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Monitoring of lateral vehicle motion is very useful in many active safety applications such as yaw stability control and rollover prevention. Lateral velocity and sideslip angle are regarded as the most important motion variables. However, it is not feasible during vehicle operation to directly measure them due to the high cost of sensors, limitations to sensor technology, etc. Therefore, the monitoring of lateral vehicle motion can be realized by using either accurate monitoring models or improved estimation algorithms. In this paper, monitoring models are obtained such that they utilize not only the vehicle dynamics but the cornering stiffness and the roll motion as well. Based on these monitoring models, three monitoring systems are constructed to estimate the vehicle velocity and the sideslip angle based on the sliding mode observer, the extended Kalman filter (EKF) including Dugoff's tire model, and the scaled Kalman filter with model error compensator (SKFMEC) methods, respectively. The estimation performance of the monitoring systems is verified in field tests, and the experimental results demonstrate the effectiveness of this approach to provide accurate monitoring of lateral vehicle motion. [ABSTRACT FROM PUBLISHER]