Environment sensing is an essential property for autonomous cars. With the help of sensors, nearby objects can be detected and localized. Furthermore, the creation of an accurate model of the surroundings is crucial for highlevel planning. In this paper, we focus on vehicle detection based on stereo camera images. While stereoscopic computer vision is applied to localize objects in the environment, the objects are then identified by image classifiers. We implemented and evaluated several algorithms from image based pattern recognition in our autonomous car framework, using HOG-, LBP-, and Haar-like features. We will present experimental results using real traffic data with focus on classification accuracy and execution times.