Vargas Meléndez, Leandro JoséLópez Boada, BeatrizLópez Boada, María JesúsGauchía Babé, AntonioDíaz López, Vicente2016-09-162016-09-162016-08-31Vargas-Meléndez, L., Boada, B.L., Boada, M.J.L., Gauchía, A., Díaz, V. (2016). A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation. Sensors, 16 (9), 1400.1424-8220https://hdl.handle.net/10016/23582This 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.18application/pdfengAtribución 3.0 EspañaSensor fusionRoll angle estimationNeural networkLinear Kalman filterresearch articleIngeniería Mecánicahttps://www.doi.org/10.3390/s16091400open access9Sensors16AR/0000018108