Aiming at the low rate of front vehicle recognition in the advanced driver assistance system (ADAS), the haar-like features in the road ahead image are studied based on the principle of machine vision, and the integral graph calculation is performed, the adaptive boosting (AdaBoost) algorithm is used to train the positive and negative samples and cascade to obtain the trained model on the basis of extracting the haar-like features, then detect and identify the vehicle in front of the vehicle. Finally, the algorithm is implemented and tested in the visual studio development environment based on open source computer vision library, the results show that the recognition time of each frame of video image is less than 40 milliseconds, the detection rate is accurate and reliable, and it can meet the real-time identification of front vehicle in multiple scenes and multiple working conditions.