{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T11:01:38Z","timestamp":1781175698273,"version":"3.54.1"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.asoc.2026.115701","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T16:02:25Z","timestamp":1780934545000},"page":"115701","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["Ego-centric congestion understanding from multi-camera traffic scenes: A benchmark dataset and graph reasoning approach for autonomous vehicles"],"prefix":"10.1016","volume":"201","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3282-0990","authenticated-orcid":false,"given":"Soumya","family":"Abbu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4250-4429","authenticated-orcid":false,"given":"Naveen Kumar","family":"Kummari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1645-8502","authenticated-orcid":false,"given":"Sobhan","family":"Babu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1023-2118","authenticated-orcid":false,"given":"Linga Reddy","family":"Cenkeramaddi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishna Mohan","family":"Chalavadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.asoc.2026.115701_bib0005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42421-023-00086-7","article-title":"Deep learning-based computer vision methods for complex traffic environments perception: a review","volume":"6","author":"Azfar","year":"2024","journal-title":"Data Science for Transportation"},{"issue":"2","key":"10.1016\/j.asoc.2026.115701_bib0010","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s00607-020-00869-8","article-title":"An improved YOLO-based road traffic monitoring system","volume":"103","author":"Al-Qaness","year":"2021","journal-title":"Computing"},{"key":"10.1016\/j.asoc.2026.115701_bib0015","series-title":"IJCAI","first-page":"3571","article-title":"Real-time traffic pattern analysis and inference with sparse video surveillance information","author":"Wang","year":"2018"},{"issue":"21","key":"10.1016\/j.asoc.2026.115701_bib0020","doi-asserted-by":"crossref","first-page":"9177","DOI":"10.3390\/su12219177","article-title":"Artificial intelligence-enabled traffic monitoring system","volume":"12","author":"Mandal","year":"2020","journal-title":"Sustainability"},{"key":"10.1016\/j.asoc.2026.115701_bib0025","article-title":"Visual and inertial sensors for robust autonomous vehicle navigation in urban environments","volume":"12","author":"Abdelaziz","year":"2022","journal-title":"AI, IoT and the Fourth Industrial Revolution Review"},{"issue":"1","key":"10.1016\/j.asoc.2026.115701_bib0030","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1186\/s40537-023-00701-y","article-title":"Exploration of issues, challenges and latest developments in autonomous cars","volume":"10","author":"Padmaja","year":"2023","journal-title":"J. Big Data"},{"issue":"2","key":"10.1016\/j.asoc.2026.115701_bib0035","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1109\/TITS.2019.2958352","article-title":"Research advances and challenges of autonomous and connected ground vehicles","volume":"22","author":"Eskandarian","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"4","key":"10.1016\/j.asoc.2026.115701_bib0040","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2020.2973615","article-title":"LiDAR for autonomous driving: the principles, challenges, and trends for automotive LiDAR and perception systems","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.asoc.2026.115701_bib0045","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2023.3312382","article-title":"Radars for autonomous driving: a review of deep learning methods and challenges","volume":"11","author":"Srivastav","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115701_bib0050","author":"Rezaei"},{"key":"10.1016\/j.asoc.2026.115701_bib0055","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"779","article-title":"You only look once: unified, real-time object detection","author":"Redmon","year":"2016"},{"issue":"6","key":"10.1016\/j.asoc.2026.115701_bib0060","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115701_bib0065","series-title":"2017 IEEE International Conference on Image Processing (ICIP)","first-page":"3645","article-title":"Simple online and realtime tracking with a deep association metric","author":"Wojke","year":"2017"},{"key":"10.1016\/j.asoc.2026.115701_bib0070","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"7942","article-title":"Mots: multi-object tracking and segmentation","author":"Voigtlaender","year":"2019"},{"key":"10.1016\/j.asoc.2026.115701_bib0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112015","article-title":"Enhancing vehicle detection in intelligent transportation systems via autonomous UAV platform and YOLOv8 integration","volume":"164","author":"Bakirci","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115701_bib0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.113463","article-title":"Multiagent reinforcement learning with evolution for multitarget tracking by unmanned aerial vehicle swarm","author":"Jiao","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115701_bib0085","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"2569","article-title":"Multi-camera multiple 3D object tracking on the move for autonomous vehicles","author":"Nguyen","year":"2022"},{"key":"10.1016\/j.asoc.2026.115701_bib0090","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops","first-page":"3265","article-title":"Multi-camera vehicle tracking system for AI city challenge 2022","author":"Li","year":"2022"},{"key":"10.1016\/j.asoc.2026.115701_bib0095","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9212","article-title":"Vmc: video motion customization using temporal attention adaption for text-to-video diffusion models","author":"Jeong","year":"2024"},{"key":"10.1016\/j.asoc.2026.115701_bib0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.artint.2020.103448","article-title":"Multiple object tracking: a literature review","volume":"293","author":"Luo","year":"2021","journal-title":"Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115701_bib0105","series-title":"Computer Vision \u2013 ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part IV","article-title":"Tracking objects as points","author":"Zhou","year":"2020"},{"key":"10.1016\/j.asoc.2026.115701_bib0110","series-title":"Computer Vision and Pattern Recognition","article-title":"LMGP: lifted multicut meets geometry projections for multi-camera multi-object tracking","author":"Nguyen","year":"2022"},{"key":"10.1016\/j.asoc.2026.115701_bib0115","first-page":"42","article-title":"Comparative evaluation of SORT, DeepSORT, and ByteTrack for multiple object tracking in highway videos","volume":"8","author":"Abouelyazid","year":"2023","journal-title":"International Journal of Sustainable Infrastructure for Cities and Societies"},{"key":"10.1016\/j.asoc.2026.115701_bib0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110457","article-title":"Multi-camera multi-object tracking on the move via single-stage global association approach","volume":"152","author":"Nguyen","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2026.115701_bib0125","article-title":"Visual object tracking: review and challenges","author":"Chen","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115701_bib0130","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.asoc.2018.05.023","article-title":"Soft computing based object detection and tracking approaches: state-of-the-art survey","volume":"70","author":"Kaushal","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115701_bib0135","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Point-GNN: graph neural network for 3D object detection in a point cloud","author":"Shi","year":"2020"},{"issue":"3","key":"10.1016\/j.asoc.2026.115701_bib0140","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1109\/TPAMI.2024.3515454","article-title":"Bevformer: learning bird\u2019s-eye-view representation from LiDAR-camera via spatiotemporal transformers","volume":"47","author":"Li","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115701_bib0145","series-title":"European Conference on Computer Vision","first-page":"541","article-title":"Learning lane graph representations for motion forecasting","author":"Liang","year":"2020"},{"key":"10.1016\/j.asoc.2026.115701_bib0150","doi-asserted-by":"crossref","first-page":"18537","DOI":"10.1109\/TITS.2024.3433591","article-title":"BEV-TP: end-to-end visual perception and trajectory prediction for autonomous driving","volume":"25","author":"Lang","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.asoc.2026.115701_bib0155","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"T2SG: traffic topology scene graph for topology reasoning in autonomous driving","author":"Lv","year":"2025"},{"issue":"1","key":"10.1016\/j.asoc.2026.115701_bib0160","article-title":"Small parallel residual convolutional neural network and traffic congestion detection","volume":"15","author":"Jiang","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2026.115701_bib0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110372","article-title":"Traffic congestion recognition based on convolutional neural networks in different scenarios","volume":"148","author":"Wang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115701_bib0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124701","article-title":"MobileNetV2 with spatial attention module for traffic congestion recognition in surveillance images","volume":"255","author":"Lin","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10.1016\/j.asoc.2026.115701_bib0175","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TASE.2020.3039908","article-title":"Flow-achieving online planning and dispatching for continuous transportation with autonomous vehicles","volume":"19","author":"Seiler","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"10.1016\/j.asoc.2026.115701_bib0180","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops","first-page":"42","article-title":"nuScenes knowledge graph \u2013 a comprehensive semantic representation of traffic scenes for trajectory prediction","author":"Mlodzian","year":"2023"},{"key":"10.1016\/j.asoc.2026.115701_bib0185","series-title":"2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)","first-page":"532","article-title":"Lanercnn: distributed representations for graph-centric motion forecasting","author":"Zeng","year":"2021"},{"issue":"7","key":"10.1016\/j.asoc.2026.115701_bib0190","doi-asserted-by":"crossref","first-page":"8370","DOI":"10.1007\/s10489-022-03961-y","article-title":"Vehicle trajectory prediction on highways using bird eye view representations and deep learning","volume":"53","author":"Izquierdo","year":"2023","journal-title":"Appl. Intell."},{"issue":"7","key":"10.1016\/j.asoc.2026.115701_bib0195","doi-asserted-by":"crossref","first-page":"9554","DOI":"10.1109\/TITS.2022.3146300","article-title":"Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network","volume":"23","author":"Mo","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.asoc.2026.115701_bib0200","series-title":"Proc. of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"17928","article-title":"Standing between past and future: spatio-temporal modeling for multi-camera 3D multi-object tracking","author":"Pang","year":"2023"},{"issue":"1","key":"10.1016\/j.asoc.2026.115701_bib0205","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The graph neural network model","volume":"20","author":"Scarselli","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.asoc.2026.115701_bib0210","series-title":"IJCAI","article-title":"Sparse multi-relational graph convolutional network for multi-type object trajectory prediction","author":"Zhang","year":"2024"},{"issue":"8","key":"10.1016\/j.asoc.2026.115701_bib0215","article-title":"SSAGCN: social soft attention graph convolution network for pedestrian trajectory prediction","volume":"14","author":"Lv","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.asoc.2026.115701_bib0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.112675","article-title":"RDGCN: reasonably dense graph convolution network for pedestrian trajectory prediction","volume":"213","author":"Sang","year":"2023","journal-title":"Measurement"},{"key":"10.1016\/j.asoc.2026.115701_bib0225","series-title":"IJCAI","article-title":"CDSTraj: characterized diffusion and spatial-temporal interaction network for trajectory prediction in autonomous driving","author":"Liao","year":"2024"},{"key":"10.1016\/j.asoc.2026.115701_bib0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2019.105916","article-title":"Classification of spatio-temporal trajectories from volunteer geographic information through fuzzy rules","volume":"86","author":"Cuenca-Jara","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115701_bib0235","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.asoc.2017.07.014","article-title":"Measuring traffic congestion: an approach based on learning weighted inequality, spread and aggregation indices from comparison data","volume":"67","author":"Beliakov","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115701_bib0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109893","article-title":"Structured prediction of sparse dependent variables for traffic state estimation in large-scale networks","volume":"133","author":"Petrovi\u0107","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115701_bib0245","article-title":"Automatic lane discovery and traffic congestion detection in a real-time multi-vehicle tracking systems","volume":"12","author":"Wang","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115701_bib0250","series-title":"Directional Statistics","author":"Mardia","year":"2009"},{"key":"10.1016\/j.asoc.2026.115701_bib0255","author":"Brody"},{"issue":"20","key":"10.1016\/j.asoc.2026.115701_bib0260","first-page":"10","article-title":"Graph attention networks","volume":"1050","author":"Velickovic","year":"2017","journal-title":"stat"},{"issue":"8","key":"10.1016\/j.asoc.2026.115701_bib0265","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Schmidhuber","year":"1997","journal-title":"Neural Comput."},{"issue":"2","key":"10.1016\/j.asoc.2026.115701_bib0270","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"10.1016\/j.asoc.2026.115701_bib0275","author":"Chung"},{"key":"10.1016\/j.asoc.2026.115701_bib0280","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.asoc.2026.115701_bib0285","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.asoc.2026.115701_bib0290","series-title":"International Conference on Learning Representations (ICLR)","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156849462601149X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156849462601149X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T10:50:38Z","timestamp":1781175038000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S156849462601149X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":58,"alternative-id":["S156849462601149X"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115701","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Ego-centric congestion understanding from multi-camera traffic scenes: A benchmark dataset and graph reasoning approach for autonomous vehicles","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115701","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115701"}}