most citedLeveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.NI2026

Semantic Edge-Cloud Communication for Real-Time Urban Traffic Digital Twins

Murat Arda Onsu, Arman Dogru, Poonam Lohan +2

Urban traffic systems are becoming increasingly complex, demanding real-time traffic perception, proactive collision prediction, and intelligent management to improve safety and re…

cs.CV2026

A Lightweight Digital-Twin-Based Framework for Edge-Assisted Vehicle Tracking and Collision Prediction

Murat Arda Onsu, Poonam Lohan, Burak Kantarci +3

Vehicle tracking, motion estimation, and collision prediction are fundamental components of traffic safety and management in Intelligent Transportation Systems (ITS). Many recent a…

cs.CV2026

Spatiotemporal Semantic V2X Framework for Cooperative Collision Prediction

Murat Arda Onsu, Poonam Lohan, Burak Kantarci +3

Intelligent Transportation Systems (ITS) demand real-time collision prediction to ensure road safety and reduce accident severity. Conventional approaches rely on transmitting raw…

cs.NI2025

Semantic Edge-Cloud Communication for Real-Time Urban Traffic Surveillance with ViT and LLMs over Mobile Networks

Murat Arda Onsu, Poonam Lohan, Burak Kantarci +3

Real-time urban traffic surveillance is vital for Intelligent Transportation Systems (ITS) to ensure road safety, optimize traffic flow, track vehicle trajectories, and prevent col…

cs.AI2025★ 1 cited

Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoring

Murat Arda Onsu, Poonam Lohan, Burak Kantarci +3

A robust and efficient traffic monitoring system is essential for smart cities and Intelligent Transportation Systems (ITS), using sensors and cameras to track vehicle movements, o…