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.
翻译:最近先进的助运系统的发展要求越来越多的测试,以验证新技术。这些测试无法在合理的时间里按部就班地进行,汽车组依赖模拟器进行大多数测试。这些用于不断改进任务的模拟器的可靠性正在成为一个问题,为了增加测试数量,该行业目前正在开发替代模型,这种模型应模仿模拟器的行为,同时要更快地执行具体任务。在本文件中,我们的目标是建立一个代用模型,以模拟和替换模拟器。我们首先测试一些典型方法,如随机森林、脊柱回归或共生神经网络。然后,我们建立三种混合模型,使用所有这些方法,并把它们结合起来,以获得高效的混合替代模型。