritime environments.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。