The level of automation in vehicles is growing. But until all vehicles are completely automated, there will be a transition period where automated vehicles and human drivers coexist. Because these road users will coexist, it is necessary that automated vehicles understand human drivers and vice versa. This study aims to create a model that predicts human risk perception in different driving scenarios, to provide an understanding of the fundamental features of human threat perception while driving. The model created is a multi-criteria decision-making process that uses KITTI Vision Benchmark data as an input. This model is tested against the data gathered by an online survey, where 1918 participants answered the question: "How high is the risk on a scale from 0-10?" for 100 situations, chosen from the KITTI Vision Benchmark data. The survey response data is then compared to the model. Analysis of the survey data revealed that risk perception of driving situations is non-linear in the extremities of risk, showing that the input image are perceived as normally distributed instead of uniformly distributed. The comparison further shows that a model with features and weights solely based on literature is only slightly capable of predicting the risk of situations with a Pearson correlation coefficient with the survey responses of 0.28, whereas a model with feature weights optimised is moderately capable of predicting the risk of situations with a correlation coefficient of 0.57. However, multivariate regression is better capable of predicting risk with a correlation coefficient of 0.70, and shows that features and weights based on literature were not enough to establish an accurate model. The features that have the most impact on the result are the information about other road users' location and heading, the ego vehicle velocity, and the road type. Further research should focus on determining if speed is the cause of the perceived risk, or if it follows from other unknown predictors. Furthermore, an extension of the questions asked of the participants and the usage of videos instead of images can clarify the discrepancy between the literature-based model and the perceived risk of the participants.