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The vehicle body sideslip angle (VBSSA) is a key variable in vehicle dynamics indicating critical driving situations. It is, e.g., essential in vehicle dynamics control concepts. Since it cannot be measured with standard sensors, it has to be determined via a model based approach. Thereto an Extended Kalman Filter will be presented that is capable of describing the VBSSA with high accuracy. The filter design is based on a nonlinear double track model combining the longitudinal and lateral dynamics. Starting point is a double track model with three state variables, that are the velocity in the center of gravity, the VBSSA and the yaw rate. Then, the longitudinal dynamics are incorporated, yielding the velocity and the longitudinal forces at the individual wheels. The resulting nonlinear state space model only requires information that is provided by the standard sensors available in series production vehicles. On basis of this nonlinear model an Extended Kalman Filter is derived.

In this approach a nonlinear controller for the lateral vehicle dynamics is designed. The basis for the design is a nonlinear model of the lateral vehicle dynamics in state space representation consisting of three states: The vehicle velocity, the yaw rate as well as the vehicle body sideslip angle (VBSSA). As control variables the yaw rate and the VBSSA are chosen. To assure the vehicle follows the driver's directional intent, the yaw rate is adapted to a desired reference value determined by means of a linear single track model. The second control variable -the VBSSA- is utilized to reduce the lateral forces. Incorporating the VBSSA, the controller's behavior can be significantly improved. Thus, a nonlinear controller is designed which is capable to stabilize the vehicle in critical driving situations. This nonlinear controller is based on an adaptation of a quality function for the nonlinear model to the one for a linear reference system.

The reproduction of the vehicle motion is a crucial element of accident reconstruction. Apart from the position of the center of gravity in an inertial coordinate system, the vehicle heading plays an important role. The heading is the sum of the yaw angle and the vehicle body side slip angle. In standard vehicles, the yaw angle can be determined using the yaw rate sensor and the wheel speeds. However, the yaw rate sensor is often subject to temperature drift. The wheel speed signals are forged at low speeds or due to slip. These errors result in significant deviations of reconstructed and real vehicle heading. Therefore, an intelligent combination of these signals is required. This paper describes a fuzzy system which is capable to increase the accuracy of yaw angle calculation by means of fuzzy logic. Before the data is applied to the fuzzy system, it is preprocessed to ensure the accuracy of the fuzzy system inputs.