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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.

With the development of intelligent cockpit, child occupants will engage in traffic operation in various sitting postures. Therefore, studying the mechanism and risk of whiplash injury of child occupants with different sitting postures has important application value for the research and development of child restraint system. In this study, the 120° and 135° sitting postures of six-year-old child occupant were developed based on the validated 105° sitting posture finite element model with detailed anatomical structure. The whiplash test in Euro NCAP was reconstructed to evaluate the influence of sitting posture angle on the risk of whiplash injury. In the three groups of simulation experiments, the Upper Neck Tension (Fz) was far less than the higher limit of Euro NCAP evaluation although the Fz value increased as the upper torso angle increases.

Computational human body models, especially detailed finite element models are suitable for investigation of human body kinematic responses and injury mechanism. A real-world lateral vehicle-tree impact accident was reconstructed by using finite element method according to the accident description in the CIREN database. At first, a baseline vehicle FE model was modified and validated according to the NCAP lateral impact test. The interaction between the car and the tree in the accident was simulated using LS-Dyna software. Parameters that affect the simulation results, such as the initial pre-crash speed, impact direction, and the initial impact location on the vehicle, were analyzed. The parameters were determined by matching the simulated vehicle body deformations and kinematics to the accident reports.