An automatic driving decision-making method, system, device and computer storage medium

Abstract

The present application discloses an automatic driving method, system, device and computer medium, which acquire real-time traffic environment information of an automatic driving vehicle during driving at the current moment; map the real-time traffic environment information based on a preset mapping relationship to obtain Map the traffic environment information; adjust the target deep reinforcement learning model based on the pre-stored existing deep reinforcement learning model and the mapped traffic environment information; judge whether to end the automatic driving, if not, return to execute to obtain the current moment, the automatic driving vehicle Steps for real-time traffic environment information during driving. In this application, the target deep reinforcement learning model can be adjusted with the help of the mapping relationship and the existing deep reinforcement learning model, which can avoid adjusting the target depth reinforcement learning model from scratch, and speed up the decision-making efficiency of the target depth reinforcement learning model. Fast and stable autopilot.

Description

Technical field [0001] The present application relates to the field of automatic driving technology, and more particularly to an automatic driving decision method, system, device, and computer storage medium. Background technique [0002] In modern urban transportation, the number of motor vehicles is increasing, and the road congestion is serious, and traffic accidents are frequent. In order to minimize the harm caused by human factors, people turn their attention to the automatic driving area. In-depth learning depth strengthening learning (DRL, Deep Reinforcement Learning) is a type of machine learning method, intelligent-environment interaction and sequence decision mechanism, which have been developed in recent years, and is also known as achieving "universal" Key Steps for Artificial Intelligence (AGI, Artificial General Intelligence "is applied to the automatic driving decision process. [0003] Although deep strengthening learning can guide the vehicle from the beginning ...

AI Technical Summary

Problems solved by technology

However, in the process of learning autonomous driving from scratch, the vehicle usually needs several steps or even dozens of steps of training to make a better decision, and the sampling efficiency is low, which is contrary to the instantaneous decision-making requirements of the autonomous driving scene.

At the same time, the step of choosing a bad action will lead to a large variance, which is reflected in the vehicle's unsteady driving, and even accidents such as running out of the lane and colliding.

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