VLALight: Lightweight Vision-Language-Action Models for Emergency-Aware Traffic Signal Control
cs.CV, cs.AI
Submitted: 2026-09-25
Updated: 2026-09-25
Comments: 9 pages, 7 figures
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: Traffic signal control (TSC) is essential for mitigating urban congestion.
Terminology
Abstract
Traffic signal control (TSC) is essential for mitigating urban congestion. Recent advances in vision-language models (VLMs) enable richer interpretation of intersection scenes, opening new opportunities for visual-context-aware TSC. However, the loose coupling and repeated information conversion between modules can lead to the loss of fine-grained visual details, while sequential inference introduces substantial latency. To address these limitations, we propose VLALight, a lightweight end-to-end vision-language-action framework that directly maps intersection observations and signal-phase information to discrete signal actions. To handle the multi-view nature of TSC, VLALight combines multiple directional camera views into a unified visual input and uses textual instructions to establish their correspondence with traffic movements and signal phases. This design enables direct action prediction with a compact 0.5 B-parameter model, without intermediate image-to-text descriptions or handcrafted traffic-state representations. Experiments show that VLALight delivers the best emergency-vehicle service of all compared methods, reducing pooled emergency waiting time by 21.1% over the cascaded VLMLight while running in real time on local hardware and generalizing to unseen intersection topologies and traffic-flow patterns.
Sources
- Proximal Policy Optimization Algorithms
- LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments
- TranSimHub:A Unified Air-Ground Simulation Platform for Multi-Modal Perception and Decision-Making
- Qwen2.5 Technical Report
- VLA-R1: Enhancing Reasoning in Vision-Language-Action Models
- A Survey on Efficient Vision-Language-Action Models
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