Prediction of the classification of the pedestrian landing mechanism in pedestrian-vehicle collisions

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-07-23 DOI:10.1177/09544070241259899

Tiefang Zou, Pengchen Luo, Zhuzi Liu, Xiangting Yuan

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引用次数: 0

Abstract

To predict the classification of the pedestrian landing mechanism in pedestrian-vehicle collisions, 1303 reconstructed real pedestrian-vehicle collision cases were selected, and relevant data from before, during, and after the collisions were extracted. A total of 1303 sets of data with eight parameters were obtained via significance analysis, correlation analysis, and collinearity analysis. Then, the Backpropagation Neural Network (BPNN), Genetic Algorithm (GA) optimized BPNN (GA-BPNN), Principal Component Analysis (PCA) optimized BPNN (PCA-BPNN), Principal Component Analysis (PCA) and Genetic Algorithm (GA) optimized BPNN (PCA-GA-BPNN) were used to construct prediction models for the classification of the pedestrian landing mechanism, and the prediction effects were evaluated. The PCA-GA-BPNN model was found to be the optimal model; the prediction accuracies of the pre-collision, in-collision, and post-collision models were 72.4%, 96.4%, and 96.8%, respectively. Further analysis revealed that the optimal model could also accurately predict the classification of the pedestrian landing mechanism in six cadaver experiments. Additionally, the ratio of the pedestrian height to the vehicle hood height ( R P-V) was found to have an impact on the prediction effect of the model. Thus, an improved model considering R P-V was proposed, and was found to significantly improve the prediction accuracy of pedestrian forward-throwing mechanism. The research results provide new ideas for ground-related injury prediction, and also provide support for pedestrian protection in intelligent vehicles.

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行人与车辆碰撞中的行人着地机制分类预测

为了预测行人与车辆碰撞中行人着地机制的分类，选取了 1303 个重建的真实行人与车辆碰撞案例，并提取了碰撞前、碰撞中和碰撞后的相关数据。通过显著性分析、相关性分析和共线性分析，共得到 1303 组数据，其中包含 8 个参数。然后，利用反向传播神经网络（BPNN）、遗传算法（GA）优化的 BPNN（GA-BPNN）、主成分分析（PCA）优化的 BPNN（PCA-BPNN）、主成分分析（PCA）和遗传算法（GA）优化的 BPNN（PCA-GA-BPNN）构建了行人着地机理分类预测模型，并对预测效果进行了评估。结果发现，PCA-GA-BPNN 模型是最优模型；碰撞前、碰撞中和碰撞后模型的预测准确率分别为 72.4%、96.4% 和 96.8%。进一步的分析表明，在六个尸体实验中，最优模型也能准确预测行人着地机制的分类。此外，研究还发现行人高度与车辆引擎盖高度之比（R P-V）会影响模型的预测效果。因此，提出了考虑 R P-V 的改进模型，并发现该模型能显著提高行人前抛机构的预测精度。研究成果为地面相关伤害预测提供了新思路，也为智能车辆的行人保护提供了支持。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.