視覚言語モデルによる死角推論と対話型協調知覚の選択的実行
Selective Cooperative Perception via Blind Spot Reasoning and Agentic Dialogue with Vision-Language Models
視覚言語モデルと物体検出・幾何モジュールを組み合わせ、安全上重要な死角のみを対話的に共有する効率的な協調知覚フレームワークを提案し、実車データで検証した。
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著者: Chengyuan Ma, Shixiao Liang, Hangyu Li, Zheng Li, Hang Zhou, Keke Long, Bofeng Cao, Zhaohui Liang, Xiaopeng Li
分類: cs.RO, cs.AI, cs.CV, cs.MA
原文アブストラクト
Real-time cooperative perception (CP) in multi-agent traffic environments is highly resource-intensive, which posing scalability challenges for large-scale deployment. Efficiency can be significantly improved by selectively performing CP only in safety-critical scenarios and for key occluding objects. Vision-Language Models (VLMs) are well-suited for complex reasoning in dynamic and uncertain environments, making them a promising tool for this selective CP task. This study proposes a VLM-driven agentic framework for blind spot reasoning and dialogue-based CP. A mixed-model architecture is designed that integrates conventional object detection for stable perception, vision-language reasoning for identifying impactful occlusions, and a physics-based geometric module to align reasoning with real-world visibility constraints. The framework also incorporates a Chain-of-Thought (CoT) prompting mechanism to enhance interpretability and intent-aware decision-making. We collect real-world data from connected vehicles operating in urban traffic and compare the driving-critical blind spot identification and dialogue performance of four state-of-the-art VLMs. Experimental results demonstrate that our method achieves over 80% accuracy in driving-critical blind spot detection and over 90% success in dialogue-driven perception sharing. This work provides a scalable, interpretable, and bandwidth-efficient alternative to conventional CP pipelines, and offers new insights into agentic communication design for future connected traffic systems.