Destructive car crash tests are an elaborate, time-consuming, and expensive necessity of the automotive development process. Today, finite element method (FEM) simulations are used to reduce costs by simulating car crashes computationally. We propose CrashNet, an encoder–decoder deep neural network architecture that reduces costs further and models specific outcomes of car crashes very accurately. We achieve this by formulating car crash events as time series prediction enriched with a set of scalar features. Traditional sequence-to-sequence models are usually composed of convolutional neural network (CNN) and CNN transpose layers. We propose to concatenate those with an MLP capable of learning how to inject the given scalars into the output time series. In addition, we replace the CNN transpose with 2D CNN transpose layers in order to force the model to process the hidden state of the set of scalars as one time series. The proposed CrashNet model can be trained efficiently and is able to process scalars and time series as input in order to infer the results of crash tests. CrashNet produces results faster and at a lower cost compared to destructive tests and FEM simulations. Moreover, it represents a novel approach in the car safety management domain.

破坏性汽车碰撞测试是汽车开发过程中一项复杂、耗时且昂贵的必需品。今天，有限元法 (FEM) 模拟被用于通过计算模拟车祸来降低成本。我们提出了 CrashNet，这是一种编码器-解码器深度神经网络架构，可进一步降低成本并非常准确地模拟车祸的特定结果。我们通过将车祸事件制定为具有一组标量特征的时间序列预测来实现这一点。传统的序列到序列模型通常由卷积神经网络 (CNN) 和 CNN 转置层组成。我们建议将那些与能够学习如何将给定标量注入输出时间序列的 MLP 连接起来。此外，我们将 CNN 转置替换为 2D CNN 转置层，以强制模型将一组标量的隐藏状态作为一个时间序列进行处理。提出的 CrashNet 模型可以有效地训练，并且能够处理标量和时间序列作为输入，以推断碰撞测试的结果。与破坏性测试和 FEM 模拟相比，CrashNet 生成结果更快，成本更低。此外，它代表了汽车安全管理领域的一种新方法。