{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:02:21Z","timestamp":1777705341186,"version":"3.51.4"},"reference-count":56,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,2,14]]},"abstract":"<jats:p>The concept of endorsing AI in embedded systems is growing in all sectors including the development of Accident Avoidance Systems. Although real-time road crash prediction is vital for enhancing road user safety, there has been limited focus on the analysis of real-time crash events within ensemble and deep learning fused systems. The main aim of this paper is to design an advanced Accident Avoidance System established on a deep learning and ensemble fusion strategy in order to acquire more performant crash predictions. As such, four highly optimized models for crash prediction have been designed based on the popular ensemble techniques: CatBoost, AdaBoost and Bagging and the deep learning CNN. Additionally, four categories of features, including driver inputs, vehicle kinematics, driver states and weather conditions, were measured during the execution of various driving tasks performed on a driving simulator. Moreover, given the infrequent nature of crash events, an imbalance-control procedure was adopted using the SMOTE and ADASYN techniques. The highest performances results have been acquired using CatBoost along with ADASYN on almost all the adopted metrics during the different weather conditions, and more than 50% of all crashes have occurred in rainy weather conditions, whereas 31% have been exhibited in fog patterns. The sensitivity analysis results indicate that the fusing all the acquired features has the highest impact on the prediction performance. To our knowledge, there has been a limited interest, if not at all, at adopting a fused ensemble deep learning system examining the real-time impact of the adopted features\u2019 combinations on the prediction of road crashes while taking into account class imbalance. The findings provide new insights into crash prediction and emphasize the relevance of the explanatory features which can be endorsed in designing efficient Accident Avoidance Systems.<\/jats:p>","DOI":"10.3233\/jifs-232446","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T13:01:20Z","timestamp":1707224480000},"page":"3659-3676","source":"Crossref","is-referenced-by-count":2,"title":["An advanced accident avoidance system based on imbalance-control ensemble and deep learning fusion design"],"prefix":"10.1177","volume":"46","author":[{"given":"Dauha","family":"Elamrani Abou Elassad","sequence":"first","affiliation":[{"name":"SARS Research Team, Computer Science Department, ENSAS, Cadi Ayyad University, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zouhair","family":"Elamrani Abou Elassad","sequence":"additional","affiliation":[{"name":"SARS Research Team, Computer Science Department, ENSAS, Cadi Ayyad University, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdel Majid","family":"Ed-dahbi","sequence":"additional","affiliation":[{"name":"SARS Research Team, Computer Science Department, ENSAS, Cadi Ayyad University, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Othmane","family":"El Meslouhi","sequence":"additional","affiliation":[{"name":"SARS Research Team, Computer Science Department, ENSAS, Cadi Ayyad University, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mustapha","family":"Kardouchi","sequence":"additional","affiliation":[{"name":"PRIME Laboratory, Computer Science Department, Moncton University, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moulay","family":"Akhloufi","sequence":"additional","affiliation":[{"name":"PRIME Laboratory, Computer Science Department, Moncton University, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-232446_ref2","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/S0001-4575(00)00022-1","article-title":"Changes in young adult offense and crash patterns over time","volume":"33","author":"Waller","year":"2001","journal-title":"Accid. 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Prev."},{"key":"10.3233\/JIFS-232446_ref10","doi-asserted-by":"crossref","first-page":"103312","DOI":"10.1016\/j.engappai.2019.103312","article-title":"Theapplication of machine learning techniques for driving behavioranalysis: A conceptual framework and a systematic literaturereview, (March","volume":"87","author":"Elamrani Abou Elassad","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"2","key":"10.3233\/JIFS-232446_ref11","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.3233\/JIFS-190558","article-title":"Design and implementation of intelligent traffic and big data mining system based on internet of things","volume":"38","author":"Li","year":"2020","journal-title":"J. Intell. 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Prev."},{"key":"10.3233\/JIFS-232446_ref44","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.aap.2012.01.020","article-title":"Evaluation of the impacts of traffic states on crash risks on freeways","volume":"47","author":"Xu","year":"2012","journal-title":"Accid. Anal. Prev."},{"issue":"2","key":"10.3233\/JIFS-232446_ref45","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1007\/s40534-017-0129-7","article-title":"Real-time crash prediction on freeways using data mining and emerging techniques","volume":"25","author":"You","year":"2017","journal-title":"J. Mod. Transp."},{"key":"10.3233\/JIFS-232446_ref46","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.3233\/JIFS-232446_ref47","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTEfor Learning from Imbalanced Data: Progress and Challenges, Markingthe 15-year Anniversary","volume":"61","author":"Fern\u00e1ndez","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"10.3233\/JIFS-232446_ref51","doi-asserted-by":"crossref","first-page":"106314","DOI":"10.1016\/j.knosys.2020.106314","article-title":"A proactive decision support system for predicting traffic crash events: A critical analysis of imbalanced class distribution","volume":"205","author":"Elamrani Abou Elassad","year":"2020","journal-title":"Knowledge-Based Syst."},{"key":"10.3233\/JIFS-232446_ref53","unstructured":"Swift D. , Schofield D. , THE IMPACT OF COLOR ON SECONDARY TASK TIME WHILE DRIVING, Int. J. Inf. Technol. 4(3) (2019)."},{"issue":"3","key":"10.3233\/JIFS-232446_ref54","doi-asserted-by":"crossref","first-page":"e0213258","DOI":"10.1371\/journal.pone.0213258","article-title":"Exploring the use of machine learning for risk adjustment: A comparison of standard and penalized linear regression models in predicting health care costs in older adults","volume":"14","author":"Kan","year":"2019","journal-title":"PLoS One"},{"issue":"3","key":"10.3233\/JIFS-232446_ref55","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.ijar.2008.11.004","article-title":"Hierarchical fuzzyrule based classification systems with genetic rule selection forimbalanced data-sets","volume":"50","author":"Fern\u00e1ndez","year":"2009","journal-title":"Int. J. Approx. Reason."},{"issue":"9","key":"10.3233\/JIFS-232446_ref56","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from Imbalanced Data","volume":"21","author":"Haibo He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"04","key":"10.3233\/JIFS-232446_ref57","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1142\/S0218001409007326","article-title":"CLASSIFICATION OF IMBALANCED DATA: A REVIEW","volume":"23","author":"SUN","year":"2009","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"10.3233\/JIFS-232446_ref58","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.ssci.2019.04.026","article-title":"Evaluating machine learning performance in predicting injury severity in agribusiness industries","volume":"117","author":"Davoudi Kakhki","year":"2019","journal-title":"Saf. Sci."},{"issue":"4","key":"10.3233\/JIFS-232446_ref60","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1016\/j.eswa.2007.06.037","article-title":"Forecasting financial condition of Chinese listed companies based on support vector machine","volume":"34","author":"Ding","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"10.3233\/JIFS-232446_ref61","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.ndteint.2018.11.019","article-title":"Abidin, Quantitative evaluation of crack depths and angles for pulsed eddy current non-destructive testing","volume":"102","author":"Nafiah","year":"2019","journal-title":"NDT E Int."},{"issue":"11\u201312","key":"10.3233\/JIFS-232446_ref62","first-page":"1131","article-title":"Neural network credit scoring models","volume":"27","author":"West","journal-title":"Comput. Oper. Res."},{"issue":"1","key":"10.3233\/JIFS-232446_ref63","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"10.3233\/JIFS-232446_ref64","unstructured":"Prokhorenkova L. , Gusev G. , Vorobev A. , Dorogush A.V. , Gulin A. , CatBoost: unbiased boosting with categorical features , Adv. Neural Inf. Process. Syst. 31 (2018)."},{"issue":"421","key":"10.3233\/JIFS-232446_ref66","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging Predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. 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