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Syst."],"published-print":{"date-parts":[[2022,3,31]]},"abstract":"<jats:p>\n            In recent years, vehicular networks have become increasingly large, heterogeneous, and dynamic, making it difficult to meet strict requirements of ultralow latency, high reliability, high security, and massive connections for next generation (6G) networks. Recently,\n            <jats:bold>deep learning (DL<\/jats:bold>\n            ) has emerged as a powerful\n            <jats:bold>artificial intelligence (AI<\/jats:bold>\n            ) technique to optimize the efficiency and adaptability of vehicle and wireless communication. However, rapidly increasing absolute numbers of vehicles on the roads are leading to increased automobile accidents, many of which are attributable to drivers interacting with their mobile phones. To address potentially dangerous driver behavior, this study applies deep learning approaches to image recognition to develop an AI-based detection system that can detect potentially dangerous driving behavior. Multiple\n            <jats:bold>convolutional neural network (CNN<\/jats:bold>\n            )-based techniques including VGG16, VGG19, Densenet, and Openpose were compared in terms of their ability to detect and identify problematic driving.\n          <\/jats:p>","DOI":"10.1145\/3466691","type":"journal-article","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T18:37:36Z","timestamp":1633459056000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["AI-Based Vehicular Network toward 6G and IoT: Deep Learning Approaches"],"prefix":"10.1145","volume":"13","author":[{"given":"Mu-Yen","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Engineering Science, National Cheng Kung University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min-Hsuan","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Information Management, National Taichung University of Science and Technology, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li-Xiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Information Management, National Taichung University of Science and Technology, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,5]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"K. B. Letaief W. Chen Y. Shi J. Zhang and Y. J. A. Zhang. 2019. The roadmap to 6G-AI empowered wireless networks. 2019 arXiv:1904.11686 .","DOI":"10.1109\/MCOM.2019.1900271"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/CC.2014.6969789"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2888904"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2954595"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/MVT.2019.2921162"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2018.02.021"},{"key":"e_1_3_1_8_2","first-page":"92301","article-title":"Border control morphing attack detection with a convolutional neural network de-morphing approach","volume":"8","author":"Ortega-Delcampo D.","year":"2020","unstructured":"D. Ortega-Delcampo, C. Conde, D. Palacios-Alonso, and E. Cabello. 2020. Border control morphing attack detection with a convolutional neural network de-morphing approach. IEEE Access 8, (2020), 92301\u201392313.","journal-title":"IEEE Access"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/e22111296"},{"key":"e_1_3_1_10_2","unstructured":"Spruyt. 2016. Driving behavior modeling using smart phone sensor data. [Online]. Retreived on 02 Aug 2021 from https:\/\/www.sentiance.com\/2016\/02\/11\/driving-behavior\/."},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113240"},{"key":"e_1_3_1_12_2","article-title":"Driver profiling using long short term memory (LSTM) and convolutional neural network (CNN) methods","author":"Cura A.","year":"2020","unstructured":"A. Cura, H. K\u00fc\u00e7\u00fck, E. Ergen, and \u0130. B. \u00d6ks\u00fczo\u011flu. 2020. Driver profiling using long short term memory (LSTM) and convolutional neural network (CNN) methods. 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IEEE Open Journal of Vehicular Technology 1, (2020), 957\u2013975.","journal-title":"IEEE Open Journal of Vehicular Technology"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2981745"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2020.2982151"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00344251"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.03.038"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.04.255"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2019.102688"},{"key":"e_1_3_1_26_2","doi-asserted-by":"crossref","unstructured":"Z. Cao G. Hidalgo T. Simon S. E. Wei and Y. Sheikh. 2018. OpenPose: Realtime multi-person 2D pose estimation using part affinity fields. arXiv preprint arXiv:1812.08008.","DOI":"10.1109\/CVPR.2017.143"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.123205"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2019.04.007"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.08.009"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.07.112"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2018.12.014"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"},{"key":"e_1_3_1_34_2","unstructured":"K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 2014."},{"key":"e_1_3_1_35_2","first-page":"4700","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","volume":"8","author":"Huang G.","unstructured":"G. Huang, Z. Liu, L. V. Der Maaten, K. Q. Weinberger. Densely connected convolutional networks. 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