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Bouhsissin, N. Sael, and F. Benabbou, \u201cDriver Behavior Classification: A Systematic Literature Review,\u201d IEEE Access, vol.11, pp.14128-14152, Feb. 2023. doi: 10.1109\/ACCESS.2023.3243865 10.1109\/access.2023.3243865","DOI":"10.1109\/ACCESS.2023.3243865"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] E. Michelaraki, C. Katrakazas, S. Kaiser, T. Brijs, and G. Yannis, \u201cReal-time monitoring of driver distraction: State-of-the-art and future insights,\u201d Accident Analysis and Prevention, vol.192, p.107241, Nov. 2023, doi: 10.1016\/j.aap.2023.107241 10.1016\/j.aap.2023.107241","DOI":"10.1016\/j.aap.2023.107241"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] C. Yan, F. Coenen, and B. Zhang, \u201cDriving Posture Recognition by Convolutional Neural Networks,\u201d IET Computer Vision, vol.10, no.2, pp.103-114, March 2016. doi: 10.1049\/iet-cvi.2015.0175 10.1049\/iet-cvi.2015.0175","DOI":"10.1049\/iet-cvi.2015.0175"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] B. Baheti, S. Talbar, and S. Gajre, \u201cTowards Computationally Efficient and Realtime Distracted Driver Detection With MobileVGG Network,\u201d IEEE Transactions on Intelligent Vehicles, vol.5, no.4, pp.565-574. May 2020. doi: 10.1109\/TIV.2020.2995555 10.1109\/tiv.2020.2995555","DOI":"10.1109\/TIV.2020.2995555"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] W. Wang, X. Lu, P. Zhang, H. Xie, and W. Zeng, \u201cDriver Action Recognition Based on Attention Mechanism,\u201d In Proceedings of the 2019 6th International Conference on Systems and Informatics (ICSAI 2019), pp.1255-1259, Nov. 2019. doi:10.1109\/ICSAI48974.2019.9010589 10.1109\/icsai48974.2019.9010589","DOI":"10.1109\/ICSAI48974.2019.9010589"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] I. Jegham, A.B. Khalifa, I. Alouani, and M.A. Mahjoub, \u201cSoft Spatial Attention-Based Multimodal Driver Action Recognition Using Deep Learning,\u201d IEEE Sensors Journal, vol.21, no.2, pp.1918-1925, Jan. 2021. doi: 10.1109\/JSEN.2020.3019258 10.1109\/jsen.2020.3019258","DOI":"10.1109\/JSEN.2020.3019258"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] T. Huang and R. Fu, \u201cDriver Distraction Detection Based on the True Driver\u2019s Focus of Attention,\u201d IEEE Transactions on Intelligent Transportation Systems, vol.23, no.10, pp.19374-19386, Oct. 2022. doi: 10.1109\/TITS.2022.3166208 10.1109\/tits.2022.3166208","DOI":"10.1109\/TITS.2022.3166208"},{"key":"10","unstructured":"[10] Y. Abouelnaga, H.M. Eraqi, and M.N. Moustafa, \u201cReal-time distracted driver posture classification,\u201d arXiv: 1706.09498, 2017."},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] H.M. Eraqi, Y. Abouelnaga, M.H. Saad, and M.N. Moustafa, \u201cDriver Distraction Identification with an Ensemble of Convolutional Neural Networks,\u201d Journal of Advanced Transportation, Article ID 4125865, vol.2019, no.1, pp.1-12, Feb. 2019. doi: 10.1155\/2019\/4125865 10.1155\/2019\/4125865","DOI":"10.1155\/2019\/4125865"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] F. Omerustaoglu, C.O. Sakar, and G. Kar, \u201cDistracted driver detection by combining in-vehicle and image data using deep learning,\u201d Applied Soft Computing Journal, vol.96, p.106657, Nov. 2020. doi: 10.1016\/j.asoc.2020.106657 10.1016\/j.asoc.2020.106657","DOI":"10.1016\/j.asoc.2020.106657"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] M. Martin, J. Popp, M. Anneken, M. Voit, and R. Stiefelhagen, \u201cBody Pose and Context Information for Driver Secondary Task Detection,\u201d 2018 IEEE Intelligent Vehicles Symposium (IV), pp.2015-2021, Oct. 2018. doi: 10.1109\/IVS.2018.8500523 10.1109\/ivs.2018.8500523","DOI":"10.1109\/IVS.2018.8500523"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] P. Gebert, A. Roitberg, M. Haurilet, and R. Stiefelhagen, \u201cEnd-to-end Prediction of Driver Intention using 3D Convolutional Neural Networks,\u201d 2019 IEEE Intelligent Vehicles Symposium (IV), pp.969-974, Aug. 2019. doi: 10.1109\/IVS.2019.8814249 10.1109\/ivs.2019.8814249","DOI":"10.1109\/IVS.2019.8814249"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] J.M. Mase, P. Chapman, G.P. Figueredo, and M.T. Torres, \u201cA Hybrid Deep Learning Approach for Driver Distraction Detection,\u201d 2020 IEEE International Conference on Technologies for Homeland Security (HST), Sept. 2020. doi: 10.1109\/ICTC49870.2020.9289588 10.1109\/ictc49870.2020.9289588","DOI":"10.1109\/ICTC49870.2020.9289588"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] M. Leekhaa, M. Goswami, R.R. Shah, Y. Yin, and R. Zimmermann, \u201cAre You Paying Attention? 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Dong, \u201cDistracted Driver Detection Based on a CNN With Decreasing Filter Size,\u201d IEEE Transactions on Intelligent Transportation Systems, vol.23, no.7, pp.6922-6933, March 2021. doi: 10.1109\/TITS.2021.3063521 10.1109\/tits.2021.3063521","DOI":"10.1109\/TITS.2021.3063521"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] J. Liang, H. Zhu, E. Zhang, and J. Zhang, \u201cStargazer: A Transformer-based Driver Action Detection System for Intelligent Transportation,\u201d 2022. arXiv preprint arXiv:2204.08096 10.1109\/cvprw56347.2022.00356","DOI":"10.1109\/CVPRW56347.2022.00356"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] A.A.Q. Mohammed, X. Geng, J. Wang, and Z. Ali, \u201cDriver distraction detection using semi-supervised lightweight vision transformer,\u201d Engineering Applications of Artificial Intelligence, vol.129, p.107618, March 2024. doi: 10.1016\/j.engappai.2023.107618 10.1016\/j.engappai.2023.107618","DOI":"10.1016\/j.engappai.2023.107618"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] L. Mou, J. Chang, C. Zhou, Y. Zhao, N. Ma, B. Yin, R. Jain, and W. Gao, \u201cMultimodal driver distraction detection using dual-channel network of CNN and Transformer,\u201d Expert Systems With Applications, vol.234, p.121066, Dec. 2023. doi: 10.1016\/j.eswa.2023.121066 10.1016\/j.eswa.2023.121066","DOI":"10.1016\/j.eswa.2023.121066"},{"key":"22","unstructured":"[22] S. Kaggle, State Farm Distracted Driver Detection, https:\/\/www.kaggle.com\/c\/state-farm-distracteddriver-detection, 2016."},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] J.D. Ortega, N. Kose, P. Ca\u00f1as, M.A. Chao, A. Unnervik, M. Nieto, and L. Salgado, \u201cDMD: A large-scale multi-modal driver monitoring dataset for attention and alertness analysis,\u201d in Proc. Eur. Conf. Comput. Vis. Cham, Switzerland: Springer, pp.387-405, Aug. 2020. doi: 10.48550\/arXiv.2008.12085 10.1007\/978-3-030-66823-5_23","DOI":"10.1007\/978-3-030-66823-5_23"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] A. Ezzouhri, Z. Charouh, M. Ghogho, and Z. Guennoun, \u201cRobust Deep Learning-Based Driver Distraction Detection and Classification,\u201d IEEE Access, vol.9, pp.168080-168092, Dec. 2023. doi: 10.1109\/ACCESS.2021.3133797 10.1109\/access.2021.3133797","DOI":"10.1109\/ACCESS.2021.3133797"},{"key":"25","unstructured":"[25] A. Wang, H. Chen, L. Liu, K. Chen, Z. Lin, J. Han, and G. Ding, \u201cYOLOv10: Real-time end-to-end object detection,\u201d arXiv:2405.14458, 2024."},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] T.-Y. Lin, P. Doll\u00e1r, R. Girshick, K. He, B. Hariharan, and S. Belongie, \u201cFeature Pyramid Networks for Object Detection,\u201d 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp.936-944, Nov. 2017. doi: 10.1109\/CVPR.2017.106. 10.1109\/cvpr.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, \u201cPath aggregation network for instance segmentation,\u201d In: Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, pp.8759-8768, March 2018. 10.1109\/cvpr.2018.00913","DOI":"10.1109\/CVPR.2018.00913"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] M. Tan, R. Pang, and Q.V. 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