Automatic Parking Trajectory Planning Based on Recurrent Neural Network

Relying on a company's automatic parking project, This paper proposes a method which use Tensor Flow training Recur rent Neural Network (RNN) to predict parking trajectory curves and determine the optimal parking trajectory. First, we established a parallel parking model with obstacles and gave the starting point ofthe par king and the range of obstacles. Then use the arctangent function to discretize the model parking trajectory, and introduce the discretized data as initial sample data into Tensorfi low for Recurrent Neural Network (RNN) training to generate diseretized parking trajectory curve data. Finally, the data will be summed into the actual parking trajectory curve and compared with the experienced drivel' parking trajectory curve. It is found that the parking trajectory curve which obtained by the method proposed in this paper is highly integrated with the rich drivel' parking trajectory curve, and even exceeded in the obstacle avoidance function.