Stereo vision is a key technology for understanding natural scenes. Most research concentrates on single image pairs. However, in robotic and intelligent vehicles applications image sequences have to be analyzed. The paper shows that an appropriate evaluation in time gives much better results than classical frame-by-frame reconstructions. We start with the state-of-the art in real-time stereo analysis and describes novel techniques to increase the sub-pixel accuracy. Secondly, we show that static scenes seen from a moving observer can be reconstructed with significantly higher precision, if the stereo correspondences are integrated over time. Finally, an optimal fusion of stereo and optical flow, called 6D-Vision, is described that directly estimates position and motion of tracked features, even if the observer is moving. This eases the detection and tracking of moving obstacles significantly.