In order to improve the efficiency of the preceding vehicle identification, a preceding vehicle identification algorithm combined Haarlike features and Adaboost algorithm was proposed. Based on massive amounts of offline training sample set, effective vehicle contour and texture characteristics was extracted, Haarlike characteristics was used to describe the goal. Adaboost machine learning algorithms was used to trained classifier, the sample characteristics of cascade classifier was built, and the test object was used to detect the vehicle existence. The experimental result shows that the algorithm based on Haarlike features and Adaboost algorithm in the paper has a high detection rate of above 91%, and average detection speed of 28 ms, it can well adapt to the uncertain factors such as environmental interference and vehicle type, improve the robustness of the preceding vehicle detection, also meet the requirement of the safe driving in the longitudinal dimensionality.