This thesis describes a method for the driver-adaptive prediction of driving maneuvers. This method builds a model of the driving behavior from data based on the observation of sample driving maneuvers. The method is grounded on the theory of fuzzy logic, which provides a framework for reasoning about a domain that approximates human reasoning. After a learning period, the driver model is capable of predicting subsequent driving maneuvers. That information can be used to adapt advanced driver assistance systems, as exemplified by an autonomous braking system, to the driver's individual behavior. This thesis contains three major contributions. The first is the driver-adaptive construction of fuzzy variables. A method was developed that converts histograms into fuzzy variables by approximating them through Gaussian Mixture Models and converting the resulting Gaussian distributions into trapezoidal fuzzy sets. These fuzzy variables partition quantities of interest, such as vehicle velocity, into fuzzy sets that have well defined and driver-specific semantic meanings. The second contribution is the generation of a driver-adaptive fuzzy state machine modeling the sequential pattern of driving maneuvers based on sample data. A quality measure assessing the performance of states and state sequences was developed that optimizes the state machine, resulting in an accurate and analyzable representation of individual driving behavior. The third contribution is the development of a driver-adaptive and situation-specific autonomous braking algorithm. The algorithm triggers an autonomous brake in a specific relevant critical situation, taking into account the prediction of individual driving behavior in the decision process. It could be shown through a simulator study that the algorithm is capable of significantly reducing the severity of, or even avoiding, the collisions that were encountered in that study, without also causing any undesired activation of the autonomous brake, thus providing significant assistance to the driver.