# Optimization Study of Driver Crash Injuries Considering the Body NWH Performance
## 1 Introduction
The utilization of neural networks serves to complement the limitations of the traditional response surface method, which heavily relies on test points. Consequently, this paper selects the Elman neural network model as the foundational model for predicting occupant injuries. While neural networks have made notable strides in the field of vehicle R&D, there remains ample room for improvement in terms of the prediction accuracy. Thus, selecting the appropriate neural network for specific optimization problems is of primary importance. [PERSON], [PERSON] et al. [27] applied an Elman neural network to accurately simulate and predict temperature changes during the digester digestion process. [PERSON], XY et al. [28] employed the Elman neural network for real-time battery capacity prediction. [PERSON], [PERSON] et al. [29] correlated human-related weather variables with active corona virus disease 2019 (COVID-19) cases using an artificial neural network (ANN) approach, with the Elman model exhibiting the best performance and the most accurate correlation coefficients, particularly in relation to the weather variables and active COVID-19 cases in Jordan. [PERSON], [PERSON] et al. [30] proposed a modeling and prediction method for indoor temperature hysteresis response characteristics, utilizing the time-lag neural network and an Elman neural network. The results indicated that the Elman network, with its simpler structure, reduced storage requirements and delivered a higher prediction accuracy, showing that this model serves as a superior modeling method for indoor temperature prediction. [PERSON], [PERSON] et al. [31] established a dynamics model for an electrohydraulic composite steering system, optimizing the steering assist torque distribution through genetic algorithms. They designed a fuzzy-compensated Elman neural network predictive controller to address the nonlinear challenges of the electrohydraulic composite system. The simulation results demonstrated that their coordinated control strategy substantially improved the electrohydraulic composite steering system in terms of the steering feel and energy consumption when compared to traditional electrohydraulic power steering systems. Therefore, this paper opts for the Elman neural network to predict driver and passenger injury values in crash accidents, subsequently comparing the prediction accuracy with other neural network approaches. A 25% overlap frontal crash is more prone to causing injuries to personnel compared to a standard frontal crash due to distinct energy absorption mechanisms. This presents one of the most challenging conditions to address in terms of vehicle crash safety. As research in this domain has deepened, several scholars have made significant strides in tackling this issue. By analyzing energy transfer pathways during crashes and optimizing vehicle structures, material thickness, and related factors, researchers have achieved notable improvements in terms of crash safety [32; 33; 34; 35; 36; 37]. However, previous optimization studies have focused primarily on enhancing crash safety without adequately considering other critical vehicle properties. This one-sided approach has often led to a trade-off, where improving crash safety results in the compromise of other vehicle attributes. In this paper, drawing from both domestic and international research on vehicle crashes, a specific compact car serves as an illustrative case study. This research aims to optimize crash safety while simultaneously considering the NVH performance of the vehicle. This optimization approach introduces constraints related to the body's first-order modal properties and its mass while prioritizing the minimization of driver head injury as the primary optimization objective [38]. The objective is to explore an optimization design method employing neural network algorithms to comprehensively address both crash safety and NVH performance in vehicle design.