Todays Advanced Driver Assistance Systems (ADAS) change from single, independent functionalities to integrated multipurpose applications. The current method to reach these multipurpose applications is the combination of the existing independent applications. But the independent applications do not provide the universality to change basic assumptions or parameters, always requiring a redesign for integrating functionalities. Furthermore, the applications are designed for a clearly defined scenario as well as environment, making an all situation support merely impossible. In addition, the complexity and performance requirements on the system level are not manageable if isolated functions and modules are simply put together. Hence, another way has to be taken in order to reach the ultimate goal of an accident avoiding vehicle that can support the driver with superior abilities in all situations. To this end, a number of novel algorithms and approaches are presented in this thesis to overcome existing limitations. More specifically, the emphasis is placed on genericity, parameterization, and simple extensibility of the algorithms to emphasis the knowledge exchange in the system context. Additionally, the integration of all algorithms in a biological motivated system design proves the benefits of a generic system architecture. In general, the robust identification of the path requires diverse knowledge of many environmental characteristics. In particular, the path is the area in front of the vehicle which was identified to satisfy the current task (e.g. identification of the ego-lane, extraction of the stop position). Therefore, the path depends on a number of different features (as, e.g., the lanes, other vehicles, symbolic information, driving rules). All of these information sources are needed to robustly provide the path task dependently in all situations and also all environments. Therefore, the first aspect of this thesis handles the classification of the current driving scene in order to be able to parameterize the system depending on its current surrounding. A reliable classification of the scene is presented requiring a single image only, which is based on a computational model mimicking the characteristics of the human visual pathway. Hence, the system gets the ability to adapt its processing at run time to the environment. Furthermore, a novel two-tiered approach for traffic sign recognition is presented, divided in a generic attention based front-end for all visual processing and an array of weak classifiers for the traffic sign classification. The attention system provides regions of interest that can be simply classified by specific combinations of the weak classifiers for different sign classes. Therefore, the required symbolic information is available to solve all kinds of tasks as well as taking the current driving rules into account. Additionally, an important aspect on the system level are ego-moving objects, that must be measured as well as predicted in order to be able to plan the actions of the ego-vehicle. The novel 3D-Warping reliably detects all kinds of moving objects in different environments and measures also their motion parameters. Thereby, it differs from the state-of-the-art by directly using 3D data for the detection and measurement of the motion. Hence, a reliable, generic method for the incorporation of dynamic objects is presented. A generic system design draws largely benefit from the fusion of the single results, providing a higher robustness as well as further information by combining the individual results. To this end, the novel Task Dependent Representation Generation performs a task specific combination of different computational results. Furthermore, a generic and expandable layer architecture is presented facilitating a generic update and fusion process. Finally, all parts have to be combined in a generic system, which adapts to the environment as well as to the currently required task by the use of its modules and a generic architecture. Therefore, human-like cognitive processing principles are used to build a generic system with moderate requirements on computational power. This universality provides the basis for new generic ADAS supporting numerous tasks in different environments based on a single system.