{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:01:12Z","timestamp":1764784872277,"version":"3.37.3"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T00:00:00Z","timestamp":1653091200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T00:00:00Z","timestamp":1653091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100012554","name":"Hubei Provincial Department of Education","doi-asserted-by":"publisher","award":["B2021055"],"award-info":[{"award-number":["B2021055"]}],"id":[{"id":"10.13039\/100012554","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s00138-022-01305-x","type":"journal-article","created":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T15:07:24Z","timestamp":1653145644000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Attention-based global context network for driving maneuvers prediction"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9857-531X","authenticated-orcid":false,"given":"Jun","family":"Gao","sequence":"first","affiliation":[]},{"given":"Jiangang","family":"Yi","sequence":"additional","affiliation":[]},{"given":"Yi Lu","family":"Murphey","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,21]]},"reference":[{"issue":"1","key":"1305_CR1","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1080\/21680566.2020.1782786","volume":"10","author":"J Gao","year":"2020","unstructured":"Gao, J., Murphey, Y.L., Yi, J.G., et al.: A data-driven lane-changing behavior detection system based on sequence learning. Transportmetr. B Transp. Dyn. 10(1), 831\u2013848 (2020)","journal-title":"Transportmetr. B Transp. Dyn."},{"issue":"12","key":"1305_CR2","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.1007\/s00607-019-00712-9","volume":"101","author":"J Gao","year":"2019","unstructured":"Gao, J., Murphey, Y.L., Zhu, H.H.: Personalized detection of lane changing behavior using multisensor data fusion. Computing 101(12), 1837\u20131860 (2019)","journal-title":"Computing"},{"key":"1305_CR3","doi-asserted-by":"crossref","unstructured":"Zyner, A., Worrall, S., Ward, J., et al.: Long short term memory for driver intent prediction. In: IEEE Intelligent Vehicles Symposium, pp. 1484\u20131489. IEEE (2017)","DOI":"10.1109\/IVS.2017.7995919"},{"key":"1305_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107276","volume":"103","author":"X Peng","year":"2020","unstructured":"Peng, X., Murphey, Y.L., Liu, R., et al.: Driving maneuver early detection via sequence learning from vehicle signals and video images. Pattern Recogn. 103, 107276 (2020)","journal-title":"Pattern Recogn."},{"issue":"2","key":"1305_CR5","first-page":"121","volume":"21","author":"HT Kim","year":"2015","unstructured":"Kim, H.T., Song, B., Lee, H., et al.: Multiple vehicle recognition based on radar and vision sensor fusion for lane change assistance. J. Inst. Control 21(2), 121\u2013129 (2015)","journal-title":"J. Inst. Control"},{"key":"1305_CR6","doi-asserted-by":"crossref","unstructured":"Ohn-Bar, E., Tawari, A., Martin, S., Trivedi, M.M.: Predicting driver maneuvers by learning holistic features. In: IEEE Intelligent Vehicles Symposium Proceedings, pp. 719\u2013724 (2014)","DOI":"10.1109\/IVS.2014.6856612"},{"key":"1305_CR7","doi-asserted-by":"publisher","first-page":"3561","DOI":"10.1109\/TITS.2019.2937287","volume":"21","author":"Q Deng","year":"2019","unstructured":"Deng, Q., Wang, J., Hillebrand, K., et al.: Prediction performance of lane changing behaviors: a study of combining environmental and eye-tracking data in a driving simulator. IEEE Trans. Intell. Transp. Syst. 21, 3561\u20133570 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"1305_CR8","doi-asserted-by":"crossref","unstructured":"Deng, Q., Wang, J., Soffker, D.: Prediction of human driver behaviors based on an improved HMM approach. In: IEEE Intelligent Vehicles Symposium, pp. 2066\u20132071. IEEE (2018)","DOI":"10.1109\/IVS.2018.8500717"},{"key":"1305_CR9","doi-asserted-by":"crossref","unstructured":"Tran, D., Sheng, W., Liu, L. et al.: A hidden Markov model based driver intention prediction system. In: IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, pp. 115\u2013120 (2015)","DOI":"10.1109\/CYBER.2015.7287920"},{"key":"1305_CR10","doi-asserted-by":"crossref","unstructured":"Ma, X., Ma, Z., Zhu, X., et al.: Driver Behavior Classification under Cut-In Scenarios Using Support Vector Machine Based on Naturalistic Driving Data. SAE Technical Paper (2019)","DOI":"10.4271\/2019-01-0136"},{"key":"1305_CR11","doi-asserted-by":"crossref","unstructured":"Dou, Y., Yan, F., Feng, D.: Lane changing prediction at highway lane drops using support vector machine and artificial neural network classifiers. In: IEEE\/ASME International Conference on Advanced Intelligent Mechatronics, pp. 901\u2013906 (2016)","DOI":"10.1109\/AIM.2016.7576883"},{"key":"1305_CR12","doi-asserted-by":"crossref","unstructured":"Leonhardt, V., Wanielik, G.: Recognition of lane change intentions fusing features of driving situation, driver behavior, and vehicle movement by means of neural networks. In: Advanced Microsystems for Automotive Applications, pp. 59\u201369. Springer (2018)","DOI":"10.1007\/978-3-319-66972-4_6"},{"key":"1305_CR13","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.apergo.2015.03.017","volume":"50","author":"J Peng","year":"2015","unstructured":"Peng, J., Guo, Y., Fu, R., Yuan, W., et al.: Multi-parameter prediction of drivers\u2019 lane changing behaviour with neural network model. Appl. Ergon. 50, 207\u2013217 (2015)","journal-title":"Appl. Ergon."},{"issue":"10","key":"1305_CR14","doi-asserted-by":"publisher","first-page":"3523","DOI":"10.1007\/s10489-018-1163-9","volume":"48","author":"J Gao","year":"2018","unstructured":"Gao, J., Murphey, Y.L., Zhu, H.H.: Multivariate time series prediction of lane changing behavior using deep neural network. Appl. Intell. 48(10), 3523\u20133537 (2018)","journal-title":"Appl. Intell."},{"key":"1305_CR15","doi-asserted-by":"crossref","unstructured":"Scheel, O., Nagaraja, N.S., Schwarz, L., et al.: Attention-based lane change prediction. arXiv:1903.01246 (2019)","DOI":"10.1109\/ICRA.2019.8793648"},{"key":"1305_CR16","doi-asserted-by":"crossref","unstructured":"Murphey, Y.L., Kochhar, D.S., Xie, Y.Q.: Driver workload in an autonomous vehicle. SAE Technical Paper (2019)","DOI":"10.4271\/2019-01-0872"},{"key":"1305_CR17","doi-asserted-by":"crossref","unstructured":"Zyner, A., Worrall, S., Nebot, E.: Naturalistic driver intention and path prediction using recurrent neural networks. In: IEEE Transactions on Intelligent Transportation Systems (2019)","DOI":"10.1109\/TITS.2019.2913166"},{"key":"1305_CR18","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dong, C., Palanisamy, P., et al.: Attention-based hierarchical deep reinforcement learning for lane change behaviors in autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00172"},{"key":"1305_CR19","doi-asserted-by":"crossref","unstructured":"Lu, C., Hu, F., Cao, D., et al.: Transfer learning for driver model adaptation in lane-changing scenarios using manifold alignment. In: IEEE Transactions on Intelligent Transportation Systems (2019)","DOI":"10.1109\/TITS.2019.2925510"},{"key":"1305_CR20","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.trc.2019.07.002","volume":"106","author":"DF Xie","year":"2019","unstructured":"Xie, D.F., Fang, Z.Z., Jia, B., et al.: A data-driven lane-changing model based on deep learning. Transp. Res. Part C Emerg. Technol. 106, 41\u201360 (2019)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"1305_CR21","first-page":"2204","volume":"2014","author":"V Mnih","year":"2014","unstructured":"Mnih, V., Heess, N., Graves, A.: Recurrent models of visual attention. Adv. Neural Inf. Process. Syst. 2014, 2204\u20132212 (2014)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1305_CR22","doi-asserted-by":"crossref","unstructured":"Yang, Z., He, X., Gao, J., et al.: Stacked attention networks for image question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 21\u201329 (2016)","DOI":"10.1109\/CVPR.2016.10"},{"key":"1305_CR23","doi-asserted-by":"crossref","unstructured":"Chu, X., Yang, W., Ouyang, W., et al.: Multi-context attention for human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1831\u20131840 (2017)","DOI":"10.1109\/CVPR.2017.601"},{"key":"1305_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dana, K., Shi, J., et al.: Context encoding for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7151\u20137160 (2018)","DOI":"10.1109\/CVPR.2018.00747"},{"key":"1305_CR25","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1305_CR26","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., et al.: GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond. arXiv:1904.11492 (2019)","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"1305_CR27","doi-asserted-by":"publisher","first-page":"11781","DOI":"10.1109\/JSEN.2020.3003121","volume":"21","author":"Z Huang","year":"2020","unstructured":"Huang, Z., Lv, C., Xing, Y., et al.: Multi-modal sensor fusion-based deep neural network for end-to-end autonomous driving with scene understanding. IEEE Sens. J. 21, 11781\u201311790 (2020)","journal-title":"IEEE Sens. J."},{"key":"1305_CR28","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., et al.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"1305_CR29","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., et al.: Sca-cnn: spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5659\u20135667 (2017)","DOI":"10.1109\/CVPR.2017.667"},{"key":"1305_CR30","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1305_CR31","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.trc.2019.05.021","volume":"104","author":"X Zhang","year":"2019","unstructured":"Zhang, X., Sun, J., Qi, X., et al.: Simultaneous modeling of car-following and lane-changing behaviors using deep learning. Transp. Res. Part C Emerg. Technol. 104, 287\u2013304 (2019)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"1305_CR32","doi-asserted-by":"crossref","unstructured":"Ramanishka, V., Chen, Y., Misu, T., Saenko, K.: Toward driving scene understanding: a dataset for learning driver behavior and causal reasoning. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7699\u20137707 (2018)","DOI":"10.1109\/CVPR.2018.00803"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-022-01305-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-022-01305-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-022-01305-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T18:16:12Z","timestamp":1658772972000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-022-01305-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,21]]},"references-count":32,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["1305"],"URL":"https:\/\/doi.org\/10.1007\/s00138-022-01305-x","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"type":"print","value":"0932-8092"},{"type":"electronic","value":"1432-1769"}],"subject":[],"published":{"date-parts":[[2022,5,21]]},"assertion":[{"value":"22 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"53"}}