@article{ref1,
title="A sensor fusion method based on an integrated neural network and Kalman filter for vehicle roll angle estimation",
journal="Sensors (Basel)",
year="2016",
author="Vargas-Meléndez, Leandro and Boada, Beatriz L. and Boada, María Jesús L. and Gauchía, Antonio and Díaz, Vicente",
volume="16",
number="9",
pages="s16091400-s16091400",
abstract="This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a &quot;pseudo-roll angle&quot; through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors' estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s16091400",
url="http://dx.doi.org/10.3390/s16091400"
}