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The conventional crash box with thin-walled column conceals some limitations on pedestrian protection and lightweight. The metallic NPR metamaterials designed in this study are based on re-entrant lattice structures. Re-entrant structures are known to be one main class of axenic structures that display negative Poisson’s ratio (NPR), which can be manufactured by 3D printing technology. This kind of metamaterial has good designability and can be used as the filling structure of the crash box to improve the crashworthiness of the car. This paper starts from the relations between geometric parameters of the metamaterial. Considering the deformation characteristics of the crash box, the structure were designed into some gradient types. The mechanical properties of different gradient structures under the same impact conditions were compared to find the proper gradient structures. Based on the studies, the gradient lattice structure is applied to the automobile crash box.

An increasing demand for vehicle noise control has been proposed and at the same time, vehicle weight and fuel economy have become critical for the automotive industry. The methodology of statistical energy analysis (SEA) is used to balance both light weight and high noise insulation performance. In this paper, the vehicle dash and floor sound package systems, which are two of the major paths for vehicle interior noise, are studied and optimized by CAE and testing technology. Two types of sound packages which are the conventional insulation system and the lightweight one are chosen for the vehicle dash and floor system. The vehicle dash and floor systems are modeled by SEA and the transmission loss (TL) of the dash and floor system is analyzed, respectively. Several influence factors of the TL are also analyzed, such as sound package coverage, the leaks, etc.

Recently, several datasets have become available for occupant monitoring algorithm development, including real and synthetic datasets. However, real data acquisition is expensive and labeling is complex, while virtual data may not accurately reflect actual human physiology. To address these issues and obtain high-fidelity data for training intelligent driving monitoring systems, we have constructed a hybrid dataset that combines real driving image data with corresponding virtual data generated from 3D driving scenarios. We have also taken into account individual anthropometric measures and driving postures. Our approach not only greatly enriches the dataset by using virtual data to augment the sample size, but it also saves the need for extensive annotation efforts. Besides, we can enhance the authenticity of the virtual data by applying ergonomics techniques based on RAMSIS, which is crucial in dataset construction.