Automatic-driving intelligent vehicle trajectory tracking control strategy based on deep reinforcement learning

Abstract

The invention discloses an automatic-driving intelligent vehicle trajectory tracking control strategy based on deep reinforcement learning. For an intelligent vehicle automatic-driving task, accordingto an action-critics structure in a deterministic policy gradient (DDPG) algorithm, a double-action network is adopted to output a steering wheel angle command and a vehicle speed command. A main reviewer network is designed to guide the updating process of the double-action network, and the stragety specifically comprises the steps of describing an automatic driving task as a Markov decision process: < st,at,Rt, st +1 >; initializing a'double-action 'network in the improved DDPG algorithm by adopting a behavior cloning algorithm; pre-training a'reviewer 'network in the deep reinforcement learning DDPG algorithm; designing a training road containing various driving scenes to carry out reinforcement learning online training; and setting a new road to test the trained deep reinforcement learning (DRL) model. The control strategy is designed by simulating the human driving learning process, and automatic driving of the intelligent vehicle in the simple road environment is achieved.

Description

technical field [0001] The invention belongs to the field of automatic driving of intelligent vehicles, and relates to a trajectory tracking control strategy for automatic driving of intelligent vehicles based on deep reinforcement learning. Background technique [0002] The emergence of self-driving smart cars provides a new solution to the occurrence of traffic accidents, and the design of an accurate trajectory tracking controller is the premise for realizing safe and stable driving of self-driving cars, and it is also the key to realize the intelligentization and commercialization of smart cars. necessary condition. [0003] At present, the trajectory tracking control methods mainly used in the existing technologies including published patents include traditional control methods such as MPC control, feedforward-feedback control, and linear quadratic regulator LQR tracking control. However, the driving environment of the car is characterized by high uncertainty, non-repe...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problems existing in the prior art, the present invention proposes a trajectory tracking control strategy for automatic driving intelligent vehicles based on deep reinforcement learning, with the purpose of solving the uncertainty, non-repeatability, unpredictability and Due to the nonlinearity and uncertainty of the vehicle itself, it is difficult for the current existing technology to guarantee a better control effect

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