Parameterized learning decision control system suitable for lane changing and lane keeping and method

Abstract

The invention belongs to the technical field of automobile advanced auxiliary driving and unmanned driving system design and relates to a parameterized learning decision control system suitable for lane changing and lane keeping behaviors and a method. The parameterized learning decision control system suitable for the lane changing and lane keeping behaviors of the invention is designed based ona parameterized decision framework; the system comprises a learning decision-making method and a track planning controller, wherein the learning decision-making method is designed under the lane changing and lane keeping scene of a vehicle on the basis of a reinforcement learning algorithm, and the track planning controller can be correspondingly parameterized under the scene so as to adapt to straight road and bend road planning. The system is suitable for a high-level automatic driving vehicle; the adaptability of the system to the different driving behavior characteristics of different drivers is effectively improved through online learning; and therefore, the system obtains better driving performance with safety guaranteed.

Description

technical field [0001] The invention belongs to the technical field of automotive advanced assisted driving and unmanned driving system design, and specifically relates to a parametric learning decision-making control system and method suitable for lane changing and lane keeping behaviors. Background technique [0002] With the continuous development of intelligent driving assistance technology and unmanned driving technology, different forms of motion control systems have been continuously proposed and applied. For example, in the problem of motion trajectory planning and control, in order to make the system more functional and adaptable to various scenarios, under the vehicle layered control framework, the integrated underlying motion controller needs to be able to perform multiple driving tasks and Scenarios such as lane changing, lane keeping, etc. At the same time, various executive subsystems, such as driving, braking, and steering systems, have the ability to coordin...

AI Technical Summary

Problems solved by technology

Furthermore, for the solution of different decision parameters, it is necessary to adapt to the changing driving conditions, and continuously adapt to the behavior and feedback behavior of real human drivers in real driving scenarios. It is difficult to achieve continuous improvement by using model-based control methods. learning effect

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