University of Georgia · Athens, GA
Advancing knowledge and technologies that enhance transportation safety, sustainability, and efficiency — bridging data, theory, and real-world practice.
Research
Evaluating the decision-making behaviors of large language models when applied to autonomous vehicle control and navigation scenarios.
Developing language-model-guided recovery strategies that help autonomous vehicles escape stuck or immobilized situations on public roads.
Mixed wireless charging infrastructure planning for electric bus fleets, balancing cost, coverage, and grid demand.
Fusing LiDAR point clouds with camera imagery to identify high-risk zones, near-miss events, and vulnerable road user behaviors.
Studying how autonomous vehicles interpret and respond to traffic signs and signals, and the downstream effects on overall traffic flow.
Building real-world and miniaturized testbeds to validate cooperative driving algorithms before public road deployment.
Principal Investigator
Assistant Professor · ECAM, University of Georgia
Dr. Li is an Assistant Professor at the School of Environmental, Civil, Agricultural and Mechanical Engineering (ECAM) at the University of Georgia. Her research interests include Connected, Automated, Electric, and Shared Mobility; AI Applications in Transportation; Smart Infrastructure Systems; and Traffic Flow Theory.
Current Lab Members
Ph.D. · Mechanical Engineering
Joined 2024
Research interests: trajectory prediction & planning, 3D computer vision, electronics, and vehicle control.
Ph.D. · Civil Engineering
Joined 2024
Research interests: traffic flow theory, transportation economics, and shared mobility systems.
Ph.D. · Civil Engineering
Joined 2025
Research interests: autonomous driving, cooperative perception, and AI in transportation.
Undergraduate Intern
Summer 2026
Mechanical Engineering, Clemson University.
Alumni
Opportunities
The CAMIs Lab is actively recruiting highly motivated Ph.D. students to work on the next generation of connected and automated transportation systems.
We are a collaborative, interdisciplinary group committed to rigorous science and meaningful real-world impact. Students will have access to full-scale and reduced-scale CAV testbeds, industry partnerships, and a supportive mentorship environment.
Send Your ApplicationPlease include your CV, class ranking, and undergraduate/graduate transcripts in your inquiry.
