Abstract: ‍ ‍As an important vehicle-mounted sensor， millimeter-wave radar has been widely used in the field of autonomous driving. In recent years， with the improvement of automobile intelligence， the generation of high-quality radar point clouds has received great attention. Traditional millimeter-wave radar point cloud imaging has many shortcomings such as too many clutter points and sparse effective point clouds， which limit its development in the field of autonomous driving. Therefore， how to improve the density and quality of millimeter-wave radar point clouds has become a key issue in industry research. In recent years， with the maturity of multiple-input multiple-output （MIMO） technology and control of multi-chip cascaded synchronization technology， the angular resolution of millimeter-wave radar antennas has been greatly improved， which has promoted the application of millimeter-wave radar in point cloud imaging. develop. On this basis， this paper designs a complete set of millimeter-wave radar system-level point cloud imaging algorithms， and uses TI's AWR2243 cascaded radar development kit to collect data from the actual scene， generating a relatively compact and reliable millimeter-wave radar. The three-dimensional point cloud image basically realizes the effective restoration of the side scene of the vehicle platform.