A Sensor Fusion Method Based on an IntegratedNeural Network and Kalman Filter for Vehicle Roll Angle Estimation Articles uri icon

publication date

  • August 2016

issue

  • 9

volume

  • 16

International Standard Serial Number (ISSN)

  • 1424-8220

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 "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.

keywords

  • Sensor fusion; Roll angle estimation; Neural network; Linear kalman filter; Fuzzy-logic; Sideslip; Dynamics