Abstract: This paper aims to provide millimeter-wave sensing channel testing technology for autonomous driving scenarios. It employs a real data acquisition, simulation configuration initialization, real measurement simulation calibration and algorithm validation approach to conduct cross-validation and analysis of millimeter-wave sensing channel simulation based on real scenarios and real-world signal measurements. Firstly, the paper validates the effectiveness of the 77 GHz millimeter-wave radar measurement system and the collected sensing signal data using a corner reflector with a fixed radar cross section (RCS). Based on this experimental platform, appropriate scenarios are selected and system parameters are set for road measurement experiments in a vehicular network scenario. By analyzing the acquired echo data and extracting key features, typical structures and corresponding echo signals in the scene are matched, and the target object state is extracted to initialize the ray tracing (RT) simulation experiment configuration. Furthermore, the paper calibrates the simulation results by comparing them with the measurement experiment results based on the matching of key target object states in the scene with radar echo data. Lastly, the paper designs a low-complexity filtering algorithm and verifies it using simulated channels, realizing a closed-loop verification and analysis of the “measurement-simulation-calibration-verification” autonomous driving testing method. This achieves closed-loop verification and analysis of millimeter-wave sensing signal real measurement and simulation methods through “measurement-simulation-calibration-verification”. The cross-validation method proposed in this paper is cost-effective, efficient, and highly scalable. Based on the validated simulation platform, a simulation system suitable for the target scenario can be established, generating accurate and efficient sensing channel echo data. This provides a basis for the design and evaluation of subsequent sensing signal algorithms.