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Safety is the predominant concern in the advancement of automated vehicles (AVs); therefore, extensive real-world testing is underway to ensure their secure operation. Despite the widespread belief that they will decrease the frequency of accidents, there remains uncertainty about their impact on the severity of crashes in which they are involved. The primary objective of this study is to use the bagging classifier technique to predict the likelihood of injuries in accidents involving AVs. This was accomplished by conducting an in-depth examination of a wide range of independent variables and an analysis of injuries sustained in crash incidents involving AVs from 2014 to July 2023. The bagging classifier model showed notable effectiveness, achieving a balanced accuracy of 0.59, along with high precision and recall values of 0.94 and 0.97, respectively. These metrics indicate the model’s strong capability for …