In this paper, we propose a new disparity-based obstacle detection method for intelligent vehicle. The disparity maps captured by the stereo vision system ZED is utilized to detect obstacles in outdoor environment. This method consists of several steps namely, histogram calculation based on the disparity map, peaks finding using Non-Maximum Suppression (NMS), disparity slicing and obstacle detection using contours. The first step is to obtain the distribution of the pixels in the disparity map. The second step is performed to get the peaks in the histogram after the binary processing on the disparity map. However, there are always many noise objects in the binary image. The real obstacle area needs to be separated from the noise obstacles. Thus, the third step is performed to filter the binary image based on a statistics by column and then segment the detected obstacle area. The last step is to calculate the obstacle including its size, distance and position on the disparity map. A series of experiments prove that the proposed method is accurate at detecting obstacles and calculating the required information based on stereovision techniques.