  Autonomous vehicles need to travel over 11 billion miles to ensure their
safety. Therefore, the importance of simulation testing before real-world
testing is self-evident. In recent years, the release of 3D simulators for
autonomous driving, represented by Carla and CarSim, marks the transition of
autonomous driving simulation testing environments from simple 2D overhead
views to complex 3D models. During simulation testing, experimenters need to
build static scenes and dynamic traffic flows, pedestrian flows, and other
experimental elements to construct experimental scenarios. When building static
scenes in 3D simulators, experimenters often need to manually construct 3D
models, set parameters and attributes, which is time-consuming and
labor-intensive. This thesis proposes an automated program generation
framework. Based on deep reinforcement learning, this framework can generate
different 2D ground script codes, on which 3D model files and map model files
are built. The generated 3D ground scenes are displayed in the Carla simulator,
where experimenters can use this scene for navigation algorithm simulation
testing.
