Construction of a Surrogate Model: Multivariate Time Series Prediction with a Hybrid Model

TL;DR

This paper develops hybrid surrogate models combining classical and neural network methods to efficiently mimic automotive simulators, aiming to accelerate testing in advanced driver-assistance systems development.

Contribution

It introduces a novel hybrid modeling approach that integrates multiple classical and neural methods for surrogate modeling of complex simulators.

Findings

Hybrid models outperform individual methods in accuracy and speed.

The approach reduces testing time significantly.

Hybrid surrogate models effectively mimic simulator behavior.

Abstract

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.