Traffic lights skip the perception pipeline: Traffic-VL reads raw camera views and cuts average queues 10.4% over three weeks at ten intersections
Traffic signal control has relied on structured traffic states from dedicated perception pipelines, or on converting simulator images into intermediate representations before deciding. Traffic-VL is an eight-billion-parameter vision-language model that maps synchronized multi-view images and natural-language phase descriptions directly to phase choices, trained with SynTra-V, which turns physics-based simulation into semantically audited photorealistic surveillance imagery. It matches the structured-state Traffic-R1 on five closed-loop benchmarks, reaches 82.1% phase-selection accuracy on real surveillance images, and over three weeks at ten intersections reduced average queue length by 10.4% relative to historical manual operation.