{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T13:22:49Z","timestamp":1762608169062,"version":"3.41.2"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T00:00:00Z","timestamp":1612483200000},"content-version":"vor","delay-in-days":35,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["CHD 300102220220"],"award-info":[{"award-number":["CHD 300102220220"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>One of the major challenges that connected autonomous vehicles (CAVs) are facing today is driving in urban environments. To achieve this goal, CAVs need to have the ability to understand the crossing intention of pedestrians. However, for autonomous vehicles, it is quite challenging to understand pedestrians\u2019 crossing intentions. Because the pedestrian is a very complex individual, their intention to cross the street is affected by the weather, the surrounding traffic environment, and even his own emotions. If the established street crossing intention recognition model cannot be updated in real time according to the diversity of samples, the efficiency of human\u2010machine interaction and the interaction safety will be greatly affected. Based on the above problems, this paper established a pedestrian crossing intention model based on the online semisupervised support vector machine algorithm (OS<jats:sup>3<\/jats:sup>VM). In order to verify the effectiveness of the model, this paper collects a large amount of pedestrian crossing data and vehicle movement data based on laser scanner, and determines the main feature components of the model input through feature extraction and principal component analysis (PCA). The comparison results of recognition accuracy of SVM, S<jats:sup>3<\/jats:sup>VM, and OS<jats:sup>3<\/jats:sup>VM indicate that the proposed OS<jats:sup>3<\/jats:sup>VM model exhibits a better ability to recognize pedestrian crossing intentions than the SVM and S<jats:sup>3<\/jats:sup>VM models, and the accuracy achieves 94.83%. Therefore, the OS<jats:sup>3<\/jats:sup>VM model can reduce the number of labeled samples for training the classifier and improve the recognition accuracy.<\/jats:p>","DOI":"10.1155\/2021\/6621451","type":"journal-article","created":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T22:03:49Z","timestamp":1612821829000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Online Semisupervised Learning Model for Pedestrians\u2019 Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications"],"prefix":"10.1155","volume":"2021","author":[{"given":"Shicai","family":"Ji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8186-7647","authenticated-orcid":false,"given":"Hongjia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengbo","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,2,5]]},"reference":[{"volume-title":"Road Traffic Safety Development Report","year":"2019","author":"Traffic Administration Bureau of the Ministry of Public Security of the People\u2019s Republic of China","key":"e_1_2_9_1_2"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trf.2019.07.004"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3025687"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2901817"},{"key":"e_1_2_9_5_2","doi-asserted-by":"crossref","unstructured":"YangB.andNiR. Vision-based recognition of pedestrian crossing intention in an urban environment 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation Control and Intelligent Systems (CYBER) 2019 Suzhou China 992\u2013995 https:\/\/doi.org\/10.1109\/CYBER46603.2019.9066706.","DOI":"10.1109\/CYBER46603.2019.9066706"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trf.2017.09.012"},{"key":"e_1_2_9_7_2","doi-asserted-by":"crossref","unstructured":"SchulzA. T.andStiefelhagenR. A controlled interactive multiple model filter for combined pedestrian intention recognition and path prediction 2015 IEEE 18th International Conference on Intelligent Transportation Systems 2015 Las Palmas 173\u2013178 https:\/\/doi.org\/10.1109\/ITSC.2015.37 2-s2.0-84950243832.","DOI":"10.1109\/ITSC.2015.37"},{"key":"e_1_2_9_8_2","doi-asserted-by":"crossref","unstructured":"VarytimidisD. Alonso-FernandezF. DuranB. andEnglundC. Action and intention recognition of pedestrians in urban traffic 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) 2018 Las Palmas de Gran Canaria Spain 676\u2013682 https:\/\/doi.org\/10.1109\/SITIS.2018.00109 2-s2.0-85065906502.","DOI":"10.1109\/SITIS.2018.00109"},{"key":"e_1_2_9_9_2","doi-asserted-by":"crossref","unstructured":"V\u00f6lzB. BehrendtK. MielenzH. GilitschenskiI. SiegwartR. andNietoJ. A data-driven approach for pedestrian intention estimation 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) 2016 Rio de Janeiro Brazil 2607\u20132612 https:\/\/doi.org\/10.1109\/ITSC.2016.7795975 2-s2.0-85010040213.","DOI":"10.1109\/ITSC.2016.7795975"},{"key":"e_1_2_9_10_2","doi-asserted-by":"crossref","unstructured":"ParkK.-H.andLeeS.-W. Movement intention decoding based on deep learning for multiuser myoelectric interfaces 2016 4th International Winter Conference on Brain-Computer Interface (BCI) Feb. 2016 Yongpyong South Korea 1\u20132 https:\/\/doi.org\/10.1109\/IWW-BCI.2016.7457459 2-s2.0-84969142146.","DOI":"10.1109\/IWW-BCI.2016.7457459"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20061776"},{"key":"e_1_2_9_12_2","doi-asserted-by":"crossref","unstructured":"SchneemannF.andHeinemannP. Context-based detection of pedestrian crossing intention for autonomous driving in urban environments 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016 Daejeon South Korea 2243\u20132248 https:\/\/doi.org\/10.1109\/IROS.2016.7759351 2-s2.0-85006415449.","DOI":"10.1109\/IROS.2016.7759351"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2927889"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17102193"},{"key":"e_1_2_9_15_2","doi-asserted-by":"crossref","unstructured":"\u0160kovierov\u00e1J. Vobeck\u00fdA. UllerM. \u0160kovieraR. andHlav\u00e1\u010dV. Motion prediction influence on the pedestrian intention estimation near a zebra crossing Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems 16\u201318 March 2018 Madeira Portugal 341\u2013348 https:\/\/doi.org\/10.5220\/0006694403410348.","DOI":"10.5220\/0006694403410348"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2836305"},{"key":"e_1_2_9_17_2","doi-asserted-by":"crossref","unstructured":"QuinteroR. ParraI. LorenzoJ. Fern\u00e1ndez-LlorcaD. andSoteloM. A. Pedestrian intention recognition by means of a hidden Markov model and body language 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) 2017 Yokohama 1\u20137 https:\/\/doi.org\/10.1109\/ITSC.2017.8317766 2-s2.0-85046283574.","DOI":"10.1109\/ITSC.2017.8317766"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3017505"},{"key":"e_1_2_9_19_2","doi-asserted-by":"crossref","unstructured":"CaruanaR.andNiculescu-MizilA. An empirical comparison of supervised learning algorithms Proceedings of the 23rd International Conference on Machine Learning - ICML \u203206 2006 New York https:\/\/doi.org\/10.1145\/1143844.1143865 2-s2.0-34250744208.","DOI":"10.1145\/1143844.1143865"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.2200\/S00196ED1V01Y200906AIM006"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2939188"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.10.054"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-87479-9_44"},{"key":"e_1_2_9_24_2","doi-asserted-by":"crossref","unstructured":"ZeislB. LeistnerC. SaffariA. andBischofH. On-line semi-supervised multiple-instance boosting 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2010 San Francisco CA https:\/\/doi.org\/10.1109\/CVPR.2010.5539860 2-s2.0-77955991184.","DOI":"10.1109\/CVPR.2010.5539860"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2019.05.012"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2016.05.014"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trf.2015.11.007"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtte.2017.06.005"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2267734"},{"key":"e_1_2_9_30_2","doi-asserted-by":"crossref","unstructured":"HashimotoY. GuY. HsuL. andKamijoS. Probability estimation for pedestrian crossing intention at signalized crosswalks 2015 IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2015 Yokohama 114\u2013119 https:\/\/doi.org\/10.1109\/ICVES.2015.7396904 2-s2.0-84966784660.","DOI":"10.1109\/ICVES.2015.7396904"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3017596"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-22899-6_34"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2706978"},{"volume-title":"Semi-supervised learning literature survey","year":"2005","author":"Zhu X. J.","key":"e_1_2_9_34_2"},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.05.035"},{"key":"e_1_2_9_36_2","article-title":"Semi-supervised classification by low density separation","author":"Chapella O.","year":"2004","journal-title":"Machine Learning"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-007-5014-x"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000018"}],"container-title":["Wireless Communications and Mobile Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/6621451.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/6621451.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/6621451","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T10:33:18Z","timestamp":1723026798000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/6621451"}},"subtitle":[],"editor":[{"given":"Shaohua","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/6621451"],"URL":"https:\/\/doi.org\/10.1155\/2021\/6621451","archive":["Portico"],"relation":{},"ISSN":["1530-8669","1530-8677"],"issn-type":[{"type":"print","value":"1530-8669"},{"type":"electronic","value":"1530-8677"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2020-11-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-01-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-02-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"6621451"}}