The development of deep learning technology has driven the rapid progress of autonomous driving. While the accuracy of perception models based on deep learning is gradually improved, hidden dangers related to robustness and reliability still exist. Therefore, tests should be conducted thoroughly under various scenes to ensure acceptable security levels. Scene-based simulation testing is crucial in autonomous driving technology. One key challenge is to describe and generate diversified simulation testing scenes. Scenario description languages can describe autonomous driving scenes and instantiate the scenes in virtual environments to obtain simulation data. However, most existing scenario description languages cannot provide high-level abstractions and descriptions of the road structure of the scene. This study presents a road network property graph to represent the abstracted entities and their relations within a road network. It also introduces SceneRoad, a language specifically designed to provide concise and expressive descriptions of the road structure in a scene. SceneRoad can build a road network feature query graph based on the described road structure features of a scene. Thus, the problem of searching the road structures in the road network is abstracted as a subgraph matching one on the property graph, which can be solved by the VF2 algorithm. Additionally, SceneRoad is incorporated as an extension into the Scenic scenario description language. With this extended language, a diverse set of static scenes are employed to build a simulation dataset. Statistical analysis of the dataset indicates the wide variety of scenes that have been generated. The results of training and testing various perception models on both real and simulated datasets show that the model’s performance on the two datasets is positively correlated, which shows that the model’s evaluation on the simulated dataset aligns with its performance in real scenes. This is significant for perception model evaluation and research on improving the model’s robustness and safety.