Surrogate modeling of vehicle dynamics using Recurrent Neural Networks

Abstract

Computer Aided Engineering (CAE) is indispensable for vehicle design to reduce the development cost; however, its computation time is a heavy burden when tuning design parameters. In this respect, several studies have been carried out for replacing CAE with machine learning-based surrogate models. In this paper, we propose a novel neural sequence network-based surrogate model for CAE using Recurrent Neural Networks (RNNs), which are neural networks that treat sequences such as temporal sequences. Our target task is the NCAP Fishhook test to evaluate vehicle dynamics of the rollover propensity. We propose a machine learning model with a sequential model to calculate the response of the NCAP Fishhook test from vehicle parameters such as tire and suspension characteristics. Our model reduced the error in approximately 10% for the NCAP Fishhook test dataset, which is generated with CAE, compared to that of the baseline neural network model with multi-layer perceptrons (MLPs). Furthermore, to improve performance and stability, our model has the following task-specific characteristics: (1) the skip connection, (2) the hybrid loss, and (3) the scheduled sampling. We confirmed that the skip connection reduced errors in the additional ablation study. Our experiments showed that the sequential model is effective as a surrogate model for CAE, and we also find that there is still room for the improvement regarding the dataset and the model because the accuracy is not saturated with the current dataset.

Keywords

• deep learning
• neural network
• recurrent neural network
• ncap fishhook test
• cae
• skip connection
• scheduled sampling