基于復(fù)合深度Gauss回歸網(wǎng)絡(luò)的汽車ORS優(yōu)化設(shè)計(jì)

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中圖分類號(hào)：U461.91 文獻(xiàn)標(biāo)識(shí)碼：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

在汽車碰撞事故中，乘員約束系統(tǒng)（occupantrestraintsystems，ORS）是保護(hù)乘員生命安全的最后屏障，也是汽車安全開(kāi)發(fā)的重點(diǎn)[1-2]。（剩余13613字）