Abstract: The present disclosure provides a hybrid decision-making method for autonomous driving, including the following steps: acquiring real-time traffic environment information of an autonomous vehicle during the running at a current moment; establishing a local decision-making model for autonomous driving based on the traffic environment information; based on the local decision-making model for autonomous driving, learning, by using a method based on deep reinforcement learning, a driving behavior of the autonomous vehicle, and extracting driving rules; sharing the driving rules; augmenting an existing expert system knowledge base; and determining whether there is an emergency: if yes, making a decision by using a machine learning model; and if not, adjusting the machine learning model based on the augmented existing expert system knowledge base, and making a decision by the machine learning model.

Abstract: The present disclosure discloses a method for sharing models among autonomous vehicles based on a blockchain, the method comprising the steps of: 1) creating a mobile edge computing network; 2) generating a key pair for each node in the mobile edge computing network; 3) creating a local model set of a mobile node set in the mobile node computing network; 4) enabling each mobile node to communicate with a corresponding nearest mobile edge computing node; 5) creating supernode sequences by the mobile edge computing node; 6) creating a blockchain based on the supernode sequences; and 7) updating the local model set.

Abstract: The present disclosure discloses a method for sharing models among autonomous vehicles based on a blockchain, the method comprising the steps of: 1) creating a mobile edge computing network; 2) generating a key pair for each node in the mobile edge computing network; 3) creating a local model set of a mobile node set in the mobile node computing network; 4) enabling each mobile node to communicate with a corresponding nearest mobile edge computing node; 5) creating supernode sequences by the mobile edge computing node; 6) creating a blockchain based on the supernode sequences; and 7) updating the local model set.