{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T10:25:45Z","timestamp":1770459945963,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,19]],"date-time":"2022-03-19T00:00:00Z","timestamp":1647648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52072069"],"award-info":[{"award-number":["52072069"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Transportation Department of Henan Province","award":["2018G7"],"award-info":[{"award-number":["2018G7"]}]},{"name":"Jiangsu Creative PHD student sponsored project","award":["KYCX20_0138"],"award-info":[{"award-number":["KYCX20_0138"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Road traffic accidents regarding commercial vehicles have been demonstrated as an important culprit restricting the steady development of the social economy, which are closely related to the distracted behavior of drivers. However, the existing driver\u2019s distracted behavior surveillance systems for monitoring and preventing the distracted behavior of drivers still have some shortcomings such as fewer recognition objects and scenarios. This study aims to provide a more comprehensive methodological framework to demonstrate the significance of enlarging the recognition objects, scenarios and types of the existing driver\u2019s distracted behavior recognition systems. The driver\u2019s posture characteristics were primarily analyzed to provide the basis of the subsequent modeling. Five CNN sub-models were established for different posture categories and to improve the efficiency of recognition, accompanied by a holistic multi-cascaded CNN framework. To suggest the best model, image data sets of commercial vehicle driver postures including 117,410 daytime images and 60,480 night images were trained and tested. The findings demonstrate that compared to the non-cascaded models, both daytime and night cascaded models show better performance. Besides, the night models exhibit worse accuracy and better speed relative to their daytime model counterparts for both non-cascaded and cascaded models. This study could be used to develop countermeasures to improve driver safety and provide helpful information for the design of the driver\u2019s real-time monitoring and warning system as well as the automatic driving system. Future research could be implemented to combine the vehicle state parameters with the driver\u2019s microscopic behavior to establish a more comprehensive proactive surveillance system.<\/jats:p>","DOI":"10.3390\/s22062373","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"2373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Proactive Recognition System for Detecting Commercial Vehicle Driver\u2019s Distracted Behavior"],"prefix":"10.3390","volume":"22","author":[{"given":"Xintong","family":"Yan","sequence":"first","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 210018, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3752-4233","authenticated-orcid":false,"given":"Jie","family":"He","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 210018, China"}]},{"given":"Guanhe","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 210018, China"}]},{"given":"Changjian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 210018, China"}]},{"given":"Chenwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 210018, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,19]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2020, May 01). 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