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Accident statistics have shown that older and obese occupants are less adaptable to existing vehicle occupant restraint systems than ordinary middle-aged male occupants, and tend to have higher injury risk in vehicle crashes. However, the current research on injury mechanism of aging and obese occupants in vehicle frontal impacts is scarce. This paper focuses on the optimization design method of occupant restraint system parameters for specific body type characteristics. Three parameters, namely the force limit value of the force limiter in the seat belt, pretensioner preload of the seat belt and the proportionality coefficient of mass flow rate of the inflator were used for optimization. The objective was to minimize the injury risk probability subjected to constraints of occupant injury indicator values for various body regions as specified in US-NCAP frontal impact tests requirements.

Forward Collision Warning System is an important part of vehicle active safety system, it can reduce the occurrence of rear-end collision accidents with high fatality rate and improve the safety of driving. At present, there are still some outstanding issues to be addressed among the existing forward collision warning systems, such as the high cost of information acquisition based on LiDAR and other high-definition sensors, and the poor real-time performance of target detection based on vision. In view of the aforementioned issues and in order to improve the detection accuracy and real-time requirements of the target detection function of the early warning system, this paper proposes an enhanced deep learning model-based vehicle target detection method, and improves the key techniques of target detection, ranging and speed measurement and early warning strategy in the warning system.

Computer Aided Engineering (CAE) models have proven themselves to be efficient surrogates of real-world systems in automotive industries and academia. To successfully integrate the CAE models into analysis process, model validation is necessarily required to assess the models’ predictive capabilities regarding their intended usage. In the context of model validation, quantitative comparison which considers specific measurements in real-world systems and corresponding simulations serves as a principal step in the assessment process. For applications such as side impact analysis, surface deformation is frequently regarded as a critical factor to be measured for the validation of CAE models. However, recent approaches for such application are commonly based on graphical comparison, while researches on the quantitative metric for surface-surface comparison are rarely found.