This paper presents an adaptive extended Kalman filter-based sideslip angle estimator, which utilizes the sensor fusion concept by combining 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. Namely, the proposed estimator simultaneously estimates the sideslip angle and inertial sensor offsets. Adaptation of EKF state covariance matrix, based on the maneuver dynamics, is utilized in order to ensure fast convergence of inertial sensors offsets estimates, and consequently a more accurate sideslip angle estimate. Throughout a detailed simulation analysis of estimator design the main sources of estimation errors have been identified to be the inaccuracies of pre-estimated vehicle longitudinal velocity obtained from nondriven wheel speed sensors, the GPS velocity measurement errors and signal latency, and the road bank-related disturbances. Moreover, several compensation methods have been proposed which may effectively decrease the related sideslip angle estimation errors. Finally, the simulation results show that the proposed EKF-based estimator enables the sideslip angle estimation accuracy within approximately 2 deg for a wide range of operating conditions, provided that the longitudinal velocity, bank, and GPS measurement latency errors are accurately pre-compensated.