For the requirement of vehicle real-time precise self-localization,an optical flow-based optimization method of vehicle motion estimation has been proposed. The modified Lucas-Kanade was used to track FAST feature points for calculating their optical flow. The coordinate systems of offsets between images are transformed to obtain the estimated value of vehicle motion in the initial coordinates. Based on the assumption of the offset and rotation angle errors obeying normal distribution,the result of using optical flow method was optimized to obtain the vehicle running trajectory when it was mapped to the world coordinate system. Through testing many different vehicle trajectories,the experiment results demonstrate that the optimization method highlights the advantage of real-time performance of optical flow and overcomes the shortcoming of its poor positioning accuracy,and effectively solve the situation that cumulative error makes vehicle trajectory drifted. So it can provide the accurate real-time positioning result. Meanwhile,it has shorter computing time compared with vehicle positioning based on feature points matching,and the obtained trajectory of the study was more accurate and smoother than common optical flow method.