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Vehicle occupant classification algorithm based on T-S fuzzy model

机译:基于T-S模糊模型的车辆乘员分类算法

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One of the critical logic deployment challenges faced by intelligent airbag system is whether the system can accurately determine the type of the occupant sitting in different postures. The main objective of this paper is the application of seat pressure sensors to predict the occupant classification, such as 5th percentile adult female and 50th percentile adult male and 95th percentile adult male. In the experiments, the occupants are recruited to sit in specified postures. During sitting, the pressure data of the occupants are measured. After that, T-S fuzzy model is introduced to investigate the highly nonlinear relationship between the pressure and the weight of the occupant. Finally, the system uses the weight information to determine the type of the occupant. Since the good validity and accuracy of the weight calculated by the T-S fuzzy model are shown in the experiments, the proposed algorithm can effectively identify the type of the occupant.
机译:智能气囊系统面临的关键逻辑部署挑战之一是系统是否可以准确地确定坐在不同姿势的乘员的类型。本文的主要目的是座椅压力传感器的应用,以预测乘员分类,如第五位成年女性和第50位成年男性和第95百分位成年男性。在实验中,招募人员被招募坐在特定姿势中。在坐着期间,测量乘员的压力数据。之后,引入T-S模糊模型以研究乘员的压力与重量之间的高度非线性关系。最后,系统使用权重信息来确定乘员的类型。由于在实验中示出了由T-S模糊模型计算的重量的良好有效性和准确性,因此该算法可以有效地识别乘员的类型。

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