Construction of a Surrogate Model: Multivariate Time Series Prediction with a Hybrid Model
This work addresses the need for faster testing in the automotive industry, but it is incremental as it builds on existing methods without introducing a new paradigm.
The paper tackled the problem of validating advanced driver-assistance systems by constructing a surrogate model to mimic and replace slower simulators, resulting in an efficient hybrid model that combines random forests, ridge regression, and convolutional neural networks.
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.