Road-side sensing is indispensable for a cooperative vehicle infrastructure system, through which vehicles could have sensing ability beyond the visual range by receiving road information via V2X communication. For the optimal sensing results in reality, RSU configuration needs to vary according to scenarios, which is both time consuming and labor intensive. Meanwhile, recognition of traffic participants based on machine learning is crucial to road-side sensing, requiring a huge amount of labeled data, and it is proven to be an inefficient way to label manually. However, these two problems can be solved by building a simulation system of road-side sensing. Experiment I shows the vehicle occlusion on extreme occasions by adjusting the height and orientation of lidar in the simulation system, which provides a recommended height for installment in reality. Experiment II proves the virtual data derived from the simulation system can be complementary to real data by mutual verification.