An Automatic Parking Model Based on Deep Reinforcement Learning

Abstract

Abstract When parking a car, it is crucial to ensure the car constantly approaches the parking point, gets an excellent heading angle, and avoids significant losses caused by line pressure. An automatic parking model based on deep reinforcement learning is proposed. A parking kinematics model is built to calculate the different states of its movement. Steering angle and displacement are used to achieve interaction as actions; A comprehensive reward function is designed to consider the focus of action and safety in different parking stages. Through training, the car’s automatic parking is realized, and a comprehensive analysis of the various stages and situations in the parking process is given. Besides, it is showed by a further generalization experiment that the model has good generalization.