Large language models for traffic and transportation research: Methodologies, state of the art, and future opportunities

Xia Wang is a Ph.D. student in Computer Science at Vanderbilt University, where her research focuses on autonomous driving, cyber-physical systems, machine learning, formal methods, and intelligent transportation systems. Her work develops interpretable, safety-aware, and human-centered AI frameworks for autonomous vehicles, including knowledge-integrated end-to-end planning, adaptive cruise control classification, runtime monitoring, and logic-based safety verification. She has contributed to the CIRCLES 100-car open-road experiment and has published research at venues including ICCPS, ITSC, IV, RV, AAMAS, CVPR autopilot workshop and IEEE Control Systems Magazine. Her recent work on NeoAD explores how large-model reasoning, BEV representations, and formal safety robustness can improve autonomous driving planning under diverse and challenging scenarios.

Professor of Computer Science at Vanderbilt University. Research in cyber-physical systems, autonomous vehicles, and domain-specific modeling.