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Some attempts on driving simulators have achieved good results(eg. DI-drive, DI-drive is an open-source application platform under OpenDILab. DI-drive applies different simulator/datasets/cases in Decision Intelligence Training & Testing for Autonomous Driving Policy). The basic idea mainly includes initializing with imitation learning, and then using reinforcement learning to obtain results that surpass expert data after reaching a certain performance. Some use the perceptual Label to train the backbone of the network, then freeze the backbone, and use reinforcement learning to specifically train the affordance method from perceptual embedding to action output. Others use a multi-model fusion approach, in which the model trained by reinforcement learning is used together with other methods to obtain the driving output. However, the emulator-based method is mainly end-to-end, and its security is difficult to guarantee, and it is difficult to apply to real vehicle scenarios.