论文标题
替代模型的构建：使用混合模型的多元时间序列预测
Construction of a Surrogate Model: Multivariate Time Series Prediction with a Hybrid Model
论文作者
论文摘要
高级驾驶员辅助系统的最新发展需要越来越多的测试来验证新技术。这些测试不能在合理的时间内在轨道上进行，并且汽车组依靠模拟器来执行大多数测试。这些模拟器对不断完善任务的可靠性正在成为一个问题，并且为了增加测试的数量，该行业现在正在开发替代模型，这些模型应该模仿模拟器的行为，同时更快地运行特定任务。 在本文中，我们旨在构建一个替代模型来模仿和替换模拟器。我们首先测试了几种经典方法，例如随机森林，山脊回归或卷积神经网络。然后，我们构建了三个使用所有这些方法的混合模型，并将它们组合在一起以获得有效的混合替代模型。
Recent developments of advanced driver-assistance systems necessitate an increasing number of tests to validate new technologies. These tests cannot be carried out on track in a reasonable amount of time and automotive groups rely on simulators to perform most tests. The reliability of these simulators for constantly refined tasks is becoming an issue and, to increase the number of tests, the industry is now developing surrogate models, that should mimic the behavior of the simulator while being much faster to run on specific tasks. In this paper we aim to construct a surrogate model to mimic and replace the simulator. We first test several classical methods such as random forests, ridge regression or convolutional neural networks. Then we build three hybrid models that use all these methods and combine them to obtain an efficient hybrid surrogate model.
