Abstract The present invention relates to an automatic driving method with multi-agent and multi-scenario data set training, comprising the following steps: Si: setting up a number of agents within a terminal control range; S2: collecting data sets in realistic scenarios using the agents; S3: performing deep learning processing on the data sets collected in the step S2, to obtain realistic scenarios and actions taken by the agents in the realistic scenarios; S4: saving the realistic scenarios and the actions taken by the agents in the realistic scenarios to a terminal according to the results of the processing in the step S3; and S5: collecting, by the agents, real-time operation scenarios, and converting the real-time operation scenarios into data and sending the data to the terminal; and comparing, by the terminal, the real-time operation scenarios with the realistic scenarios saved in the terminal, wherein after a preset similarity value is reached, the terminal selects an optimal action measure to guide the agents in path planning and obstacle avoidance. Through the present invention, the terminal can process these collected data more rapidly and efficiently. Drawings of the Description Si: Setting up a number of agents within a terminal control range S2: Collecting data sets in realistic scenarios using the agents S3: Performing deep learning processing on the data sets collected in the step S2, to obtain realistic scenarios and actions taken by the agents in the realistic scenarios S4: Saving the realistic scenarios and the actions taken by the agents in the realistic scenarios to a terminal according to the results of the processing in the step S3 S5: Collecting, by the agents, real-time operation scenarios, and converting the real-time operation scenarios into data and sending the data to the terminal; and comparing, by the terminal, the real-time operation scenarios with the realistic scenarios saved in the terminal, wherein after a preset similarity value is reached, the terminal selects an optimal action measure to guide the agents in path planning and obstacle avoidance Fig. 1
Automatic Driving Method With Multi-Agent And Multi Scenario Data Set Training