Deep Learning-Based Estimation of the Unknown Road Profile and State Variables for the Vehicle Suspension System

Abstract

The vehicle suspension control unit serves as a critical component to the vehicle system, as it ensures the steering stability and sound ride quality of the vehicle. To effectively realize control strategies, it is essential to foreknowledge the road profile and the suspension system's internal state variables. While the mentioned variables are not practically measurable using commercial sensors, it is necessary to estimate the desired variables by utilizing observer systems. Conventional means have mainly employed model-based approaches, in which model uncertainties and high computational cost pose limitations for practical implementation. Herein, we propose a data-driven deep learning method as an alternative because no explicit physical modeling is required, and evaluation is computationally cheap. We first propose a novel encoder-decoder structured recurrent neural network model with a two-phase attention mechanism to estimate the unknown road profile and four state variables of the vehicle suspension system. Based on a simulated data set, we assess the proposed model's qualitative and quantitative results and demonstrate that our model can achieve highly accurate estimation results with fast computation time. Besides, we validate our black-box model's reliability by comparing its interpretation with the suspension system's actual physical characteristics. Furthermore, we compare the proposed model with existing baseline methods, and the results show that our proposed deep learning model significantly outperforms the baseline. Lastly, we experiment with our network's autoregressive capability and demonstrate the feasibility of estimating a sequence of future values, which has not been presented in previous works.