{
  "abstract": "Pixel-wise lane segmentation provides dense geometric cues for lane keeping, road-structure inference, and local trajectory planning in advanced driver-assistance systems and autonomous vehicles. The objective is to improve binary lane-mask segmentation under sparse, thin, low-contrast, occluded, and discontinuous lane-marking conditions while retaining a compact real-time configuration. To this end, we propose HSCM-Lane, a ResNet-based encoder-decoder architecture enhanced by a Hierarchical Swin Context Module (HSCM). HSCM organizes shifted-window context modeling at the 1/4, 1/8, and 1/16 encoder feature levels, so that context-enhanced features are propagated through subsequent residual stages and reused by a lightweight decoder with projected static-sum fusion. For backbone-capacity analysis, the architecture is evaluated as HSCM-Lane-S (Small, ResNet18), HSCM-Lane-M (Medium, ResNet34), and HSCM-Lane-L (Large, ResNet50). These configurations differ only in the ResNet backbone, while the HSCM placement, window size, depth, projected static-sum fusion strategy, and Focal-Tversky objective are kept fixed. On the BDD100K validation set, HSCM-Lane-S achieves 34.44% lane-class intersection over union (IoU), 65.00% lane recall, and 82.12% balanced accuracy, compared with 33.00%, 62.48%, and 80.38% for the no-HSCM counterpart. Relative to the no-HSCM counterpart, these values represent gains of 1.44 percentage points in lane IoU, 2.52 points in lane recall, and 1.74 points in balanced accuracy. The lane-IoU gain corresponds to a 4.36% relative improvement. Scaling the backbone yields 34.86% lane IoU for HSCM-Lane-M and 34.98% lane IoU for HSCM-Lane-L, showing a gradual quality-cost trade-off. Under a derived pixel-level out-of-domain evaluation protocol on TuSimple, HSCM-Lane-S, HSCM-Lane-M, and HSCM-Lane-L achieve 27.2%, 27.6%, and 27.6% lane IoU, respectively, without target-domain fine-tuning. These results indicate that multi-level shifted-window context modeling improves the no-HSCM counterpart and yields competitive lane-mask segmentation results under these protocols. Future work will address temporal, instance-level, multimodal, planning, and safety validation.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam",
    "Faculty of Information Technology, Ho Chi Minh City University of Foreign Languages - Information Technology, Ho Chi Minh City, Vietnam",
    "Faculty of Information Technology, Ho Chi Minh City University of Industry and Trade, Ho Chi Minh City, Vietnam"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
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      "name": "Quang Toai Ton"
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    {
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      "name": "Thanh Hien Vu"
    },
    {
      "affs": [
        2
      ],
      "name": "Thanh Nguyen Vu"
    },
    {
      "name": "Tuong Le"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8k4pv0wjnajp3.bib",
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  "doi": "10.1371/journal.pone.0359959",
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  "licence": "CC-BY-4.0",
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  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359959&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/8k4pv0wjnajp3.ris",
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  "title": "HSCM-Lane: A ResNet-based encoder-decoder architecture with multi-level shifted-window context modeling for pixel-wise lane segmentation",
  "title_text": "HSCM-Lane: A ResNet-based encoder-decoder architecture with multi-level shifted-window context modeling for pixel-wise lane segmentation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/8k4pv0wjnajp3",
  "venue": "PLOS One",
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