Focusing on the demand of intelligent driving under non-visual conditions，the millimeter-wave radar with the characteristics that can work all day and is less affected by light and weather is used to build a shape and position feature recognition model of crossable obstacles on the road in this paper. Taking the road speed bump as an example，the road obstacle feature perception system based on millimeter wave radar is constructed. The radar antenna plane faces the ground and has a certain angle with the ground to collect road information. The FFT-CZT two-stage processing structure is used to refine the spectrum of radar intermediate frequency data and to obtain the range value with high accuracy. Then，by analyzing the radar point cloud，the shortest target distance measured in each frame is fused to obtain the two-dimensional imaging of the road deceleration zone. Finally，through the analysis of visual data，the geometric model of road deceleration zone is established，and the calculation method of characteristic parameters of deceleration zone is put forward. A real vehicle-testing platform is established to collect data of different angles between millimeter wave radar and the ground from 0 to 90. The average absolute error of the estimated speed bump height at the included angle of 45 is within 4 mm，and the average absolute error of the estimated width is about 21 mm，which verifies the effectiveness of the method proposed in this paper.