Model-Based Embedded Road Grade Estimation Using Quaternion Unscented Kalman Filter

The available road grade information makes a significant impact on improving the quality of vehicle control. In order to solve the limited application scenario and insufficient accuracy of current road grade estimation methods, this paper presents a novel model-based road grade estimation approach. First, a Quaternion Unscented Kalman Filter (QUKF) using the three-axle angular velocities and three-axle accelerations from a low-cost Inertial Measurement Unit (IMU) and the vehicle speed from CAN bus is designed to estimate the pitch angle of the vehicle. In particular, the measurement noise of UKF is analyzed by integrating Allan variance method. Second, a simplified vehicle-road model is derived to represent the road grade with the estimated pitch angle and longitudinal acceleration. Then, the performance of the proposed algorithm is tested by co-simulation of MATLAB/Simulink and CarSim, which indicates that the error rate of estimation is within 4%. Finally, the feasibility and accuracy of the proposed method implemented in the embedded prototype are verified in experiments conducted on standard slopes.

Automated summary

This paper presents a novel model-based road grade estimation approach, which has been verified to have good accuracy and precision through experiments.