CLIFE: Camera-LiDAR Fusion Framework for Edge-Deployable Roadside VRU Perception
Reliable roadside perception of vulnerable road users (VRUs) remains challenging under occlusions, variable lighting, and diverse weather conditions, particularly under strict edge-computing and latency constraints. Existing multi-sensor fusion systems rely on cloud or server-grade infrastructure, creating a deployment gap at real-world intersections. We present CLIFE, an edge-native camera-LiDAR fusion framework that integrates targetless online calibration and lightweight late-fusion tracking entirely on a single embedded device, without cloud offloading. CLIFE adaptively refines camera-LiDA
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- FuzzyOverlapping authors or contributors · 62%open-webui/open-webui →
“Shared author/contributor keys: nguyen”
- FuzzyOverlapping authors or contributors · 62%DietrichGebert/ponytail →
“Shared author/contributor keys: cheng”
- FuzzyOverlapping authors or contributors · 62%mudler/LocalAI →
“Shared author/contributor keys: guo”
- LinkedLinked via arxiv author · 85%Tam Bang →
“CLIFE: Camera-LiDAR Fusion Framework for Edge-Deployable Roadside VRU Perception”
- LinkedLinked via arxiv author · 85%Hoang H. Nguyen →
“CLIFE: Camera-LiDAR Fusion Framework for Edge-Deployable Roadside VRU Perception”
- LinkedLinked via arxiv author · 85%Lei Cheng →
“CLIFE: Camera-LiDAR Fusion Framework for Edge-Deployable Roadside VRU Perception”
- LinkedLinked via arxiv author · 85%Lihao Guo →
“CLIFE: Camera-LiDAR Fusion Framework for Edge-Deployable Roadside VRU Perception”
- LinkedLinked via arxiv author · 85%Siyang Cao →
“CLIFE: Camera-LiDAR Fusion Framework for Edge-Deployable Roadside VRU Perception”