With the development of intelligent driving technology, recognition the vehicle in front of our cars became the hotspot in the field of intelligent driving research. This paper presents a self-adaptive front vehicle recognition algorithm with some unique improved method on the basis of analyzing and comparing the popular vehicle detection algorithm of domestic and foreign. Using the gray feature, vehicle shadow feature, taillights feature, license plate color domain feature and other features, the recognition algorithm can detect the vehicle in front of cars effectively, find out the safe passage area and avoid the potential risks. Finally, the feasibility of the algorithm is verified by experiment results with MATLAB tools.

Pedestrian detection has a broad application prospect in automotive assisting driving system, but the real time performance is very poor in most common used detection methods. This paper presents a fast algorithm to realize the real-time pedestrian detection. The Local Binary Patterns (LBP) is used to describe the local texture information with the feature of less calculation, the HOG classifier to extract a typical feature of pedestrian’s edge, and then SVM to train and classify on the databases of INRIA and MIT. While scanning the images, interest regions are extracted to speed up the detection. Series of experiment results shows that the proposed pedestrian detecting strategy is effective and efficient.