Patent ID: 11887009
Assignee: INSPUR SUZHOU INTELLIGENT TECHNOLOGY CO., LTD.
Field: Control (Instruments)
Classification: CPC G  B | IPC B  G

Claim 0:
1. A method for automatic driving control, comprising:
initializing a system parameter of a deep-reinforcement-learning automatic driving decision system, wherein the deep-reinforcement-learning automatic driving decision system comprises a noiseless strategic network and a noisy strategic network;
obtaining vehicle traffic environmental state information;
inputting the vehicle traffic environmental state information into the noiseless strategic network and the noisy strategic network to perform automatic driving strategy generation, to obtain a noiseless strategy and a noisy strategy;
adjusting a noise parameter injected into the noisy strategic network within a disturbance threshold according to the noisy strategy and the noiseless strategy, wherein adjusting the noise parameter injected into the noisy strategic network within the disturbance threshold according to the noisy strategy and the noiseless strategy comprises: calculating strategy difference between the noisy strategy and the noiseless strategy; determining whether the strategy difference exceeds a disturbance threshold; taking a quotient of the strategy difference and a modulation factor as the noise parameter when the strategy difference exceeds the disturbance threshold; and taking a product of the strategy difference and the modulation factor as the noise parameter when the strategy difference does not exceed the disturbance threshold; wherein the modulation factor is greater than 1;
performing parameter optimization on a system parameter of the noisy strategic network according to the noise parameter to generate an optimized noisy strategic network; and
performing automatic driving control according to a driving strategy generated by the optimized noisy strategy network;
wherein performing parameter optimization on the system parameter of the noisy strategic network according to the noise parameter comprises:
performing parameter optimization on a system parameter of the noiseless strategic network according to the noisy strategy, and taking a system parameter of the optimized noiseless strategic network as an original parameter; and
taking a sum of the original parameter and the noise parameter as an optimized system parameter of the noisy strategic network;
wherein the system parameter of the deep-reinforcement-learning automatic driving decision system comprises an initial strategy parameter with no noise, an initial strategy parameter with implicit noise, an initial network parameter and initial strategy parameter noise.