Vehicle adaptive automatic driving decision-making method and system based on meta reinforcement learning

Abstract

The invention discloses a vehicle adaptive automatic driving decision-making method and system based on meta reinforcement learning, and the method comprises the steps: introducing a task coding module, recognizing current vehicle condition information from a vehicle driving track, coding the current vehicle condition information into vector representation, enabling the system to achieve the current vehicle condition, and timely adjusting the own driving strategy when the vehicle condition changes, thereby improving the driving efficiency, and the system is more robust and safer. In order to achieve better riding experience, reinforcement learning is used to solve the decision problem in the field of automatic driving. The system comprises a virtual environment module, a memory module, a sensing module, a coding module, a decision module and a control module. The reliability of the system is enhanced by adding task models which are extremely likely to be abundant to the virtual environment database; by changing the SAC reinforcement learning algorithm, the SAC reinforcement learning algorithm can make a decision based on the task coding module; and through a mode of maximizing mutual information between a task code and a sampling track, a task coding module can learn a task code containing rich information.

Description

technical field [0001] The invention relates to a vehicle self-adaptive automatic driving decision-making method and system based on meta-intensive learning, which is suitable for various types of vehicles with different specifications and does not need to re-learn a new automatic driving system for each vehicle. in the field of autonomous driving technology. Background technique [0002] Autonomous driving mainly needs to solve three core problems: state perception, path planning and the choice of driving behavior. At present, the problem of how to determine the state of the car, that is, "where am I", can be solved by using a variety of sensors; how to determine the overall path to the destination, that is, "how to get there", can use the current mainstream Dijkstra, A *, dynamic programming and other algorithms to solve. However, how to choose the most appropriate driving behavior according to the current state of the car, such as how to decide whether to pass at a cons...

AI Technical Summary

Problems solved by technology

Taking automatic driving as an example, an automatic driving system that can run perfectly on a car, transplanted to a van may cause the system to make unsafe decisions due to changes in vehicle length, width and height

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