Intelligent vehicle speed decision-making method based on deep reinforcement learning and simulation method thereof

Abstract

The invention discloses an intelligent vehicle speed decision-making method based on a deep reinforcement learning method. The method comprises the steps of constructing a state space S, an action space A and an instant rewarding space R of a Markov decision-making model of an intelligent vehicle passing through an intersection; initializing a neural network, and constructing an experience pool; performing action selection by adopting an epsilon-greed algorithm, and filling the experience into the experience pool constructed in the step 2; randomly selecting a part of experience from the experience pool, and training a neural network by adopting a stochastic gradient descent method; completing the speed decision of the intelligent vehicle at the current moment according to the latest neural network, adding the experience to an experience pool, randomly selecting a part of experience, and carrying out the training of a new round of neural network. The invention further discloses a simulation method of the intelligent vehicle speed decision-making method based on deep reinforcement learning. The method is advantaged in that simulation experiments are carried out based on a deep reinforcement learning simulation system established by a matlab automatic driving toolbox.

Description

technical field [0001] The invention relates to the technical field of deep reinforcement learning and the technical field of automatic driving, in particular to a speed decision method for a smart car based on deep reinforcement learning and Matlab. Background technique [0002] With the development of society and the continuous improvement of the level of science and technology, people put forward new requirements for cars. Many automakers and scientific research institutions are shifting their research focus from "traditional cars" to "smart cars that integrate people, vehicles, and roads." Various countries have also introduced regulations and policies to promote the development of smart cars. [0003] Autonomous driving technology has become the core technology and research hotspot for the future development of smart cars, and decision-making control, as one of the four branches of autonomous driving technology, occupies an important position. Existing intelligent vehi...

AI Technical Summary

Problems solved by technology

Existing intelligent vehicle decision-making methods have more or less defects. For example, the rule-based method is mostly suitable for simple scenarios, and it is difficult to be competent for the urban road environment with rich structural features; the decision tree method based on the polling mechanism requires Decision network is defined offline for each driving scenario; information / ontology-based decision-making inference methods rely on vehicle-to-vehicle (V2V) communication and require full knowledge of other vehicles’ information (including driving intentions)

[0004] At the same time, the above methods also have the common problem of ignoring the dynamics and uncertainty of the environment. However, the real traffic environment often has strong uncertainties. The relationship between vehicles and vehicles, vehicles and roads is intricate and changing in real time. It is difficult to achieve safe and efficient decision-making, affecting the intelligent driving of vehicles, and even causing traffic accidents

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