Two undergraduate researchers complete UROP projects: TMC-Agent and HighwayVLM

Through the Undergraduate Research Opportunities Program (UROP), Leo Curtis advanced TMC-Agent — an agentic AI system for traffic management — and Ismail Yusuf built HighwayVLM, a hybrid computer-vision + vision-language-model highway incident-detection system.

Two talented undergraduate students worked with the lab through the Undergraduate Research Opportunities Program (UROP) this semester.

Leo Curtis worked on TMC-Agent, an agentic AI system that digests multi-source data streams — traffic detector data, aggregated probe-vehicle speeds, and camera video streams — to deliver traffic operators actionable recommendations and real-time status reports.

Ismail Yusuf worked on HighwayVLM, a highway incident-detection system that combines classical computer vision with Vision-Language Models (VLMs) in a hybrid architecture for robust, context-aware detection.

Both projects push the boundaries of how AI can support real-world transportation operations, and it’s been genuinely rewarding to see their curiosity, rigor, and ownership throughout the semester.

View the announcement on LinkedIn ↗