Physics

Geology

GNSS applications

Aerospace engineering

Automotive engineering

Inertial measurement unit

Engineering

Artificial intelligence

Kalman filter

Gyroscope

Control (management)

Programming language

Pitch angle

SIGNAL (programming language)

Computer science

Global Positioning System

Vehicle dynamics

Computer vision

Smoothing

Kinematics

Control theory (sociology)

Classical mechanics

Telecommunications

Geophysics

IEEE Sensors Journal

Vehicle slip angle (VSA) estimation is of paramount importance for connected automated vehicle dynamic control, especially in critical lateral driving scenarios. In this paper, a novel kinematic-model-based VSA estimation method is proposed by fusing information from a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). First, to reject the gravity components induced by the vehicle roll and pitch, a vehicle attitude angle observer based on the square-root cubature Kalman filter (SCKF) is designed to estimate the roll and pitch. A novel feedback mechanism based on the vehicle intrinsic information (the steering angle and wheel speed) for the pitch and roll is designed. Then, the integration of the reverse smoothing and grey prediction is adopted to compensate for the cumulative velocity errors during the relatively low sampling interval of the GNSS. Moreover, the GNSS signal delay has been addressed by an estimation-prediction integrated framework. Finally, the results confirm that the proposed method can estimate the VSA under both the slalom and double lane change (DLC) scenarios.

Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic