Metraipolis › archive › dc_3d248deb91c1

VLALight: Lightweight Vision-Language-Action Models for Emergency-Aware Traffic Signal Control — arXiv:2609.30709

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.30709
License (as archived): CC0-1.0
Archived: 2026-10-08T01:39:26.589Z
SHA-256 of the archived text: f1e993e250e9e72df1a080369ab95bd1f0ecae5d5a7a98a0d1b7a3c558f53396
Stable URL of this page: https://metraipolis.com/documento/dc_3d248deb91c1

How to cite

VLALight: Lightweight Vision-Language-Action Models for Emergency-Aware Traffic Signal Control — arXiv:2609.30709 — arXiv (en), https://arxiv.org/abs/2609.30709 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3d248deb91c1 (Metraipolis archive dc_3d248deb91c1, sha256 f1e993e250e9e72df1a080369ab95bd1f0ecae5d5a7a98a0d1b7a3c558f53396)

BibTeX

@misc{dc3d248deb91c1,
  title = {VLALight: Lightweight Vision-Language-Action Models for Emergency-Aware Traffic Signal Control — arXiv:2609.30709},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.30709},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3d248deb91c1; sha256 f1e993e250e9e72df1a080369ab95bd1f0ecae5d5a7a98a0d1b7a3c558f53396},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.30709. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-28 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.30709 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T01:12:14.466Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).