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RoadState: A Domain-Prior-Enhanced Vision-Language System for Industrial-Grade Lane-Level Map Updating — arXiv:2611.10746

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.10746
License (as archived): CC0-1.0
Archived: 2026-11-10T18:42:44.514Z
SHA-256 of the archived text: ae0a5fec81396c0320f9e9eb655ac77cc3f4ca7e6ee32cb11c3f5ffe56490a40
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How to cite

RoadState: A Domain-Prior-Enhanced Vision-Language System for Industrial-Grade Lane-Level Map Updating — arXiv:2611.10746 — arXiv (en), https://arxiv.org/abs/2611.10746 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_445efeb6c836 (Metraipolis archive dc_445efeb6c836, sha256 ae0a5fec81396c0320f9e9eb655ac77cc3f4ca7e6ee32cb11c3f5ffe56490a40)

BibTeX

@misc{dc445efeb6c836,
  title = {RoadState: A Domain-Prior-Enhanced Vision-Language System for Industrial-Grade Lane-Level Map Updating — arXiv:2611.10746},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.10746},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_445efeb6c836; sha256 ae0a5fec81396c0320f9e9eb655ac77cc3f4ca7e6ee32cb11c3f5ffe56490a40},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.10746. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Lane-level map updating requires temporally consistent, physically valid, and auditable road-scene semantics despite occlusions, ambiguous lane markings, temporary obstacles, and viewpoint changes. We present RoadState, a production-deployed vision-language system that transforms sequential road-scene observations into structured lane-level semantics for Baidu Maps' map-updating pipeline. Its core model, RoadState-VLM, formulates road-scene understanding as autoregressive state estimation, predicting the current structured road state from the previous observation and symbolic state together with the current observation. We introduce Geometric Chain-of-Thought (Geo-CoT), which uses a directed acyclic graph of semantic dependencies as a domain prior to improve semantic consistency and reasoning fidelity across interdependent road-state dimensions. Extensive experiments on a real-world dataset show that RoadState-VLM achieves 94.33% mean accuracy across six road-state dimensions, outperforming the strongest fine-tuned VLM baseline. Ablation studies further demonstrate the complementary effectiveness of Geo-CoT and temporal transition context. A cascaded routing pipeline directs routine cases to lightweight models while reserving complex cases for RoadState-VLM. Since its deployment in March 2025, RoadState has supported lane-level map updating across more than 360 cities and reduced overall production costs by 30%. Together with human-in-the-loop auditing, the system maintains robust performance under stress-test conditions. These results demonstrate how domain-prior-enhanced reasoning, structured state interfaces, selective routing, and human quality control can bridge the gap between VLM-based road-scene understanding and large-scale lane-level map updating.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.10746 — 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 2026-11-10T18:37:37.601Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).