Autonomous driving technology has five levels, from L0 to L5. Currently, only the L2 level (partial automation) can be achieved, and there is a long way to go before reaching the final level of L5 (full automation). The key to crossing these levels lies in training the autonomous driving model. However, relying solely on real-world road data to train the model is far from enough and consumes a great deal of resources. Although there are already examples of training autonomous driving models through simulators that simulate real-world scenarios, these scenarios require complete manual construction. Directly converting 3D scenes from road network formats will lack a large amount of detail and cannot be used as training sets. Underground parking garage static scenario simulation is regarded as a procedural content generation (PCG) problem. This paper will use the Sarsa algorithm to solve procedural content generation on underground garage structures.

翻译：自动驾驶技术分为L0至L5五个等级，目前仅能实现L2级（部分自动化），距离最终L5级（完全自动化）仍任重道远。跨越这些等级的关键在于训练自动驾驶模型。然而，仅依赖真实道路数据训练模型远远不够，且会消耗大量资源。尽管已有通过模拟真实场景的仿真器训练自动驾驶模型的案例，但这些场景需要完全人工构建。直接从路网格式转换三维场景会缺失大量细节，无法用作训练集。地下停车场静态场景模拟被视为过程化内容生成（PCG）问题。本文采用Sarsa算法解决地下车库结构的过程化内容生成问题。