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The head injury mechanisms of occupants in traffic accidents will be more complicated due to the diversified seating postures in autonomous driving environments. The injury risks and assessment parameters in complex collision conditions need to be investigated thoroughly. Mining the simulation data by the support vector machine (SVM) and the random forest algorithms, some head injury predictive models for a 6-year-old child occupant under a frontal 100% overlap rigid barrier crash scenario were developed. In these head injury predictive models, the impact speed and sitting posture of the occupant were considered as the input variables. All of these head injury predictive models were validated to have good regression and reliability (R2>0.93) by the ten-fold cross-validation. When the collision speed is less than 60km/h, rotational load is the primary factor leading to head injury, and the trends of BrIC, von Mise stress, Maxshear stress, and MPS are similar.