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This assessment involves reconstructing the accident scenario based on information gathered from sources such as dashcam footage. The final determination of negligence is made by simulating the information contained in the video. Therefore, accident cases for split liability assessment should be classified based on information affecting the negligence degree. While deep learning has recently been in the spotlight for video recognition using short video clips, no research has been conducted to extract meaningful information from long videos, which are necessary for split liability assessment. To address this issue, we propose a new task for analysing long videos by stacking the important information predicted through the 3D CNNs model. We demonstrate the feasibility of our approach by proposing a split liability assessment method using dashcam footage.<\/jats:p>","DOI":"10.1093\/jcde\/qwad063","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T05:23:58Z","timestamp":1687843438000},"page":"1579-1601","source":"Crossref","is-referenced-by-count":2,"title":["Split liability assessment in car accident using 3D convolutional neural network"],"prefix":"10.1093","volume":"10","author":[{"given":"Sungjae","family":"Lee","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Gwangju institute of science and technology (GIST), 123 Cheomdangwagi-ro , Buk-gu, 61005 Gwangju , Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong-Gu","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Gwangju institute of science and technology (GIST), 123 Cheomdangwagi-ro , Buk-gu, 61005 Gwangju , Republic of 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