{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T06:21:21Z","timestamp":1775110881546,"version":"3.50.1"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62073295 and 62072409"],"award-info":[{"award-number":["62073295 and 62072409"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004731","name":"Zhejiang Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["LR21F020003"],"award-info":[{"award-number":["LR21F020003"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"crossref"}]},{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R&D Program of Zhejiang","award":["2022C01050"],"award-info":[{"award-number":["2022C01050"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Spatial Algorithms Syst."],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>As a significant part of traffic accident prevention, abnormal driving behavior recognition has been receiving extensive attention. However, the granularity of existing abnormal driving behavior recognition is mostly at road-level, and these methods\u2019 high complexity leads to high overhead on training and recognition. In this article, we propose a deformation gated recurrent network for lane-level abnormal driving behavior recognition. First, we use conditional random field model to calculate the lane-change necessity of the vehicle, which helps us to distinguish whether the lane-changing behavior is reasonable. Second, we propose deformation gated recurrent network (DF-GRN) and trajectory entropy to capture the implicit relationship between trajectories and shorten recognition time. Finally, we get classified results including aggressive, distracted, and normal driving behavior from the network. Distracted and aggressive behavior will be marked as anomaly. The effectiveness and real-time nature of the network are verified by experiments on Hangzhou and Chengdu location datasets.<\/jats:p>","DOI":"10.1145\/3635141","type":"journal-article","created":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T12:02:31Z","timestamp":1701518551000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Deformation Gated Recurrent Network for Lane-level Abnormal Driving Behavior Recognition"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1064-1250","authenticated-orcid":false,"given":"Guojiang","family":"Shen","sequence":"first","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2247-3262","authenticated-orcid":false,"given":"Juntao","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2698-3319","authenticated-orcid":false,"given":"Xiangjie","family":"Kong","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1632-8740","authenticated-orcid":false,"given":"Zhanhao","family":"Ji","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2670-0059","authenticated-orcid":false,"given":"Bing","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2324-2523","authenticated-orcid":false,"given":"Tie","family":"Qiu","sequence":"additional","affiliation":[{"name":"Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.3029338"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.10.005"},{"key":"e_1_3_1_4_2","first-page":"1550","article-title":"Semi-Traj2Graph: Identifying fine-grained driving style with GPS trajectory data via multi-task learning","author":"Chen Chao","year":"2021","unstructured":"Chao Chen, Qiang Liu, Xingchen Wang, Chengwu Liao, and Daqing Zhang. 2021. Semi-Traj2Graph: Identifying fine-grained driving style with GPS trajectory data via multi-task learning. IEEE Trans. Big Data 8, 6 (2021), 1550\u20131565.","journal-title":"IEEE Trans. Big Data"},{"key":"e_1_3_1_5_2","article-title":"A flexible and explainable vehicle motion prediction and inference framework combining semi-supervised AOG and ST-LSTM","author":"Dai Shengzhe","year":"2020","unstructured":"Shengzhe Dai, Zhiheng Li, Li Li, Nanning Zheng, and Shuofeng Wang. 2020. A flexible and explainable vehicle motion prediction and inference framework combining semi-supervised AOG and ST-LSTM. IEEE Transactions on Intelligent Transportation Systems 23, 2 (2020), 840\u2013860.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMTT.2019.2934413"},{"key":"e_1_3_1_7_2","first-page":"8378","article-title":"Natural policy gradient primal-dual method for constrained Markov decision processes","volume":"33","author":"Ding Dongsheng","year":"2020","unstructured":"Dongsheng Ding, Kaiqing Zhang, Tamer Basar, and Mihailo Jovanovic. 2020. Natural policy gradient primal-dual method for constrained Markov decision processes. Adv. Neural Inf. Process. Syst. 33 (2020), 8378\u20138390.","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_1_8_2","article-title":"SafeDriving: An effective abnormal driving behavior detection system based on EMG signals","author":"Fan Yuanzhao","year":"2021","unstructured":"Yuanzhao Fan, Fei Gu, Jin Wang, Jianping Wang, Kejie Lu, and Jianwei Niu. 2021. SafeDriving: An effective abnormal driving behavior detection system based on EMG signals. IEEE Internet Things J. 9, 14 (2021), 12338\u201312350.","journal-title":"IEEE Internet Things J."},{"issue":"7","key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"2430","DOI":"10.1109\/TITS.2018.2866079","article-title":"Detection with a high-resolution automotive radar by introducing a new type of road marking","volume":"20","author":"Feng Zhaofei","year":"2018","unstructured":"Zhaofei Feng, Mingkang Li, Martin Stolz, Martin Kunert, and Werner Wiesbeck. 2018. Detection with a high-resolution automotive radar by introducing a new type of road marking. IEEE Trans. Intell. Transport. Syst.11 20, 7 (2018), 2430\u20132447.","journal-title":"IEEE Trans. Intell. Transport. Syst.11"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2886414"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.10.001"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482000"},{"key":"e_1_3_1_13_2","first-page":"8943","volume-title":"Proceedings of the International Conference on Robotics and Automation","author":"Jin Kefan","year":"2022","unstructured":"Kefan Jin, Hongye Wang, Changxing Liu, Yu Zhai, and Ling Tang. 2022. Graph neural network based relation learning for abnormal perception information detection in self-driving scenarios. In Proceedings of the International Conference on Robotics and Automation. IEEE, 8943\u20138949."},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","first-page":"9225","DOI":"10.1109\/TVT.2022.3176243","article-title":"RMGen: A tri-layer vehicular trajectory data generation model exploring urban region division and mobility pattern","author":"Kong Xiangjie","year":"2022","unstructured":"Xiangjie Kong, Qiao Chen, Mingliang Hou, Azizur Rahim, Kai Ma, and Feng Xia. 2022. RMGen: A tri-layer vehicular trajectory data generation model exploring urban region division and mobility pattern. IEEE Trans. Vehic. Technol. 71, 9 (2022), 9225\u20139238.","journal-title":"IEEE Trans. Vehic. Technol."},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3155162"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3111536"},{"issue":"4","key":"e_1_3_1_17_2","first-page":"2665","article-title":"A temporal-spatial deep learning approach for driver distraction detection based on EEG signals","volume":"19","author":"Li Guofa","year":"2021","unstructured":"Guofa Li, Weiquan Yan, Shen Li, Xingda Qu, Wenbo Chu, and Dongpu Cao. 2021. A temporal-spatial deep learning approach for driver distraction detection based on EEG signals. IEEE Trans. Autom. Sci. Eng. 19, 4 (2021), 2665\u20132677.","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"e_1_3_1_18_2","first-page":"1228","volume-title":"Proceedings of the IEEE International Conference on Data Mining","author":"Liu Hongbin","year":"2019","unstructured":"Hongbin Liu, Hao Wu, Weiwei Sun, and Ickjai Lee. 2019. Spatio-temporal GRU for trajectory classification. In Proceedings of the IEEE International Conference on Data Mining. IEEE, 1228\u20131233."},{"key":"e_1_3_1_19_2","first-page":"491","volume-title":"Proceedings of the International Conference on Database Systems for Advanced Applications","author":"Liu Zhidan","year":"2021","unstructured":"Zhidan Liu, Junhong Zheng, Zengyang Gong, Haodi Zhang, and Kaishun Wu. 2021. Exploiting multi-source data for adversarial driving style representation learning. In Proceedings of the International Conference on Database Systems for Advanced Applications. Springer, 491\u2013508."},{"issue":"4","key":"e_1_3_1_20_2","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1177\/0013916519879773","article-title":"Dealing with feeling crowded on public transport: The potential role of design","volume":"53","author":"Lombardi Debora B.","year":"2021","unstructured":"Debora B. Lombardi and Maria Rita Ciceri. 2021. Dealing with feeling crowded on public transport: The potential role of design. Environ. Behav. 53, 4 (2021), 339\u2013378.","journal-title":"Environ. Behav."},{"issue":"7","key":"e_1_3_1_21_2","first-page":"5718","article-title":"Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle","volume":"67","author":"Lv Chen","year":"2018","unstructured":"Chen Lv, Yang Xing, Chao Lu, Yahui Liu, Hongyan Guo, Hongbo Gao, and Dongpu Cao. 2018. Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle. IEEE Trans. Vehic. Technol. 67, 7 (2018), 5718\u20135729.","journal-title":"IEEE Trans. Vehic. Technol."},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01408"},{"key":"e_1_3_1_23_2","first-page":"387","volume-title":"Proceedings of the IEEE 19th International Conference on Intelligent Transportation Systems","author":"Romera Eduardo","year":"2016","unstructured":"Eduardo Romera, Luis M. Bergasa, and Roberto Arroyo. 2016. Need data for driver behaviour analysis? Presenting the public UAH-DriveSet. In Proceedings of the IEEE 19th International Conference on Intelligent Transportation Systems. IEEE, 387\u2013392."},{"key":"e_1_3_1_24_2","first-page":"1","volume-title":"Proceedings of the IEEE 20th International Conference on Intelligent Transportation Systems","author":"Saleh Khaled","year":"2017","unstructured":"Khaled Saleh, Mohammed Hossny, and Saeid Nahavandi. 2017. Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks. In Proceedings of the IEEE 20th International Conference on Intelligent Transportation Systems. IEEE, 1\u20136."},{"key":"e_1_3_1_25_2","article-title":"An attention-based digraph convolution network enabled framework for congestion recognition in three-dimensional road networks","author":"Shen Guojiang","year":"2021","unstructured":"Guojiang Shen, Xiao Han, KwaiSang Chin, and Xiangjie Kong. 2021. An attention-based digraph convolution network enabled framework for congestion recognition in three-dimensional road networks. IEEE Trans. Intell. Transport. Syst. 23, 9 (2021), 14413\u201314426.","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"issue":"3","key":"e_1_3_1_26_2","doi-asserted-by":"crossref","first-page":"2959","DOI":"10.1109\/TSG.2018.2815689","article-title":"Extended range electric vehicle with driving behavior estimation in energy management","volume":"10","author":"Vatanparvar Korosh","year":"2018","unstructured":"Korosh Vatanparvar, Sina Faezi, Igor Burago, Marco Levorato, and Mohammad Abdullah Al Faruque. 2018. Extended range electric vehicle with driving behavior estimation in energy management. IEEE Trans. Smart Grid 10, 3 (2018), 2959\u20132968.","journal-title":"IEEE Trans. Smart Grid"},{"key":"e_1_3_1_27_2","first-page":"125","volume-title":"Proceedings of the International Conference on Big Data","author":"Wang Chen","year":"2019","unstructured":"Chen Wang, Aibek Musaev, Pezhman Sheinidashtegol, and Travis Atkison. 2019. Towards detection of abnormal vehicle behavior using traffic cameras. In Proceedings of the International Conference on Big Data. Springer, 125\u2013136."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219985"},{"key":"e_1_3_1_29_2","first-page":"4685","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"37","author":"Wang Renzhi","year":"2023","unstructured":"Renzhi Wang, Senzhang Wang, Hao Yan, and Xiang Wang. 2023. WSiP: Wave superposition inspired pooling for dynamic interactions-aware trajectory prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 4685\u20134692."},{"issue":"8","key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TKDE.2020.3025580","article-title":"Deep learning for spatio-temporal data mining: A survey","volume":"34","author":"Wang Senzhang","year":"2020","unstructured":"Senzhang Wang, Jiannong Cao, and S. Yu Philip. 2020. Deep learning for spatio-temporal data mining: A survey. IEEE Trans. Knowl. Data Eng. 34, 8 (2020), 3681\u20133700.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3083152"},{"key":"e_1_3_1_32_2","first-page":"1303","volume-title":"Proceedings of the 16th ACM International Conference on Web Search and Data Mining","author":"Wang Zhaonan","year":"2023","unstructured":"Zhaonan Wang, Renhe Jiang, Zipei Fan, Xuan Song, and Ryosuke Shibasaki. 2023. Towards an event-aware urban mobility prediction system. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining. 1303\u20131304."},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16582"},{"key":"e_1_3_1_34_2","first-page":"2366","volume-title":"Proceedings of the IEEE 25th International Conference on Intelligent Transportation Systems","author":"Yang Shichun","year":"2022","unstructured":"Shichun Yang, Yuyi Chen, Yaoguang Cao, Rui Wang, Runwu Shi, and Jiayi Lu. 2022. Lane change trajectory prediction based on spatiotemporal attention mechanism. In Proceedings of the IEEE 25th International Conference on Intelligent Transportation Systems. IEEE, 2366\u20132371."},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.11.024"},{"issue":"8","key":"e_1_3_1_36_2","doi-asserted-by":"crossref","first-page":"7431","DOI":"10.1109\/TVT.2019.2926787","article-title":"A framework for turning behavior classification at intersections using 3D LiDAR","volume":"68","author":"Zhang Mingfang","year":"2019","unstructured":"Mingfang Zhang, Rui Fu, Daniel D. Morris, and Chang Wang. 2019. A framework for turning behavior classification at intersections using 3D LiDAR. IEEE Trans. Vehic. Technol. 68, 8 (2019), 7431\u20137442.","journal-title":"IEEE Trans. Vehic. Technol."},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2020.2991008"},{"issue":"5","key":"e_1_3_1_38_2","first-page":"2742","article-title":"Social-aware pedestrian trajectory prediction via states refinement LSTM","volume":"44","author":"Zhang Pu","year":"2022","unstructured":"Pu Zhang, Jianru Xue, Pengfei Zhang, Nanning Zheng, and Wanli Ouyang. 2022. Social-aware pedestrian trajectory prediction via states refinement LSTM. IEEE Trans. Pattern Anal. Mach. Intell. 44, 5 (2022), 2742\u20132759.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"e_1_3_1_39_2","doi-asserted-by":"crossref","first-page":"128317","DOI":"10.1016\/j.physa.2022.128317","article-title":"Recognition method of abnormal driving behavior using the bidirectional gated recurrent unit and convolutional neural network","volume":"609","author":"Zhang Yu","year":"2023","unstructured":"Yu Zhang, Yingying He, and Likai Zhang. 2023. Recognition method of abnormal driving behavior using the bidirectional gated recurrent unit and convolutional neural network. Physica A: Statist. Mechan. Applic. 609 (2023), 128317.","journal-title":"Physica A: Statist. Mechan. Applic."},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2010.2055239"},{"key":"e_1_3_1_41_2","first-page":"287","volume-title":"Proceedings of the IEEE 25th International Conference on Intelligent Transportation Systems","author":"Zhang Yi","year":"2022","unstructured":"Yi Zhang, Sheng Zhang, and Ruikang Luo. 2022. Lane change intent prediction based on multi-channel CNN considering vehicle time-series trajectory. In Proceedings of the IEEE 25th International Conference on Intelligent Transportation Systems. IEEE, 287\u2013292."},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2985082"},{"issue":"6","key":"e_1_3_1_43_2","doi-asserted-by":"crossref","first-page":"3934","DOI":"10.1109\/TNSE.2022.3144699","article-title":"2D federated learning for personalized human activity recognition in cyber-physical-social systems","volume":"9","author":"Zhou Xiaokang","year":"2022","unstructured":"Xiaokang Zhou, Wei Liang, Jianhua Ma, Zheng Yan, I. Kevin, and Kai Wang. 2022. 2D federated learning for personalized human activity recognition in cyber-physical-social systems. IEEE Trans. Netw. Sci. Eng. 9, 6 (2022), 3934\u20133944.","journal-title":"IEEE Trans. Netw. Sci. Eng."}],"container-title":["ACM Transactions on Spatial Algorithms and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3635141","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3635141","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:49:06Z","timestamp":1750286946000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3635141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"references-count":42,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,9,30]]}},"alternative-id":["10.1145\/3635141"],"URL":"https:\/\/doi.org\/10.1145\/3635141","relation":{},"ISSN":["2374-0353","2374-0361"],"issn-type":[{"value":"2374-0353","type":"print"},{"value":"2374-0361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"2023-03-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-21","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}