VLMLight: Safety-Critical Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning Architecture

Maonan Wang (The Chinese University of Hong Kong) · Yirong Chen (Shanghai Artificial Intelligence Laboratory) · Aoyu Pang (The Chinese University of Hong Kong) · Yuxin Cai (Carnegie Mellon University) · Chung Shue Chen (Nokia Bell Labs) · Yuheng Kan (Enbodied AI Research Center, Fourier) · Man On Pun (The Chinese University of Hong Kong, Shenzhen)

dual-branch reasoningemergency vehicle prioritizationimage-based traffic simulatorinterpretable ai solutionslarge language model llm agentsmulti-view visual perceptionreal-time decision-makingreinforcement learningrule compliance verificationsafety-prioritized meta-controllerstructured reasoning branchtraffic phase assessmenttraffic signal controlvision-language meta-control

Traffic signal control (TSC) is a core challenge in urban mobility, where real-time decisions must balance efficiency and safety. Existing methods—ranging from rule-based heuristics to reinforcement learning (RL)—often struggle to generalize to complex, dynamic, and safety-critical scenarios. We introduce \textbf{VLMLight}, a novel TSC framework that integrates vision-language meta-control with dual-branch reasoning. At the core of VLMLight is the first image-based traffic simulator that enables multi-view visual perception at intersections, allowing policies to reason over rich cues such as vehicle type, motion, and spatial density. A large language model (LLM) serves as a safety-prioritized meta-controller, selecting between a fast RL policy for routine traffic and a structured reasoning branch for critical cases. In the latter, multiple LLM agents collaborate to assess traffic phases, prioritize emergency vehicles, and verify rule compliance. Experiments show that VLMLight reduces waiting times for emergency vehicles by up to 65% over RL-only systems, while preserving real-time performance in standard conditions with less than 1% degradation. VLMLight offers a scalable, interpretable, and safety-aware solution for next-generation traffic signal control.