基于复合深度Gauss回归网络的汽车ORS优化设计

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中图分类号：U461.91 文献标识码：A DOI：10.3969/j.issn.1674-8484.2025.03.002

Abstract：A data-driven optimization method was investigated forautomobile occupant restraint systems （ORS）based on composite deep Gaussian process regression network to improve the safety performance and to develop the eficiency of the ORS.In terms of the prediction of occupant dummy injury values，an improved composite deep Gaussian process regression network was proposed as the prediction model by combining neural network architecture with Gaussian process regression.Based on the prediction results，the ORS parameter optimization was carried out by using the group-based crow search algorithm.The method's effectiveness was verified byusing engineering simulation data.The results showed that this ORS design reduces the dummy injuries by up to 30.77% with an average of 12.11% compared to theoriginal engineering scheme.Therefore，the method can predict the injuryvalues formultiplepartsofthe dummywitha high-quality ORS design.

Keywords：automobile crash;occupant restraint systems （ORS）;dummy injury;data-driven; composite deep Gaussian process regression network; group-based crow search algorithm

在汽车碰撞事故中，乘员约束系统（occupantrestraintsystems，ORS）是保护乘员生命安全的最后屏障，也是汽车安全开发的重点[1-2]。（剩余13613字）