{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:47:40Z","timestamp":1778082460133,"version":"3.51.4"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031917660","type":"print"},{"value":"9783031917677","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-91767-7_22","type":"book-chapter","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T17:44:44Z","timestamp":1748281484000},"page":"322-338","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["RoSA Dataset: Road Construct Zone Segmentation for Autonomous Driving"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7323-919X","authenticated-orcid":false,"given":"Jinwoo","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8379-6283","authenticated-orcid":false,"given":"Kyounghwan","family":"An","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4962-8478","authenticated-orcid":false,"given":"Donghwan","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","DOI":"10.4218\/etrij.2023-0017","author":"T An","year":"2023","unstructured":"An, T., Kang, J., Choi, D., Min, K.W.: Crfnet: context refinement network used for semantic segmentation. ETRI J. (2023). https:\/\/doi.org\/10.4218\/etrij.2023-0017","journal-title":"ETRI J."},{"key":"22_CR2","unstructured":"Burnett, K., et\u00a0al.: Boreas: a multi-season autonomous driving dataset. Int. J. Robot. Res. (IJRR) (2023)"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Caesar, H., et al.: Nuscenes: a multimodal dataset for autonomous driving. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11621\u201311631 (2020)","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"22_CR4","unstructured":"Chen, L., Wu, J., Liu, R.: Attention-guided construction zone segmentation for autonomous driving. In: Asian Conference on Computer Vision (2021)"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Cheng, B., Parkhi, O., Kirillov, A.: Pointly-supervised instance segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.00264"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"22_CR8","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: Carla: an open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 1\u201316 (2017)"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Ettinger, S., et\u00a0al.: Large scale interactive motion forecasting for autonomous driving: the waymo open motion dataset. In: Proceedings of the International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00957"},{"key":"22_CR10","unstructured":"Federal Highway Administration: Manual on uniform traffic control devices for streets and highways. Official publication, U.S. Department of Transportation, Washington, D.C. (2009). https:\/\/mutcd.fhwa.dot.gov\/pdfs\/2009r1r2\/mutcd2009r1r2edition.pdf"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., et al.: Simple copy-paste is a strong data augmentation method for instance segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR46437.2021.00294"},{"key":"22_CR13","unstructured":"Ghosh, A., et al.: Roadwork dataset: learning to recognize, observe, analyze and drive through work zones. In: TBD. Carnegie Mellon University (2024). https:\/\/www.cs.cmu.edu\/~ILIM\/roadwork_dataset"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Graf, R., Wimmer, A., Dietmayer, K.C.: Probabilistic estimation of temporary lanes at road work zones. In: Proceedings of the International Conference on Intelligent Transportation Systems (ITSC) (2012)","DOI":"10.1109\/ITSC.2012.6338764"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., Van\u00a0Gool, L.: Daformer: improving network architectures and training strategies for domain-adaptive semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"22_CR16","unstructured":"Hu, A., et al.: Gaia-1: a generative world model for autonomous driving. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023). https:\/\/arxiv.org\/abs\/2309.17080"},{"key":"22_CR17","unstructured":"INAVI: Inavi qxd5000 black box. https:\/\/www.inavi.com\/Products\/BlackBox\/Gate?target=_QXD5000 (2024). product Specification"},{"key":"22_CR18","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: Yolov8: a high-performance object detection and segmentation model. arXiv preprint arXiv:2305.09972 (2023)"},{"key":"22_CR19","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: Yolov8-world: a real-time global object detection and segmentation model. arXiv preprint arXiv:2308.05975 (2023)"},{"key":"22_CR20","doi-asserted-by":"publisher","DOI":"10.4218\/etrij.2021-0055","author":"J Kang","year":"2021","unstructured":"Kang, J., Han, S.J., Kim, N., Min, K.W.: Etli: efficiently annotated traffic lidar dataset using incremental and suggestive annotation. ETRI J. (2021). https:\/\/doi.org\/10.4218\/etrij.2021-0055","journal-title":"ETRI J."},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Kim, H.J., Ohn-Bar, E.: Motion diversification networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1650\u20131660 (2024)","DOI":"10.1109\/CVPR52733.2024.00163"},{"key":"22_CR22","unstructured":"Kim, J., Park, J., Kim, S.: Detection of construction areas in road environments using deep learning. IEEE Trans. Intell. Transp. Syst. (2019)"},{"key":"22_CR23","unstructured":"Kirillov, A., et al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"key":"22_CR24","unstructured":"Kong, H., Audibert, J.Y., Ponce, J.: Seanet: semantic embedding attention network for road detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020)"},{"key":"22_CR25","doi-asserted-by":"publisher","DOI":"10.4218\/etrij.2023-0109","author":"D Lee","year":"2023","unstructured":"Lee, D., Han, S.J., Min, K.W., Choi, J., Park, C.H.: Emos: enhanced moving object detection and classification via sensor fusion and noise filtering. ETRI J. (2023). https:\/\/doi.org\/10.4218\/etrij.2023-0109","journal-title":"ETRI J."},{"key":"22_CR26","doi-asserted-by":"crossref","unstructured":"Li, X., Li, J., Hu, X., Yang, J.: Line-cnn: end-to-end traffic line detection with line proposal unit. IEEE Trans. Intell. Transp. Syst. (2019)","DOI":"10.1109\/TITS.2019.2890870"},{"key":"22_CR27","doi-asserted-by":"publisher","unstructured":"Li, Y., An, Z., Wang, Z., Zhong, Y., Chen, S., Feng, C.: V2x-sim: a virtual collaborative perception dataset for autonomous driving. arXiv preprint arXiv:2202.08449 (2022). https:\/\/doi.org\/10.48550\/ARXIV.2202.08449","DOI":"10.48550\/ARXIV.2202.08449"},{"key":"22_CR28","doi-asserted-by":"crossref","unstructured":"Mathibela, B., Osborne, M.A., Posner, I., Newman, P.: Can priors be trusted? Learning to anticipate roadworks. In: Proceedings of the International Conference on Intelligent Transportation Systems (ITSC) (2012)","DOI":"10.1109\/ITSC.2012.6338696"},{"key":"22_CR29","unstructured":"Ministry of Land, Infrastructure and Transport: Guidelines for traffic management in road construction sites. Technical guidelines, Ministry of Land, Infrastructure and Transport, Sejong, South Korea (2022). http:\/\/www.molit.go.kr\/USR\/I0204\/m_45\/dtl.jsp?idx=15952"},{"key":"22_CR30","unstructured":"Ministry of Transport of the People\u2019s Republic of China: Technical specifications for design and construction of highway traffic safety facilities. Technical standards, Ministry of Transport of the People\u2019s Republic of China, Beijing, China (2016). document Number: JTG D81-2017. https:\/\/www.mot.gov.cn\/zhengcejiedu\/jsdajtssgf\/"},{"key":"22_CR31","unstructured":"National Highway Traffic Safety Administration: Work zone safety. Safety report, U.S. Department of Transportation, Washington, D.C. (2021). https:\/\/www.nhtsa.gov\/road-safety\/work-zone-safety"},{"key":"22_CR32","doi-asserted-by":"crossref","unstructured":"Neuhold, G., Ollmann, T., Rota\u00a0Bul\u00f3, S., Kontschieder, P.: The mapillary vistas dataset for semantic understanding of street scenes. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.534"},{"key":"22_CR33","doi-asserted-by":"publisher","unstructured":"Ohgushi, T., Horiguchi, K., Yamanaka, M. (eds.): Computer Vision \u2013 ACCV 2020. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-69544-6_14","DOI":"10.1007\/978-3-030-69544-6_14"},{"key":"22_CR34","doi-asserted-by":"crossref","unstructured":"Sakaridis, C., Dai, D., Van\u00a0Gool, L.: Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00747"},{"key":"22_CR35","doi-asserted-by":"crossref","unstructured":"Sun, P., et\u00a0al.: Scalability in perception for autonomous driving: waymo open dataset. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2446\u20132454 (2020)","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"22_CR36","unstructured":"Tao, A., Sapra, K., Catanzaro, B.: Hierarchical multi-scale attention for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020)"},{"key":"22_CR37","doi-asserted-by":"crossref","unstructured":"Wang, P., Huang, X., Cheng, X., Zhou, D., Geng, Q., Yang, R.: The apolloscape open dataset for autonomous driving and its application. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2019)","DOI":"10.1109\/TPAMI.2019.2926463"},{"key":"22_CR38","unstructured":"Wang, X., Li, Y., Zhang, H.: Roadworks-net: a real-time semantic segmentation network for road construction zone detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2020)"},{"key":"22_CR39","unstructured":"Wilson, B., et\u00a0al.: Argoverse 2: next generation datasets for self-driving perception and forecasting. arXiv preprint arXiv:2301.00493 (2023)"},{"key":"22_CR40","doi-asserted-by":"crossref","unstructured":"Wimmer, A., Weiss, T., Flogel, F., Dietmayer, K.: Automatic detection and classification of safety barriers in road construction sites using a laser scanner. In: Proceedings of the IEEE Intelligent Vehicles Symposium (2009)","DOI":"10.1109\/IVS.2009.5164342"},{"key":"22_CR41","doi-asserted-by":"crossref","unstructured":"Xu, J., Liu, S., Vahdat, A., Byeon, W., Wang, X., De\u00a0Mello, S.: Open-vocabulary panoptic segmentation with text-to-image diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)","DOI":"10.1109\/CVPR52729.2023.00289"},{"key":"22_CR42","doi-asserted-by":"crossref","unstructured":"Xu, R., Xiang, H., Xia, X., Han, X., Li, J., Ma, J.: Opv2v: an open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 2583\u20132589 (2022)","DOI":"10.1109\/ICRA46639.2022.9812038"},{"key":"22_CR43","doi-asserted-by":"crossref","unstructured":"Yu, F., et al.: Bdd100k: a diverse driving dataset for heterogeneous multitask learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"22_CR44","doi-asserted-by":"crossref","unstructured":"Yu, H., et\u00a0al.: Dair-v2x: a large-scale dataset for vehicle-infrastructure cooperative 3d object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21361\u201321370 (2022)","DOI":"10.1109\/CVPR52688.2022.02067"},{"key":"22_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: A simple framework for open-vocabulary segmentation and detection. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00100"},{"key":"22_CR46","unstructured":"Zhou, Y., Zhu, H., Liu, Z., Shen, S., Wang, H.: Da-roadseg: diverse annotation for real-time road segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91767-7_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T17:44:59Z","timestamp":1748281499000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91767-7_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031917660","9783031917677"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91767-7_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}