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Meta-reinforcement learning-based vehicle adaptive autonomous driving decision-making method and system

An autonomous driving and reinforcement learning technology, applied in control/regulation systems, vehicle position/route/altitude control, non-electric variable control, etc., can solve problems such as unsafe decision-making, achieve flexibility, improve user experience, The effect of safe and stable driving process

Active Publication Date: 2022-07-29
NANJING UNIV
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  • Application Information

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

Method used

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  • Meta-reinforcement learning-based vehicle adaptive autonomous driving decision-making method and system
  • Meta-reinforcement learning-based vehicle adaptive autonomous driving decision-making method and system
  • Meta-reinforcement learning-based vehicle adaptive autonomous driving decision-making method and system

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Embodiment Construction

[0053] Below in conjunction with specific embodiments, the present invention will be further illustrated, and it should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. The modifications all fall within the scope defined by the appended claims of this application.

[0054]In the present invention, the selection of driving behavior is based on the task information given by the task coding module and the state information given by the perception module. The task to be completed is to quickly and safely reach another point on the map under a certain vehicle condition. Obviously, if a certain driving scheme can drive reliably under the current road conditions, the scheme will be given a positive reward value; otherwise, it will be given a negative reward value. To maximize cumulative reward, we need to find the optimal mapping from environmental states and task encodings to driving behavior....

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Abstract

The invention discloses a vehicle adaptive automatic driving decision-making method and system based on meta-reinforcement learning. A task coding module is introduced to identify the current vehicle condition information from the vehicle's driving track, and encode it into a vector representation, so that the system is aware of the current vehicle condition. , and adjust its own driving strategy in time when the vehicle conditions change, making the system more robust and safe. In order to achieve a better ride experience, reinforcement learning is used to solve decision-making problems in the field of autonomous driving. The system includes virtual environment module, memory module, perception module, coding module, decision-making module and control module. Strengthen the reliability of the system by adding the most likely rich task models to the virtual environment database; by changing the SAC reinforcement learning algorithm to make decisions based on the task encoding module; by maximizing the mutual information between task encoding and sampled trajectories In this way, the task encoding module can learn a task encoding that contains 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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B60W60/00G05D1/02
CPCB60W60/0011G05D1/0221
Inventor 章宗长俞扬周志华胡亚飞徐峰
Owner NANJING UNIV
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