Man-machine cooperative dynamic obstacle avoidance method and system based on deep reinforcement learning

Abstract

The invention provides an intelligent vehicle dynamic obstacle avoidance method and an intelligent vehicle dynamic obstacle avoidance system based on deep reinforcement learning. The intelligent vehicle dynamic obstacle avoidance method comprises the steps of: S1, acquiring an image of an intelligent vehicle at a moment t; S2, inputting the image into a neural network model, and outputting a probability corresponding to execution of each action by the intelligent vehicle; S3, selecting an executed action of the intelligent vehicle at the moment t; S4, recording simulation data of the intelligent vehicle at the moment t; S5, setting t to be equal to t+1, repeating the S1-S4 until the simulation ends, and archiving the simulation data; S6, and transferring simulation data from a positive sample experience pool or a negative sample experience pool for training the neural network model while circulating the S1-S6 continuously until a dynamic obstacle avoidance strategy of the intelligent vehicle can perform dynamic obstacle avoidance completely in the simulation process. The trained dynamic obstacle avoidance strategy is applied to the dynamic obstacle avoidance under a man-machine cooperation mechanism, so as to complement the advantages of a human driver and an intelligent machine on behavior decisions of emergency obstacle avoidance of the intelligent vehicle, thereby achievinga unified and superior decision-making method. The man-machine cooperative dynamic obstacle avoidance method is applied to the field of intelligent decision making of intelligent vehicles.

Description

technical field [0001] The invention relates to the field of environment perception of intelligent vehicles, in particular to a dynamic obstacle avoidance method for intelligent vehicles based on a deep reinforcement learning method under a human-computer cooperation mechanism. Background technique [0002] In research in the field of autonomous driving, safety is the primary consideration, and research in all aspects is meaningful only when safety requirements are met. In order to achieve safe autonomous driving, smart cars sense the surrounding environment through sensors and then make decisions and plans, and then control the vehicle to reach the desired destination without traffic accidents. In recent years, in order to improve the safety of smart cars, some safety systems have been proposed, such as collision avoidance systems, pedestrian detection systems and forward obstacle warning systems. [0003] In a complex traffic system, the most important and key point to re...

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Problems solved by technology

[0004] Aiming at the problems that the intelligent vehicle dynamic obstacle avoidance method in the prior art is difficult to adapt to the complex and different scenes on the real road, the purpose of the present invention is to provide a intelligent vehicle dynamic obstacle avoidance method based on the deep reinforcement learning method under the human-computer cooperation mechanism And the system, effectively avoiding the shortco

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