Abstract: Autopilot is a hot research direction,and traffic congestion is a perennial social problem in China.Inthe future,traffic congestion is likely to occur on the road where self-driving vehicle and artificial driving vehicle coexist.On the basis of existing theories,this paper considered a variety of factors that may affect autopilot,including different speeds and neural networks.In order to improve the overall traffic efficiency,on the premise of maintaining safety,all self-driving vehicles should be as fast as possible to improve road efficiency and fundamentally solve traffic congestion.The feature extraction problem of this special case is different from the feature extraction of the image data,so the method of representing the road in the two-dimensional plane is used to process the three-dimensional data formed by the stacking of two-dimensional informationand hybrid neural network.The spatial and temporal features are extracted by using the depth neural network,so that the vehicle can make a better response.Finally,the system design is conbining with reinforcement learning so that it can be trained,and thus the effect of neural networks can be tested.

Key words: Autonomous vehicle, Convolutional neural networks, Deep learning, Recurrent neural networks, Reinforcement learning