More than one third of all traffic accidents with injuries occur in urban areas, especially at intersections. A suitable driver assistance system for such complex situations requires the understanding of the scene, in particular a reliable detection of other moving traffic participants. This contribution shows how a robust and fast detection of relevant moving objects is obtained by a smart combination of stereo vision and motion analysis. This approach, called 6D Vision, estimates location and motion of pixels simultaneously which enables the detection of moving objects on a pixel level. Using a Kalman filter attached to each tracked pixel, the algorithm propagates the current interpretation to the next image. In addition, a Kalman filter based ego-motion compensation is described that takes advantage of the 6D information. This precise information enables us to discriminate between static and moving objects exactly and to obtain a better prediction. This speeds up tracking and a real-time implementation is achieved. Examples of critical situations in urban areas exhibit the potential of the 6D Vision concept which can also be extended to robotics applications.