{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:58:57Z","timestamp":1791395937002,"version":"4.3.3"},"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1371\/journal.pone.0359959","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:31:21Z","timestamp":1791394281000},"page":"e0359959","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["HSCM-Lane: A ResNet-based encoder-decoder architecture with multi-level shifted-window context modeling for pixel-wise lane segmentation"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7958-6195","authenticated-orcid":true,"given":"Quang Toai","family":"Ton","sequence":"first","affiliation":[{"name":"HUTECH University","place":["Ho Chi Minh City, Vietnam"],"department":["Faculty of Information Technology"]},{"name":"Ho Chi Minh City University of Foreign Languages - Information Technology","place":["Ho Chi Minh City, Vietnam"],"department":["Faculty of Information Technology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thanh Hien","family":"Vu","sequence":"additional","affiliation":[{"name":"HUTECH University","place":["Ho Chi Minh City, Vietnam"],"department":["Faculty of Information Technology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thanh Nguyen","family":"Vu","sequence":"additional","affiliation":[{"name":"Ho Chi Minh City University of Industry and Trade","place":["Ho Chi Minh City, Vietnam"],"department":["Faculty of Information Technology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0909-4974","authenticated-orcid":true,"given":"Tuong","family":"Le","sequence":"additional","affiliation":[{"name":"HUTECH University","place":["Ho Chi Minh City, Vietnam"],"department":["Faculty of Information Technology"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"issue":"1","key":"pone.0359959.ref001","article-title":"Spatial as deep: Spatial CNN for traffic scene understanding","volume":"32","author":"X Pan","year":"2018","journal-title":"AAAI"},{"key":"pone.0359959.ref002","doi-asserted-by":"crossref","unstructured":"Hou Y, Ma Z, Liu C, Loy CC. 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