Volume 3, Issue 1 (1-2017)                   ITCMS 2017, 3(1): 1-7 | Back to browse issues page

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Kim B, Kim G, Yoon J. Estimation Method for Steering Angle using In-Vehicle Sensors. ITCMS. 2017; 3 (1) :1-7
URL: http://europeansp.org/article-11-175-en.html
Graduate School of Electrical Engineering, University of Ulsan, 93 Daehak-ro,Ulsan, Republic of Korea
Abstract:   (47 Views)
For Advanced Driver Assist Systems (ADASs) and vehicle safety systems, accurate vehicle-state detection is one of the key issues that confirm system performance. Many of the vehicle-state parameters (sideslip angle, yaw rate, and steering angle) are important for the lateral dynamics of the vehicle. In the past, model-based methods have been used to estimate vehicle-state parameters using directly measured parameters (steering angle, yaw rate, etc.). In this paper, we propose a method to estimate the information of the vehicle s states (side-slip angle, steering angle) that are essential parameters for ADASs and vehicle safety systems. The proposed algorithm is based on the bicycle model and linear tire model using extended Kalman filter (EKF), which is a widely used method of estimating the vehicle state by combining the measured information from multiple sensors. Its merit is that it can be applied directly, without a separate linearization process even in a non-linear system. In this paper, the proposed algorithm was evaluated under different driving scenarios using the dSPACE s automotive simulation models (ASM) in Matlab/Simulink. Results from this study were similar to ideal vehicle state values using various driving speeds, with the maximum speed of the vehicle being 60 [km/h]. From the simulation results, utilizing a simple bicycle model and linear tire model, compared to the complex four-wheel vehicle model (FWVM), the estimated vehicle state values (sideslip angle, yaw rate, and steering angle) can be expected to contribute to the fault diagnosis of sensors, which directly measure vehicle-state variables, as well as improve safety.
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Received: 2019/08/8 | Accepted: 2019/08/8 | Published: 2019/08/8

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