TY  - JOUR
PY  - 2016//
TI  - A sensor fusion method based on an integrated neural network and Kalman filter for vehicle roll angle estimation
JO  - Sensors (Basel)
A1  - Vargas-Meléndez, Leandro
A1  - Boada, Beatriz L.
A1  - Boada, María Jesús L.
A1  - Gauchía, Antonio
A1  - Díaz, Vicente
SP  - s16091400
EP  - s16091400
VL  - 16
IS  - 9
N2  - 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 "pseudo-roll angle" 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>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s16091400
ID  - ref1
ER  -