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This paper presents an adaptive extended Kalman filter (EKF)-based sideslip angle estimator, which utilizes a sensor fusion concept that combines the high-rate inertial sensors measurements with the low-rate GPS velocity measurements. The sideslip angle estimation is based on a vehicle kinematic model relying on the lateral accelerometer and yaw rate gyro measurements. The vehicle velocity measurements from low-cost, single antenna GPS receiver are used for compensation of potentially large drift-like estimation errors caused by inertial sensors offsets. Adaptation of EKF state covariance matrix ensures a fast convergence of inertial sensors offsets estimates, and consequently a more accurate sideslip angle estimate.

This paper presents a detailed experimental analysis of potentials for road condition estimation based on the tire static curve gradient in the low-slip region, and the damping of the 40 Hz tire vibration mode. An experimental vehicle equipped with four standard ABS sensors (44 pulses/rev), two special high-resolution wheel speed sensors (512 pulses/rev), and a halfshaft torque sensor was utilized for collecting the experimental data. The test data were recorded on a test track characterized by different road conditions such as dry concrete, wet and dry ice, and wet and dry snow.