{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:53:31Z","timestamp":1775264011801,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,5]],"date-time":"2018-11-05T00:00:00Z","timestamp":1541376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61803298"],"award-info":[{"award-number":["61803298"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Road scene model construction is an important aspect of intelligent transportation system research. This paper proposes an intelligent framework that can automatically construct road scene models from image sequences. The road and foreground regions are detected at superpixel level via a new kind of random walk algorithm. The seeds for different regions are initialized by trapezoids that are propagated from adjacent frames using optical flow information. The superpixel level region detection is implemented by the random walk algorithm, which is then refined by a fast two-cycle level set method. After this, scene stages can be specified according to a graph model of traffic elements. These then form the basis of 3D road scene models. Each technical component of the framework was evaluated and the results confirmed the effectiveness of the proposed approach.<\/jats:p>","DOI":"10.3390\/s18113782","type":"journal-article","created":{"date-parts":[[2018,11,5]],"date-time":"2018-11-05T10:43:45Z","timestamp":1541414625000},"page":"3782","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Road Scene Simulation Based on Vehicle Sensors: An Intelligent Framework Using Random Walk Detection and Scene Stage Reconstruction"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5741-5280","authenticated-orcid":false,"given":"Yaochen","family":"Li","sequence":"first","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Xi\u2019an 710049, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhichao","family":"Cui","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Xi\u2019an 710049, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuehu","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Xi\u2019an 710049, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihua","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Xi\u2019an 710049, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danchen","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Xi\u2019an 710049, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Xi\u2019an 710049, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1109\/TITS.2015.2497408","article-title":"Three-dimensional traffic scenes simulation from road image sequences","volume":"17","author":"Li","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","first-page":"1662","article-title":"Spatio-temporal traffic scene modeling for object motion detection","volume":"14","author":"Hao","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","unstructured":"(2018, November 05). Simulation of ADAS and Active Safety. Available online: http:\/\/www.tassinternational.com\/prescan."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1007\/s00170-011-3588-8","article-title":"Vehicle dynamics analysis of a heavy-duty commercial vehicle by using multibody simulation methods","volume":"60","author":"Hasagasioglu","year":"2012","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MC.2010.170","article-title":"Google street view: Capturing the world at street level","volume":"42","author":"Anguelov","year":"2010","journal-title":"Computer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1145\/1778765.1778833","article-title":"Street slide: Browsing street level imagery","volume":"29","author":"Kopf","year":"2010","journal-title":"ACM Trans. Graph."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Schick, A., Bauml, M., and Stiefelhagen, R. (2012, January 16\u201321). Improving foreground segmentations with probabilistic superpixel Markov random fields. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA.","DOI":"10.1109\/CVPRW.2012.6238923"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"29594","DOI":"10.3390\/s151129594","article-title":"Vision sensor-based road detection for field robot navigation","volume":"15","author":"Lu","year":"2015","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1109\/TITS.2012.2223686","article-title":"Random-walker monocular road detection in adverse conditions using automated spatiotemporal seed selection","volume":"14","author":"Siogkas","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1109\/TIP.2014.2302892","article-title":"Lazy random walks for superpixel segmentation","volume":"23","author":"Shen","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tian, X., and Jung, C. (2015, January 27\u201330). Point-cut: Fixation point-based image segmentation using random walk model. Proceedings of the 2015 IEEE International Conference on Image Processing, Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351176"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Teichmann, M., Weber, M., Zoellner, M., Cipolla, R., and Urtasun, R. (2018, January 26\u201330). MultiNet: Real-time joing semantic reasoning for autonomous driving. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500504"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving: The KITTI vision benchmark suite. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the 2014 European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1109\/TPAMI.2008.132","article-title":"Make3D: Learning 3D scene structure from a single still image","volume":"31","author":"Saxena","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s11263-006-0031-y","article-title":"Recovering surface layout from an image","volume":"75","author":"Hoiem","year":"2007","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Delage, E., Lee, H., and Ng, A.Y. (2006, January 17\u201322). A dynamic Bayesian model for autonomous 3D reconstruction from a single indoor image. Proceedings of the 2006 IEEE Conference on Computer Vision and Pattern Recognition, New York, NY, USA.","DOI":"10.1109\/CVPR.2006.23"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Horry, Y., Anjyo, K., and Arai, K. (1997, January 3\u20138). Tour into the picture: Using a spidery interface to make animation from a single image. Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, Los Angeles, CA, USA.","DOI":"10.1145\/258734.258854"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.1109\/TPAMI.2009.174","article-title":"Stages as models of scene geometry","volume":"32","author":"Nedovic","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3098","DOI":"10.1109\/TIP.2015.2431443","article-title":"Extracting 3D layout from a single image using global image structures","volume":"24","author":"Lou","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","unstructured":"Bouguet, J.Y. (1999). Pyramidal Implementation of the Lucas-Kanade Feature Tracker, Intel Corporation, Microprocessor Research Labs."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","article-title":"Determining optical flow","volume":"17","author":"Horn","year":"1981","journal-title":"Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1023\/B:VISI.0000045324.43199.43","article-title":"Lucas\/Kanade meets Horn\/Schunck: Combining local and global optical flow methods","volume":"61","author":"Bruhn","year":"2005","journal-title":"Int. J. Comput. Vis."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1109\/34.537343","article-title":"Region competition: Unifying snakes, region growing, and Bayes\/MDL for multiband image segmentation","volume":"18","author":"Zhu","year":"1996","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TIP.2008.920737","article-title":"A real-time algorithm for the approximationof level-set-based curve evolution","volume":"17","author":"Shi","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/MCG.2009.61","article-title":"Non-photorealistic rendering: Unleashing the artist\u2019s imagination","volume":"29","author":"Agrawal","year":"2009","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_29","unstructured":"(2018, November 05). Institute of Artificial Intelligence and Robotics at Xi\u2019an Jiaotong University in China. Available online: http:\/\/trafficdata.xjtu.edu.cn\/index.do."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3782\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T23:45:15Z","timestamp":1775259915000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3782"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,5]]},"references-count":29,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113782"],"URL":"https:\/\/doi.org\/10.3390\/s18113782","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,5]]}}}