{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T22:16:16Z","timestamp":1781907376655,"version":"3.54.5"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>In this study, we investigate a deep learning-based vehicle fault prediction model aimed at achieving accurate prediction of vehicle faults by analyzing the correlations among different faults and the impact of critical faults on future fault development. To this end, we propose a collaborative modeling approach utilizing multiple attention mechanisms. This approach incorporates a graph attention mechanism for the fusion representation of fault correlation information and employs a novel learning method that combines a Long Short-Term Memory (LSTM) network with an attention mechanism to capture the impact of key faults. Based on experimental validation using real-world vehicle fault record data, the model significantly outperforms existing prediction models in terms of fault prediction accuracy.<\/jats:p>","DOI":"10.3390\/fi17090428","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:53:42Z","timestamp":1758300822000},"page":"428","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Collaborative Fusion Attention Mechanism for Vehicle Fault Prediction"],"prefix":"10.3390","volume":"17","author":[{"given":"Hong","family":"Jia","sequence":"first","affiliation":[{"name":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Automobile Transportation Research Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China"},{"name":"Key Laboratory of Operation Safety Technology on Transport Vehicles, Research Institute of Highway Ministry of Transport, Beijing 100088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dalin","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6441-1986","authenticated-orcid":false,"given":"Fanghua","family":"Chen","sequence":"additional","affiliation":[{"name":"Automobile Transportation Research Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China"},{"name":"Key Laboratory of Operation Safety Technology on Transport Vehicles, Research Institute of Highway Ministry of Transport, Beijing 100088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Automobile Transportation Research Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China"},{"name":"Key Laboratory of Operation Safety Technology on Transport Vehicles, Research Institute of Highway Ministry of Transport, Beijing 100088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Qi, J. 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