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Edge-native CLIFE framework enhances roadside VRU perception

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CLIFE, a new framework for edge-deployable roadside perception of vulnerable road users (VRUs). This system integrates targetless online calibration and lightweight late-fusion tracking, operating entirely on a single embedded device without cloud offloading. CLIFE demonstrates substantial improvements in perceptual range and robustness compared to individual sensors, achieving a high throughput of 53.2 FPS on the Jetson AGX Thor, making it suitable for real-time intersection applications. AI

IMPACT This edge-native framework could enable more robust and real-time safety applications at intersections by improving perception of vulnerable road users.

RANK_REASON The cluster contains a research paper detailing a new framework for computer vision. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Edge-native CLIFE framework enhances roadside VRU perception

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The cluster contains a research paper detailing a new framework for computer vision. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Tam Bang, Hoang H. Nguyen, Lei Cheng, Lihao Guo, Siyang Cao, Hussam Abubakr, Tianya Zhang, Austin Harris, Mina Sartipi ·

    CLIFE: Camera-LiDAR Fusion Framework for Edge-Deployable Roadside VRU Perception

    arXiv:2607.16154v1 Announce Type: new Abstract: 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-s…

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