{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:10:11Z","timestamp":1763345411246,"version":"3.45.0"},"reference-count":53,"publisher":"Tech Science Press","issue":"1","license":[{"start":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T00:00:00Z","timestamp":1756598400000},"content-version":"vor","delay-in-days":242,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.066423","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T07:42:49Z","timestamp":1752219769000},"page":"1999-2020","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Modal Attention Networks for Driving Style-Aware Trajectory Prediction in Autonomous Driving"],"prefix":"10.32604","volume":"85","author":[{"given":"Lang","family":"Ding","sequence":"first","affiliation":[]},{"given":"Qinmu","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Jiaheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Linqing","family":"Bian","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"58443","DOI":"10.1109\/ACCESS.2020.2983149","article-title":"A survey of autonomous driving: common practices and emerging technologies","volume":"8","author":"Yurtsever","year":"2020","journal-title":"IEEE Access"},{"key":"ref2","first-page":"100733","article-title":"Machine learning for autonomous vehicle\u2019s trajectory prediction: a comprehensive survey, challenges, and future research directions","volume":"46","author":"Bharilya","year":"2024","journal-title":"Veh Commun"},{"journal-title":"Handbook of human factors for automated, connected and intelligent vehicles","year":"2020","author":"Endsley","key":"ref3"},{"key":"ref4","series-title":"2023 IEEE International Conference on Robotics and Automation (ICRA); 2023 May 29\u2013Jun 2","first-page":"2980","article-title":"Wayformer: motion forecasting via simple & efficient attention networks","author":"Nayakanti"},{"key":"ref5","unstructured":"Ngiam J, Caine B, Vasudevan V, Zhang Z, Chiang HTL, Ling J, et al. Scene transformer: a unified architecture for predicting multiple agent trajectories. arXiv:2106.08417. 2021. doi:10.48550\/arxiv.2106.08417."},{"key":"ref6","series-title":"2022 International Conference on Robotics and Automation (ICRA); 2022 May 23\u201327","first-page":"7814","article-title":"Multipath++: efficient information fusion and trajectory aggregation for behavior prediction","author":"Varadarajan"},{"key":"ref7","unstructured":"Leon F, Gavrilescu M. A review of tracking, prediction and decision making methods for autonomous driving. arXiv:1909.07707. 2019. doi:10.48550\/arxiv.1909.07707."},{"key":"ref8","doi-asserted-by":"crossref","first-page":"101603","DOI":"10.1109\/ACCESS.2024.3431437","article-title":"Explainable artificial intelligence for autonomous driving: a comprehensive overview and field guide for future research directions","volume":"12","author":"Atakishiyev","year":"2024","journal-title":"IEEE Access"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"10362","DOI":"10.1109\/TITS.2023.3275792","article-title":"Decision-making driven by driver intelligence and environment reasoning for high-level autonomous vehicles: a survey","volume":"24","author":"Wang","year":"2023","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref10","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Kalyan KS, Rajasekharan A, Sangeetha S. AMMUS: a survey of transformer-based pretrained models in natural language processing. arXiv:2108.05542. 2021. doi:10.1016\/j.jbi.2021.103982; 34974190","DOI":"10.1016\/j.jbi.2021.103982"},{"key":"ref12","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"journal-title":"Encyclopedia of information science and technology","year":"2020 [Internet]. [cited 2025 Jun 8]","author":"Wiseman","key":"ref13"},{"key":"ref14","unstructured":"Ho J, Kalchbrenner N, Weissenborn D, Salimans T. Axial attention in multidimensional transformers. arXiv:1912.12180. 2019. doi:10.48550\/arXiv.1912.12180."},{"key":"ref15","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision; 2021 Oct 11\u201317","first-page":"6836","article-title":"ViViT: a video vision transformer","author":"Arnab"},{"key":"ref16","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence; 2024 Feb 20\u201327","first-page":"10332","article-title":"Bat: behavior-aware human-like trajectory prediction for autonomous driving","author":"Liao"},{"key":"ref17","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2019 Jun 15\u201320","first-page":"8748","article-title":"Argoverse: 3D tracking and forecasting with rich maps","author":"Chang"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TIV.2022.3167103","article-title":"A survey on trajectory-prediction methods for autonomous driving","volume":"7","author":"Huang","year":"2022","journal-title":"IEEE Trans Intell Veh"},{"key":"ref19","series-title":"2021 IEEE International Conference on Robotics and Biomimetics (ROBIO); 2021 Dec 27\u201331","first-page":"978","article-title":"A survey on deep-learning approaches for vehicle trajectory prediction in autonomous driving","author":"Liu"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput"},{"key":"ref21","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 27\u201330","first-page":"961","article-title":"Social LSTM: human trajectory prediction in crowded spaces","author":"Alahi"},{"key":"ref22","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018 Jun 18\u201323","first-page":"2255","article-title":"Social GAN: socially acceptable trajectories with generative adversarial networks","author":"Gupta"},{"key":"ref23","series-title":"2020 International Joint Conference on Neural Networks (IJCNN); 2020 Jul 19\u201324","first-page":"1","article-title":"GISNET: graph-based information sharing network for vehicle trajectory prediction","author":"Zhao"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11432-019-2761-y","article-title":"Motion trajectory prediction based on a CNN-LSTM sequential model","volume":"63","author":"Xie","year":"2020","journal-title":"Sci China Inf Sci"},{"key":"ref25","series-title":"2021 IEEE Intelligent Vehicles Symposium (IV); 2021 Jul 11\u201317","first-page":"1051","article-title":"Predicting vehicles trajectories in urban scenarios with transformer networks and augmented information","author":"Quintanar"},{"key":"ref26","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2021 Jun 19\u201325","first-page":"7577","article-title":"Multimodal motion prediction with stacked transformers","author":"Liu"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"4599","DOI":"10.1109\/TIV.2023.3279425","article-title":"A review of driving style recognition methods from short-term and long-term perspectives","volume":"8","author":"Chu","year":"2023","journal-title":"IEEE Trans Intell Veh"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1109\/TITS.2017.2706978","article-title":"Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey","volume":"19","author":"Martinez","year":"2017","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref29","series-title":"2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems; 2009 Mar 30\u2013Apr 2","first-page":"23","article-title":"Driver\u2019s style classification using jerk analysis","author":"Murphey"},{"journal-title":"Relationships between driving style and fuel consumption in highway driving","year":"2011","author":"Lee","key":"ref30"},{"key":"ref31","series-title":"IEEE International Conference on Systems, Man, and Cybernetics; 2013 Oct 13\u201316","first-page":"3710","article-title":"Quantitative driving style estimation for energy-oriented applications in road vehicles","author":"Corti","year":"2013"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1109\/JPROC.2006.888405","article-title":"Driver modeling based on driving behavior and its evaluation in driver identification","volume":"95","author":"Miyajima","year":"2007","journal-title":"Proc IEEE"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"654","DOI":"10.15837\/ijccc.2010.5.2221","article-title":"Driving style analysis using data mining techniques","volume":"5","author":"Constantinescu","year":"2010","journal-title":"Int J Comput Commun Control"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1007\/s12239-009-0071-8","article-title":"Preliminary classification of driving style with objective rank method","volume":"10","author":"Augustynowicz","year":"2009","journal-title":"Int J Automot Technol"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1080\/00207160.2013.829567","article-title":"Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles","volume":"91","author":"Guardiola","year":"2014","journal-title":"Int J Comput Math"},{"key":"ref36","series-title":"The Sixth International Conference on Adaptive and Self-Adaptive Systems and Applications; 2014 May 25\u201329","first-page":"73","article-title":"Driving style recognition for co-operative driving: A survey","author":"Bolovinou"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/TITS.2022.3217673","article-title":"A learning-based discretionary lane-change decision-making model with driving style awareness","volume":"24","author":"Zhang","year":"2022","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1109\/TIV.2023.3239903","article-title":"Safety-balanced driving-style aware trajectory planning in intersection scenarios with uncertain environment","volume":"8","author":"Wang","year":"2023","journal-title":"IEEE Trans Intell Vehicles"},{"key":"ref39","doi-asserted-by":"crossref","unstructured":"Huang R, Xue H, Pagnucco M, Salim F, Song Y. Multimodal trajectory prediction: a survey. arXiv:2302.10463. 2023. doi:10.1109\/TNNLS.2025.3550350.","DOI":"10.1109\/TNNLS.2025.3550350"},{"key":"ref40","article-title":"Social-BiGAT: multimodal trajectory forecasting using bicycle-GAN and graph attention networks","volume":"32","author":"Kosaraju","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref41","doi-asserted-by":"crossref","unstructured":"Chen G, Li J, Zhou N, Ren L, Lu J. Personalized trajectory prediction via distribution discrimination. arXiv:2107.14204. 2022. doi:10.48550\/arXiv.2107.14204.","DOI":"10.1109\/ICCV48922.2021.01529"},{"key":"ref42","doi-asserted-by":"crossref","unstructured":"Halawa M, Hellwich O, Bideau P. Action-based contrastive learning for trajectory prediction. arXiv:2207.08664. 2022. doi:10.1007\/978-3-031-19842-7_9.","DOI":"10.1007\/978-3-031-19842-7_9"},{"key":"ref43","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition; 2022 Jun 19\u201324","first-page":"8823","article-title":"HiVT: hierarchical vector transformer for multi-agent motion prediction","author":"Zhou"},{"article-title":"FiLM: visual reasoning with a general conditioning layer","series-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence; 2018 Feb 2\u20137","author":"Perez","key":"ref44"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s40815-023-01616-9","article-title":"Driving style recognition using interval type-2 fuzzy inference system and multiple experts decision-making","volume":"26","author":"Pach\u00eaco Gomes","year":"2024","journal-title":"Int J Fuzzy Syst"},{"key":"ref46","doi-asserted-by":"crossref","first-page":"110854","DOI":"10.1016\/j.ymssp.2023.110854","article-title":"Lane change trajectory prediction considering driving style uncertainty for autonomous vehicles","volume":"206","author":"Chen","year":"2024","journal-title":"Mech Syst Signal Process"},{"key":"ref47","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TVT.2019.2960110","article-title":"Personalized vehicle trajectory prediction based on joint time-series modeling for connected vehicles","volume":"69","author":"Xing","year":"2019","journal-title":"IEEE Trans Vehicular Technol"},{"key":"ref48","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision; 2023 Oct 2\u20136; Paris, France. Piscataway, NJ, USA","first-page":"8295","article-title":"Adapt: efficient multi-agent trajectory prediction with adaptation","author":"Aydemir"},{"key":"ref49","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref50","series-title":"Computer Vision-ECCV 2020: 16th European Conference; 2020 Aug 23\u201328; Glasgow, UK. Cham, Switzerland","first-page":"541","article-title":"Learning lane graph representations for motion forecasting","author":"Liang"},{"key":"ref51","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision; 2021 Oct 11\u201317; Montreal, QC, Canada. Piscataway, NJ, USA","first-page":"15303","article-title":"Densetnt: end-to-end trajectory prediction from dense goal sets","author":"Gu"},{"key":"ref52","series-title":"2023 IEEE International Conference on Robotics and Automation (ICRA); 2023 May 29\u2013Jun 2; London, UK. Piscataway, NJ, USA","first-page":"1609","article-title":"GANet: goal area network for motion forecasting","author":"Wang"},{"key":"ref53","doi-asserted-by":"crossref","first-page":"12285","DOI":"10.1109\/TITS.2024.3381631","article-title":"FFINet: future feedback interaction network for motion forecasting","volume":"25","author":"Kang","year":"2024","journal-title":"IEEE Trans Intell Transp Syst"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-85-1\/TSP_CMC_66423\/TSP_CMC_66423.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:06:07Z","timestamp":1763345167000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v85n1\/63537"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.066423","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2025-04-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-09","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-29","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}