Since the traditional preceding vehicle detection strategies cannot meet the accuracy and real-time requirements simultaneously, we propose an approach which combines AdaBoost, an integrated learning method, with the constraint of positions information. Firstly, regions proposal (RP) are obtained by edge boxes method according to the sequence information of vehicle edges. Secondly, the position information of vehicles in the  frame coordinate system is used to filter out nontarget RPs. Finally, the obtained windows are clustered and fed into the AdaBoost classifiers for vehicles detection, and at the same time borders regression is utilized to improve the accuracy of detection results. Experimental results demonstrate that the proposed method has robustness to different detection scenarios and that it can meet the accuracy and real-time requirements of vehicle detection.