Selective Cooperative Perception via Blind Spot Reasoning and Agentic Dialogue with Vision-Language Models
Overview
This paper introduces a VLM-driven agentic framework for selective cooperative perception in multi-agent traffic environments. The system integrates conventional object detection, vision-language reasoning for occlusion identification, and a physics-based geometric module to align reasoning with visibility constraints. Using real-world data from connected vehicles in urban traffic, the authors evaluate four state-of-the-art VLMs, reporting over 80% accuracy in driving-critical blind spot detection and over 90% success in dialogue-driven perception sharing. The work aims to provide a scalable, interpretable, and bandwidth-efficient alternative to conventional cooperative perception pipelines.
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Reported quantity · Reported accuracy: 80 percent · Basis not reported · Differing source assertions
“od 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.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported accuracy: 90 percent · Basis not reported · Differing source assertions
“od 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.”
Exact source · revision 1Source owner not reported
Dataset · modalities: Vision-Language Models · Differing source assertions
“arXiv:2611.10819v1 Announce Type: new Abstract: 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 i”
Exact source · revision 1Source owner not reported
“n 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 reasonin”
Exact source · revision 1Source owner not reported
Dataset · modalities: conventional object detection · Differing source assertions
“n 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 reasonin”
Exact source · revision 1Source owner not reported
Dataset · tasks: blind spot reasoning
“n 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 reasonin”
Exact source · revision 1Source owner not reported
“od 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.”
Exact source · revision 1Source owner not reported
Dataset · embodiments: connected vehicles operating in urban traffic
“g 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 meth”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSelective Cooperative Perception via Blind Spot Reasoning and Agentic Dialogue with Vision-Language Models
This paper introduces a VLM-driven agentic framework for selective cooperative perception in multi-agent traffic environments. The system integrates conventional object detection, vision-language reasoning for occlusion identification, and a physics-based geometric module to align reasoning with visibility constraints. Using real-world data from connected vehicles in urban traffic, the authors evaluate four state-of-the-art VLMs, reporting over 80% accuracy in driving-critical blind spot detection and over 90% success in dialogue-driven perception sharing. The work aims to provide a scalable, interpretable, and bandwidth-efficient alternative to conventional cooperative perception pipelines.
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