An Intelligent Parking Method Based on Model Reinforcement Learning

Abstract

The invention relates to an intelligent parking method based on model reinforcement learning, using Monte Carlo tree search, vehicle kinematics model, action classification network and state value fitting network, specifically comprising the following steps: S1. Monte Carlo tree search and combination The action classification network and the vehicle kinematics model obtain the parking data pre-training model; S2. Train the state value fitting network according to the parking data generated by the parking pre-training model; S3. The state value fitting network and Monte Carlo The combination of tree search and action classification network constitutes an online driving strategy model; S4. The parking online driving strategy model receives storage location and vehicle movement information in a rolling time domain manner, generates a control command at each time interval, and sends it to The vehicle motion control module is used to control the target vehicle to complete parking. Compared with the prior art, the present invention has the advantages of better final parking heading angle and success rate, reducing the influence of the accuracy of the vehicle model on the final parking effect, and the like.

Description

technical field [0001] The invention relates to the technical field of automatic parking, in particular to an intelligent parking method based on model reinforcement learning. Background technique [0002] Automated Parking Systems (APS) are important intelligent driver assistance systems because they have great potential to reduce accidents in cramped cities and increase the utilization of parking spaces. For all APS platforms, the smart vehicle must have a parking space that detects the location and generates its movement by on-board sensor systems, such as around view cameras (AVM) and LiDAR (LIDAR). The conventional motion planning method of APS is the path velocity decomposition method, which decomposes the parking task into a kinematics sub-problem and a dynamics sub-problem, which are solved by path planning and path tracking methods respectively, but cannot flexibly process real-time perception information, and cannot use historical parking Vehicle data to improve i...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a smart parking method based on model reinforcement learning in order to overcome the defects of the above-mentioned prior art that are overly dependent on the accuracy of the vehicle model, the information processing is not flexible enough, and the existing data cannot be effectively used

Images