Automatic intersection passing decision method based on convolutional neural network

A technology of convolutional neural network and decision-making method, which is applied in the field of automatic intersection traffic decision-making, can solve the problems of increased calculation time, difficult practical application, and poor effect, so as to reduce calculation time, improve average traffic speed, and improve management performance. Effect

Active Publication Date: 2021-11-23
SOUTHEAST UNIV
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Problems solved by technology

The former follows the first-come-first-serve (FCFS) rule, which does not work well in practice
The latter traverses all possible traffic schemes, and conducts simulatio

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  • Automatic intersection passing decision method based on convolutional neural network
  • Automatic intersection passing decision method based on convolutional neural network
  • Automatic intersection passing decision method based on convolutional neural network

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Embodiment Construction

[0053] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0054] The present invention designs an automatic intersection of the convolutional neural network, such as figure 1 As shown, the steps are as follows:

[0055] 1. Establish a self-automatic intersection model, using V2I communication technology to make information interaction with intelligent network vehicles within the communication range;

[0056] 2. Determine all feasible passwords based on planning trees;

[0057] 3. Collect the vehicle track data, and use the planned tree to raise all candidates, traverse each order and obtain the time consumption corresponding to each order, establish a tab for describing the traffic state and the traffic sequence, and build a tensor-time consumption database;

[0058] 4, build and train convolutional neural networks, and obtain the short-term time consumption of the tensor of the tensor as the final gener...

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Abstract

The invention discloses an automatic intersection passing decision method based on a convolutional neural network. The method sequentially comprises the steps of: building an automatic intersection model; determining all feasible passing sequences based on a plan tree method; collecting vehicle track data, listing all candidate passing sequences by using a plan tree, traversing all the sequences and obtaining time consumption corresponding to each sequence, establishing a tensor for describing a traffic state and a passing sequence, and constructing a tensor-time consumption database; and constructing and training a convolutional neural network, and obtaining a passing sequence corresponding to the tensor with the shortest time through the trained convolutional neural network as a final passing scheme. The method can significantly reduce the calculation time, reduce the driving delay, and improve the passing speed.

Description

Technical field [0001] The present invention belongs to the field of traffic engineering, and in particular, the present invention relates to an automatic intersection. Background technique [0002] With the continuous development of wireless communications and Internet technology, artificial driving vehicles will be replaced by intelligent network vehicles. The car has received attention in the field of intelligent transportation. It is the best means of reducing driving delays in today's internationally recognized driving, improving operational efficiency, achieving energy saving and emission reduction. [0003] In fact, the key factors that truly restrict urban transportation, affect traffic safety, has always been the general order of the intersection. In the past few decades, scholars have proposed some ways to solve automatic intersection management issues, mainly including reservation-based strategies and planned strategies. The former followed the first service (FCFS) rul...

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Application Information

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IPC IPC(8): G08G1/0967G08G1/01G08G1/16H04W4/44G06N3/04G06N3/08
CPCG08G1/096783G08G1/0129G08G1/164H04W4/44G06N3/08G06N3/045
Inventor 张健姜夏李丹张晓亮张平刘子懿熊壮
Owner SOUTHEAST UNIV
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