Traffic accidents are one of the leading causes of death around the world.One wellestablished strategy to deal with this public health issue is the design and deployment of road safety systems, which are in charge of predicting traffic crashes to promote safer roads.Increasing data availability has supported Machine learning (ML) to address the prediction of crashes and their severity.Transportation literature reports various methods for such purposes; however, there is no single method that achieves competitive results in all crash prediction problems.In this context, Automated machined learning (AutoML) arises as a suitable approach to automatically address the model selection problem in areas wherein specialized ML knowledge is not always available or affordable, such as road safety.AutoML has been successfully used in other areas; nevertheless, extensive analysis to determine their strengths and weaknesses has not been done in very diverse learning tasks, such as crash severity forecasting.Thus, this paper aims to examine to what extent AutoML can be competitive against ad hoc methods (Gradient Boosting, Gaussian Naive Bayes, k-Nearest Neighbors, Multilayer Perceptron, Random Forest) on crash severity prediction modeled from a supervised learning perspective.We test 3 state-of-the-art AutoML methods (Auto-Sklearn, TPOT, AutoGluon).Results show that AutoML can be considered a powerful approach to support the model selection problem in crash severity prediction.