In order to solve the problem of autonomous vehicle decision and control at an unsignalized intersection, this paper studies the confluence condition of two vehicles at a two-way single-lane intersection. The method of reinforcement learning is used for the decision and control of autonomous vehicle to establish the mapping relationship between vehicle state space and action space. Aiming at the problem that the vehicle speed setting is too simple in the present study, the data collected in the actual scene was used as the trajectory information of the environmental vehicle to construct the scene model. Based on the autoregressive moving average model, the vehicle speed of environmental vehicle is predicted. Combining with the autonomous vehicle and the predicted environmental vehicle speed timing information, the forward decision-making model is established to calculate the reference speed, and the reference speed is introduced to construct the reinforcement learning reward function to accelerate the training convergence speed. The results show that the enhanced learning model has a fast convergence speed, and the trained agent can safely pass through the intersection when gambling with environmental vehicles with different driving styles, this paper provides a reference for autonomous vehicle safety decision-making and control at no-signal intersection.