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

• URL: http://arxiv.org/abs/2602.22923v1
• Date: Thu, 26 Feb 2026 12:12:40 GMT
• Title: WaterVideoQA: ASV-Centric Perception and Rule-Compliant Reasoning via Multi-Modal Agents
• Authors: 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,
• Abstract summary: We present WaterVideoQA, the first large-scale, comprehensive Video Question Answering benchmark specifically engineered for all-waterway environments.<n>We also introduce NaviMind, a pioneering multi-agent neuro-symbolic system designed for open-ended maritime reasoning.
• Score: 23.828845891763617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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