KALMAN FILTER BASED ROAD GRADE ESTIMATION METHOD USING ACCELEROMETER, GYROSCOPE, AND VEHICLE VELOCITY

摘要

Kalman filter based road grade estimation techniques use models of a longitudinal accelerometer, an angular pitch rate gyroscope, and a velocity sensor and their outputs, and fuses the sensor measurements to optimally estimate the road grade. The proposed Kalman filter formulation is unique in that it uses a mathematical model of the sensors where the gyroscope output is considered as the input and the combined accelerometer and velocity sensor output is considered as the output of the model whose states are to be estimated. By using this unique second-order state space model, a Kalman filter based estimation algorithm is developed to estimate road grade accurately in real-time. This estimated road grade is then being utilized by various vehicle efficiency and/or safety systems to improve vehicle efficiency and/or vehicle safety.