{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:54:28Z","timestamp":1774540468613,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T00:00:00Z","timestamp":1652140800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T00:00:00Z","timestamp":1652140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Intel Serv Robotics"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s11370-022-00422-w","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T04:05:36Z","timestamp":1652155536000},"page":"307-320","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Multi-scale graph-transformer network for trajectory prediction of the autonomous vehicles"],"prefix":"10.1007","volume":"15","author":[{"given":"Divya","family":"Singh","sequence":"first","affiliation":[]},{"given":"Rajeev","family":"Srivastava","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"issue":"3","key":"422_CR1","doi-asserted-by":"publisher","first-page":"4882","DOI":"10.1109\/LRA.2020.3004794","volume":"5","author":"R Chandra","year":"2020","unstructured":"Chandra R et al (2020) Forecasting trajectory and behavior of road-agents using spectral clustering in graph-LSTMs. IEEE Robot Autom Lett 5(3):4882\u20134890","journal-title":"IEEE Robot Autom Lett"},{"key":"422_CR2","doi-asserted-by":"crossref","unstructured":"Xiong W, Wu L, Alleva F, Droppo J, Huang X, Stolcke A (2018) The microsoft 2017 conversational speech recognition system. In: ICASSP, International conference on acoustics, speech, and signal processing, proceedings vol 2018-April, no. August, pp 5934\u20135938","DOI":"10.1109\/ICASSP.2018.8461870"},{"key":"422_CR3","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL HLT 2019\u20142019 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol.\u2014Proc. Conf., vol 1, no Mlm, pp 4171\u20134186"},{"key":"422_CR4","doi-asserted-by":"crossref","unstructured":"Fragkiadaki K, Levine S, Felsen P, Malik J (2015) Recurrent network models for human dynamics. In: Proceedings of the IEEE international conference on computer vision, vol 2015 Inter, pp 4346\u20134354","DOI":"10.1109\/ICCV.2015.494"},{"key":"422_CR5","first-page":"5999","volume":"2017","author":"A Vaswani","year":"2017","unstructured":"Vaswani A et al (2017) Attention is all you need. Adv Neural Inf Process Syst 2017:5999\u20136009","journal-title":"Adv Neural Inf Process Syst"},{"key":"422_CR6","doi-asserted-by":"crossref","unstructured":"Luong MT, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In: Conf. Proc. - EMNLP 2015 Conf. Empir. Methods Nat. Lang. Process., pp 1412\u20131421","DOI":"10.18653\/v1\/D15-1166"},{"key":"422_CR7","doi-asserted-by":"crossref","unstructured":"Plastiras G, Kyrkou C, Theocharides T (2018) Efficient convnet-based object detection for unmanned aerial vehicles by selective tile processing. In: ACM international conference proceeding series","DOI":"10.1145\/3243394.3243692"},{"issue":"1","key":"422_CR8","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1023\/A:1008162616689","volume":"38","author":"C Papageorgiou","year":"2000","unstructured":"Papageorgiou C, Poggio T (2000) Trainable system for object detection. Int J Comput Vis 38(1):15\u201333","journal-title":"Int J Comput Vis"},{"key":"422_CR9","doi-asserted-by":"crossref","unstructured":"Lin J, Koch L, Kurowski M, Gehrt JJ, Abel D, Zweigel R (2020) Environment perception and object tracking for autonomous vehicles in a harbor scenario. In: 2020 IEEE 23rd Int. Conf. Intell. Transp. Syst. ITSC 2020, no. 4","DOI":"10.1109\/ITSC45102.2020.9294618"},{"issue":"4","key":"422_CR10","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1109\/TIV.2019.2938110","volume":"4","author":"A Rangesh","year":"2019","unstructured":"Rangesh A, Trivedi MM (2019) No blind spots: full-surround multi-object tracking for autonomous vehicles using cameras and LiDARs. IEEE Trans Intell Veh 4(4):588\u2013599","journal-title":"IEEE Trans Intell Veh"},{"key":"422_CR11","doi-asserted-by":"crossref","unstructured":"Zhang P, Ouyang W, Zhang P, Xue J, Zheng N (2019) SR-LSTM: State refinement for lstm towards pedestrian trajectory prediction. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, vol 2019-June, pp 12077\u201312086","DOI":"10.1109\/CVPR.2019.01236"},{"key":"422_CR12","doi-asserted-by":"crossref","unstructured":"Ivanovic B, Pavone M (2019) The trajectron: probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs. In: Proceedings of the IEEE international conference on computer vision, vol 2019-Octob, pp 2375\u20132384","DOI":"10.1109\/ICCV.2019.00246"},{"key":"422_CR13","doi-asserted-by":"crossref","unstructured":"Fisac JF, Bronstein E, Stefansson E, Sadigh D, Sastry SS, Dragan AD (2019) Hierarchical game-theoretic planning for autonomous vehicles. In: Proceedings of international conference on robotics and automation, vol 2019-May, pp 9590\u20139596","DOI":"10.1109\/ICRA.2019.8794007"},{"key":"422_CR14","first-page":"174","volume":"5","author":"C Liu","year":"2017","unstructured":"Liu C, Lee S, Varnhagen S, Tseng HE (2017) Path planning for autonomous vehicles using model predictive control. IEEE Intell Veh Symp Proc 5:174\u2013179","journal-title":"IEEE Intell Veh Symp Proc"},{"key":"422_CR15","doi-asserted-by":"publisher","first-page":"8355","DOI":"10.1109\/TIP.2020.3014952","volume":"29","author":"M Wu","year":"2020","unstructured":"Wu M et al (2020) Visual tracking with multiview trajectory prediction. IEEE Trans Image Process 29:8355\u20138367","journal-title":"IEEE Trans Image Process"},{"key":"422_CR16","doi-asserted-by":"crossref","unstructured":"Huynh M, Alaghband G (2019) Trajectory prediction by coupling scene-LSTM with human movement LSTM. In: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol 11844 LNCS, pp 244\u2013259","DOI":"10.1007\/978-3-030-33720-9_19"},{"issue":"1","key":"422_CR17","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/s40998-019-00228-0","volume":"44","author":"E D\u00f6nmez","year":"2020","unstructured":"D\u00f6nmez E, Kocamaz AF (2020) Design of mobile robot control infrastructure based on decision trees and adaptive potential area methods. Iran J Sci Technol Trans Electr Eng 44(1):431\u2013448","journal-title":"Iran J Sci Technol Trans Electr Eng"},{"issue":"2","key":"422_CR18","first-page":"95","volume":"8","author":"M Dirik","year":"2020","unstructured":"Dirik M, Kocamaz AF, D\u00f6nmez E (2020) Visual servoing based control methods for nonholonomic mobile robot. J Eng Res 8(2):95\u2013113","journal-title":"J Eng Res"},{"issue":"12","key":"422_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/electronics9122023","volume":"9","author":"F Okumu\u015f","year":"2020","unstructured":"Okumu\u015f F, D\u00f6nmez E, Kocamaz AF (2020) A cloudware architecture for collaboration of multiple agvs in indoor logistics: case study in fabric manufacturing enterprises. Electron 9(12):1\u201324","journal-title":"Electron"},{"issue":"12","key":"422_CR20","doi-asserted-by":"publisher","first-page":"7127","DOI":"10.1007\/s13369-017-2917-0","volume":"43","author":"E D\u00f6nmez","year":"2018","unstructured":"D\u00f6nmez E, Kocamaz AF, Dirik M (2018) A vision-based real-time mobile robot controller design based on Gaussian function for indoor environment. Arab J Sci Eng 43(12):7127\u20137142","journal-title":"Arab J Sci Eng"},{"key":"422_CR21","doi-asserted-by":"crossref","unstructured":"Gupta A, Johnson J, Fei-Fei L, Savarese S, Alahi A (2018) Social GAN: socially acceptable trajectories with generative adversarial networks. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 2255\u20132264","DOI":"10.1109\/CVPR.2018.00240"},{"key":"422_CR22","doi-asserted-by":"crossref","unstructured":"Yu C, Ma X, Ren J, Zhao H, Yi S (2020) Spatio-temporal graph transformer networks for pedestrian trajectory prediction","DOI":"10.1007\/978-3-030-58610-2_30"},{"key":"422_CR23","unstructured":"Lee D, Gu Y, Hoang J, Marchetti-Bowick M (2019) Joint interaction and trajectory prediction for autonomous driving using graph neural networks, no. NeurIPS"},{"key":"422_CR24","doi-asserted-by":"crossref","unstructured":"Sun J, Jiang Q, Lu C (2020) Recursive social behavior graph for trajectory prediction, pp 660\u2013669","DOI":"10.1109\/CVPR42600.2020.00074"},{"key":"422_CR25","doi-asserted-by":"crossref","unstructured":"Morzy M (2007) Mining frequent trajectories of moving objects for location prediction. In: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol 4571 LNAI, pp 667\u2013680","DOI":"10.1007\/978-3-540-73499-4_50"},{"key":"422_CR26","doi-asserted-by":"crossref","unstructured":"Monreale A, Pinelli F, Trasarti R, Giannotti F (2009) WhereNext: a location predictor on trajectory pattern mining. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 637\u2013645","DOI":"10.1145\/1557019.1557091"},{"key":"422_CR27","doi-asserted-by":"crossref","unstructured":"Won JI, Kim SW, Baek JH, Lee J (2009) Trajectory clustering in road network environment. In: 2009 IEEE Symp. Comput. Intell. Data Mining, CIDM 2009 - Proc., pp 299\u2013305","DOI":"10.1109\/CIDM.2009.4938663"},{"key":"422_CR28","doi-asserted-by":"crossref","unstructured":"Roh GP, Hwang SW (2010) NNCluster: an efficient clustering algorithm for road network trajectories. In: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol 5982 LNCS, no. PART 2, pp 47\u201361","DOI":"10.1007\/978-3-642-12098-5_4"},{"issue":"2","key":"422_CR29","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1109\/TMC.2013.119","volume":"14","author":"B Han","year":"2015","unstructured":"Han B, Liu L, Omiecinski E (2015) Road-network aware trajectory clustering: Integrating locality, flow, and density. IEEE Trans Mob Comput 14(2):416\u2013429","journal-title":"IEEE Trans Mob Comput"},{"key":"422_CR30","doi-asserted-by":"crossref","unstructured":"Chen M, Liu Y, Yu X (2015) Predicting next locations with object clustering and trajectory clustering. In: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol 9078, pp 344\u2013356","DOI":"10.1007\/978-3-319-18032-8_27"},{"issue":"5","key":"422_CR31","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s00779-003-0240-0","volume":"7","author":"D Ashbrook","year":"2003","unstructured":"Ashbrook D, Starner T (2003) Using GPS to learn significant locations and predict movement across multiple users. Pers Ubiquitous Comput 7(5):275\u2013286","journal-title":"Pers Ubiquitous Comput"},{"issue":"6","key":"422_CR32","doi-asserted-by":"publisher","first-page":"5204","DOI":"10.1109\/TVT.2016.2611654","volume":"66","author":"Q Lv","year":"2017","unstructured":"Lv Q, Qiao Y, Ansari N, Liu J, Yang J (2017) Individual mobility prediction at points of interest. IEEE Trans Veh Technol 66(6):5204\u20135216","journal-title":"IEEE Trans Veh Technol"},{"key":"422_CR33","unstructured":"Ishikawa Y. From Indexed Spatio-Temporal Datasets. Development, pp 9\u201316"},{"key":"422_CR34","doi-asserted-by":"crossref","unstructured":"Gambs S, Killijian MO, Del Prado Cortez MN (2012) Next place prediction using mobility Markov chains. In: Proc. 1st Work. Meas. Privacy, Mobility, MPM\u201912, pp 0\u20135","DOI":"10.1145\/2181196.2181199"},{"key":"422_CR35","doi-asserted-by":"crossref","unstructured":"Chandra R, Bhattacharya U, Bera A, Di Manocha R (2019) Traphic: trajectory prediction in dense and heterogeneous traffic using weighted interactions. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, vol 2019, pp 8475\u20138484","DOI":"10.1109\/CVPR.2019.00868"},{"key":"422_CR36","doi-asserted-by":"crossref","unstructured":"Carrasco S, Llorca DF, Sotelo M\u00c1 (2021) SCOUT: Socially-COnsistent and UndersTandable graph attention network for trajectory prediction of vehicles and VRUs","DOI":"10.1109\/IV48863.2021.9575874"},{"key":"422_CR37","doi-asserted-by":"crossref","unstructured":"Gao J et al (2020) VectorNet: encoding HD maps and agent dynamics from vectorized representation. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 11522\u201311530","DOI":"10.1109\/CVPR42600.2020.01154"},{"key":"422_CR38","doi-asserted-by":"crossref","unstructured":"Kim B et al (2021) LaPred: lane-aware prediction of multi-modal future trajectories of dynamic agents","DOI":"10.1109\/CVPR46437.2021.01440"},{"key":"422_CR39","unstructured":"Park SH et al (2020) Diverse and admissible trajectory forecasting through multimodal context understanding, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol 12356 LNCS, pp 282\u2013298"},{"key":"422_CR40","doi-asserted-by":"crossref","unstructured":"Fang L, Jiang Q, Shi J, Zhou B (2020) TPNet: trajectory proposal network for motion prediction. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 6796\u20136805","DOI":"10.1109\/CVPR42600.2020.00683"},{"key":"422_CR41","first-page":"2370","volume":"56","author":"C Luo","year":"2020","unstructured":"Luo C, Sun L, Dabiri D, Yuille A (2020) Probabilistic multi-modal trajectory prediction with lane attention for autonomous vehicles. IEEE Int Conf Intell Robot Syst 56:2370\u20132376","journal-title":"IEEE Int Conf Intell Robot Syst"},{"key":"422_CR42","first-page":"5962","volume":"56","author":"H He","year":"2020","unstructured":"He H, Dai H, Wang N (2020) UST: unifying spatio-temporal context for trajectory prediction in autonomous driving. IEEE Int Conf Intell Robot Syst 56:5962\u20135969","journal-title":"IEEE Int Conf Intell Robot Syst"},{"key":"422_CR43","first-page":"4095","volume-title":"\u201cF-NET: fusion neural network for vehicle trajectory prediction in autonomous driving","author":"J Wang","year":"2021","unstructured":"Wang J et al (2021) \u201cF-NET: fusion neural network for vehicle trajectory prediction in autonomous driving, vol 1. Peking University, Beijing, pp 4095\u20134099"},{"key":"422_CR44","unstructured":"Becker S, Hug R, H\u00fcbner W, Arens M (2018) An evaluation of trajectory prediction approaches and notes on the TrajNet Benchmark"},{"key":"422_CR45","doi-asserted-by":"crossref","unstructured":"Becker S, Hug R, H\u00fcbner W, Arens M (2019) RED: a simple but effective baseline predictor for the TrajNet benchmark. In: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol 11131 LNCS, pp 138\u2013153","DOI":"10.1007\/978-3-030-11015-4_13"},{"issue":"2","key":"422_CR46","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1109\/LRA.2020.2969925","volume":"5","author":"C Scholler","year":"2020","unstructured":"Scholler C, Aravantinos V, Lay F, Knoll A (2020) What the constant velocity model can teach us about pedestrian motion prediction. IEEE Robot Autom Lett 5(2):1696\u20131703","journal-title":"IEEE Robot Autom Lett"},{"issue":"2","key":"422_CR47","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","volume":"30","author":"DK Hammond","year":"2011","unstructured":"Hammond DK, Vandergheynst P, Gribonval R (2011) Wavelets on graphs via spectral graph theory. Appl Comput Harmon Anal 30(2):129\u2013150","journal-title":"Appl Comput Harmon Anal"},{"key":"422_CR48","unstructured":"Badue C et al (2017) 1901.04407-1"},{"key":"422_CR49","unstructured":"Ma Y, Zhu X, Zhang S, Yang R, Wang W, Manocha D (2009) TrafficPredict: trajectory prediction for heterogeneous traffic-agents, no. Kalman 1960"},{"key":"422_CR50","unstructured":"Chen L (2020) One thousand and one hours: self-driving motion prediction dataset, no. CoRL 2020, pp 1\u201310"},{"key":"422_CR51","unstructured":"Chang M et al. Argoverse\u202f: 3D tracking and forecasting with rich maps"},{"key":"422_CR52","doi-asserted-by":"crossref","unstructured":"Li X, Ying X, Chuah MC (2019) GRIP++: enhanced graph-based interaction-aware trajectory prediction for autonomous driving","DOI":"10.1109\/ITSC.2019.8917228"},{"key":"422_CR53","doi-asserted-by":"crossref","unstructured":"Li X, Ying X, Chuah MC (2019) GRIP: graph-based interaction-aware trajectory prediction, pp 3960\u20133966","DOI":"10.1109\/ITSC.2019.8917228"},{"key":"422_CR54","doi-asserted-by":"crossref","unstructured":"Julka S, Sowrirajan V, Schloetterer J, Granitzer M (2021) Conditional generative adversarial networks for speed control in trajectory simulation","DOI":"10.1007\/978-3-030-95470-3_33"},{"key":"422_CR55","first-page":"1","volume":"56","author":"G Kim","year":"2021","unstructured":"Kim G, Kim D, Ahn Y, Huh K (2021) Hybrid approach for vehicle trajectory prediction using weighted integration of multiple models. IEEE Access 56:1","journal-title":"IEEE Access"}],"container-title":["Intelligent Service Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11370-022-00422-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11370-022-00422-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11370-022-00422-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T08:34:41Z","timestamp":1657701281000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11370-022-00422-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,10]]},"references-count":55,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["422"],"URL":"https:\/\/doi.org\/10.1007\/s11370-022-00422-w","relation":{},"ISSN":["1861-2776","1861-2784"],"issn-type":[{"value":"1861-2776","type":"print"},{"value":"1861-2784","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,10]]},"assertion":[{"value":"7 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}