{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:31:02Z","timestamp":1760146262483,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T00:00:00Z","timestamp":1728604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["2021R1A2C3008370"],"award-info":[{"award-number":["2021R1A2C3008370"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Drivable Area (DA) detection is crucial for autonomous driving. Camera-based methods rely heavily on illumination conditions and often fail to capture accurate 3D information, while LiDAR-based methods offer accurate 3D data and are less susceptible to illumination conditions. However, existing LiDAR-based methods focus on point-wise detection, so are prone to occlusion and limited by point cloud sparsity, which leads to decreased performance in motion planning and localization. We propose Argoverse-grid, a grid-wise DA detection dataset derived from Argoverse 1, comprising over 20K frames with fine-grained BEV DA labels across various scenarios. We also introduce Grid-DATrNet, a first grid-wise DA detection model utilizing global attention through transformers. Our experiments demonstrate the superiority of Grid-DATrNet over various methods, including both LiDAR and camera-based approaches, in detecting grid-wise DA on the proposed Argoverse-grid dataset. Grid-DATrNet achieves state-of-the-art results with an accuracy of 93.28% and an F1-score of 0.8328. We show that Grid-DATrNet can detect grids even in occluded and unmeasured areas by leveraging contextual and semantic information through global attention, unlike CNN-based DA detection methods. The preprocessing code for Argoverse-grid, experiment code, Grid-DATrNet implementation, and result visualization code are available at AVE Laboratory official git hub.<\/jats:p>","DOI":"10.3390\/rs16203777","type":"journal-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T08:10:16Z","timestamp":1728634216000},"page":"3777","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["See the Unseen: Grid-Wise Drivable Area Detection Dataset and Network Using LiDAR"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0552-5065","authenticated-orcid":false,"given":"Christofel Rio","family":"Goenawan","sequence":"first","affiliation":[{"name":"Robotics Program, KAIST, Dae-jeon 34141, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0008-3726","authenticated-orcid":false,"given":"Dong-Hee","family":"Paek","sequence":"additional","affiliation":[{"name":"Graduate School of Mobility, KAIST, Dae-jeon 34141, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4753-1998","authenticated-orcid":false,"given":"Seung-Hyun","family":"Kong","sequence":"additional","affiliation":[{"name":"Graduate School of Mobility, KAIST, Dae-jeon 34141, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1109\/TITS.2013.2295427","article-title":"Combining Priors, Appearance, and Context for Road Detection","volume":"15","author":"Gevers","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"847406","DOI":"10.1155\/2014\/847406","article-title":"Automatic parking based on a bird\u2019s eye view vision system","volume":"6","author":"Wang","year":"2014","journal-title":"Adv. Mech. Eng."},{"key":"ref_3","unstructured":"Liu, Y., Yuan, T., Wang, Y., Wang, Y., and Zhao, H. (2023, January 23\u201329). Vectormapnet: End-to-end vectorized hd map learning. Proceedings of the International Conference on Machine Learning, Honolulu, HI, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liao, B., Chen, S., Wang, X., Cheng, T., Zhang, Q., Liu, W., and Huang, C. (2023). MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction. arXiv.","DOI":"10.1007\/s11263-024-02235-z"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, Q., Wang, Y., Wang, Y., and Zhao, H. (2022). HDMapNet: An Online HD Map Construction and Evaluation Framework. arXiv.","DOI":"10.1109\/ICRA46639.2022.9812383"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Paigwar, A., Erkent, O., Sierra-Gonzalez, D., and Laugier, C. (2020, January 25\u201329). GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous Vehicles. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9340979"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chang, M.F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., and Ramanan, D. (2019). Argoverse: 3D Tracking and Forecasting with Rich Maps. arXiv.","DOI":"10.1109\/CVPR.2019.00895"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.neucom.2015.05.092","article-title":"Video-based road detection via online structural learning","volume":"168","author":"Yuan","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Aly, M. (2008, January 4\u20136). Real time detection of lane markers in urban streets. Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621152"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1109\/TIP.2010.2045715","article-title":"General Road Detection From a Single Image","volume":"19","author":"Kong","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., and Cottrell, G. (2018). Understanding Convolution for Semantic Segmentation. arXiv.","DOI":"10.1109\/WACV.2018.00163"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017). Pyramid Scene Parsing Network. arXiv.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., and Hajishirzi, H. (2018). ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation. arXiv.","DOI":"10.1007\/978-3-030-01249-6_34"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019). Dual Attention Network for Scene Segmentation. arXiv.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1007\/s11633-022-1339-y","article-title":"YOLOP: You Only Look Once for Panoptic Driving Perception","volume":"19","author":"Wu","year":"2022","journal-title":"Mach. Intell. Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Che, Q.H., Nguyen, D.P., Pham, M.Q., and Lam, D.K. (2023, January 5\u20136). TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars. Proceedings of the 2023 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), Quy Nhon, Vietnam.","DOI":"10.1109\/MAPR59823.2023.10288646"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yuan, T., Liu, Y., Wang, Y., Wang, Y., and Zhao, H. (2023). StreamMapNet: Streaming Mapping Network for Vectorized Online HD Map Construction. arXiv.","DOI":"10.1109\/WACV57701.2024.00719"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Qiao, L., Ding, W., Qiu, X., and Zhang, C. (2023, January 17\u201324). End-to-End Vectorized HD-Map Construction with Piecewise Bezier Curve. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01270"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Blayney, H., Tian, H., Scott, H., Goldbeck, N., Stetson, C., and Angeloudis, P. (2024, January 17\u201321). Bezier Everywhere All at Once: Learning Drivable Lanes as Bezier Graphs. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01455"},{"key":"ref_21","first-page":"3702","article-title":"Compact HD Map Construction via Douglas-Peucker Point Transformer","volume":"38","author":"Liu","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhu, T., Leng, J., Zhong, J., Zhang, Z., and Sun, C. (2024, January 2\u20135). LaneMapNet: Lane Network Recognization and HD Map Construction Using Curve Region Aware Temporal Bird\u2019s-Eye-View Perception. Proceedings of the 2024 IEEE Intelligent Vehicles Symposium (IV), Jeju Island, Republic of Korea.","DOI":"10.1109\/IV55156.2024.10588419"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jia, P., Wen, T., Luo, Z., Yang, M., Jiang, K., Lei, Z., Tang, X., Liu, Z., Cui, L., and Sheng, K. (2024). DiffMap: Enhancing Map Segmentation with Map Prior Using Diffusion Model. arXiv.","DOI":"10.1109\/LRA.2024.3455853"},{"key":"ref_24","unstructured":"Hao, X., Wei, M., Yang, Y., Zhao, H., Zhang, H., Zhou, Y., Wang, Q., Li, W., Kong, L., and Zhang, J. (2024). Is Your HD Map Constructor Reliable under Sensor Corruptions?. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhong, C., Li, B., and Wu, T. (2023). Off-Road Drivable Area Detection: A Learning-Based Approach Exploiting LiDAR Reflection Texture Information. Remote. Sens., 15.","DOI":"10.3390\/rs15010027"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Paek, D.H., Kong, S.H., and Wijaya, K.T. (2022, January 18\u201324). K-lane: Lidar lane dataset and benchmark for urban roads and highways. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00491"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ali, A., Gergis, M., Abdennadher, S., and El Mougy, A. (2021, January 11\u201317). Drivable Area Segmentation in Deteriorating Road Regions for Autonomous Vehicles using 3D LiDAR Sensor. Proceedings of the 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan.","DOI":"10.1109\/IV48863.2021.9575552"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, W. (2010, January 21\u201324). LIDAR-based road and road-edge detection. Proceedings of the 2010 IEEE Intelligent Vehicles Symposium, La Jolla, CA, USA.","DOI":"10.1109\/IVS.2010.5548134"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., and Beijbom, O. (2019, January 15\u201320). Pointpillars: Fast encoders for object detection from point clouds. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01298"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Nagy, I., and Oniga, F. (2021, January 28\u201330). Free Space Detection from Lidar Data Based on Semantic Segmentation. Proceedings of the 2021 IEEE 17th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania.","DOI":"10.1109\/ICCP53602.2021.9733571"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Raguraman, S.J., and Park, J. (August, January 31). Intelligent Drivable Area Detection System using Camera and Lidar Sensor for Autonomous Vehicle. Proceedings of the 2020 IEEE International Conference on Electro Information Technology (EIT), Chicago, IL, USA.","DOI":"10.1109\/EIT48999.2020.9208327"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, L., and Huang, Y. (2022). LiDAR\u2013camera fusion for road detection using a recurrent conditional random field model. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-14438-w"},{"key":"ref_33","first-page":"619","article-title":"Semantic Terrain Classification for Off-Road Autonomous Driving","volume":"Volume 164","author":"Faust","year":"2022","journal-title":"Proceedings of the 5th Conference on Robot Learning"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Caltagirone, L., Scheidegger, S., Svensson, L., and Wahde, M. (2017). Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks. arXiv.","DOI":"10.1109\/IVS.2017.7995848"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Badrinarayanan, V., Kendall, A., and Cipolla, R. (2016). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. arXiv.","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Graham, B. (2015). Sparse 3D convolutional neural networks. arXiv.","DOI":"10.5244\/C.29.150"},{"key":"ref_38","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ku, J., Mozifian, M., Lee, J., Harakeh, A., and Waslander, S.L. (2018, January 1\u20135). Joint 3D proposal generation and object detection from view aggregation. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594049"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Simony, M., Milzy, S., Amendey, K., and Gross, H.M. (2018, January 8\u201314). Complex-yolo: An euler-region-proposal for real-time 3D object detection on point clouds. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11009-3_11"},{"key":"ref_41","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2018). Focal Loss for Dense Object Detection. arXiv.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_43","unstructured":"Tolstikhin, I., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., and Uszkoreit, J. (2021). MLP-Mixer: An all-MLP Architecture for Vision. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. arXiv.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Xia, Z., Pan, X., Song, S., Li, L.E., and Huang, G. (2022). Vision Transformer with Deformable Attention. arXiv.","DOI":"10.1109\/CVPR52688.2022.00475"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3777\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:11:28Z","timestamp":1760112688000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3777"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,11]]},"references-count":45,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16203777"],"URL":"https:\/\/doi.org\/10.3390\/rs16203777","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,10,11]]}}}