Dynamic speed limit control method and system based on intelligent network connection vehicle, terminal and readable storage medium

A dynamic speed limit, intelligent network technology, applied in the direction of road vehicle traffic control system, traffic control system, neural learning method, etc., to achieve the effect of ensuring reliability, reducing the probability of occurrence, fast and efficient

Active Publication Date: 2021-07-09
CENT SOUTH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing control methods pose great challenges to the construction of roadside speed limit facilities and the collection of traffic flow information

Method used

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  • Dynamic speed limit control method and system based on intelligent network connection vehicle, terminal and readable storage medium
  • Dynamic speed limit control method and system based on intelligent network connection vehicle, terminal and readable storage medium
  • Dynamic speed limit control method and system based on intelligent network connection vehicle, terminal and readable storage medium

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0065] This embodiment provides a dynamic speed limit control method based on intelligent networked vehicles, which uses a dynamic speed limit control model to construct a sample, and then trains a BP neural network to construct a neural network model for predicting the deceleration parameter sequence. The specific process is as follows :

[0066] Step 1: Obtain the initial vehicle speed V of the leading vehicle initial , bottleneck speed V bottleneck , the fleet length L, and the distance between the fleet and the bottleneck section d are used as the state parameters of the fleet, which are input to the traffic simulation platform, and the genetic algorithm is used to solve the corresponding dynamic speed limit control model to obtain the deceleration parameter sequence.

[0067] Among them, the dynamic speed limit control model in different scenarios is simulated, and the deceleration parameter sequence is obtained by repeating the calculation in step 1. Therefore, each gr...

Embodiment 2

[0080] The difference between this embodiment and Embodiment 1 is that in this embodiment, the dynamic speed limit control model is directly used to solve the deceleration parameters of the dynamic speed limit control strategy, and then the deceleration parameters of the dynamic speed limit control strategy at the same time are compared with those in the vehicle. The deceleration parameter a of the embedded car-following control algorithm cav Make a comparison and choose the better solution a(t) to execute. The specific implementation process is as follows:

[0081] Step 1-1: Monitor in real time whether there is a bottleneck section on the current road section.

[0082] Step 1-2: If there is a bottleneck section, start the dynamic speed limit control. Divide the total duration of speed limit control into M cycles, the duration of a single cycle is preset as 30s, T is the sequence number of the cycle, T=1,2,...,M; take any dynamic speed limit control fleet within the communi...

Embodiment 3

[0088] This embodiment provides a system based on the above dynamic speed limit control method, including a monitoring module, a vehicle fleet state parameter acquisition module and a control module.

[0089] Among them, the monitoring module: used to monitor whether there is a bottleneck section in the road section; the fleet status parameter acquisition module, used to obtain the fleet status parameters of the fleet; the control module, used to control the deceleration parameters of the head car in the fleet based on the dynamic speed limit control model .

[0090] In some implementations, the control module further includes: a neural network model building module, a prediction module, and a selection module.

[0091] Wherein, the neural network model building module is used to obtain the neural network model built based on the dynamic speed limit control model, and the specific implementation process can refer to step 2; the prediction module is used to input the fleet stat...

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Abstract

The invention discloses a dynamic speed limit control method and system based on an intelligent network connection vehicle, a terminal and a readable storage medium, and the method comprises the steps: through the close connection between an intelligent vehicle-mounted information terminal and an information network, in a scene that a traffic bottleneck exists at the downstream of a road section, intelligent network connection vehicles are used for leading manual driving vehicles to form a motorcade for dynamic speed limit control, so that the safety of highway speed limit control is improved, the cost of speed limit control is reduced, the road traffic accident rate is reduced, and the highway traffic safety and efficiency are improved. Deceleration parameters of a head vehicle in a motorcade are controlled based on a dynamic speed limit control model, and a neural network model is further constructed by using the dynamic speed limit control model and is applied to a vehicle speed control process. The method can overcome the defects of the existing expressway dynamic speed limit control, effectively improves the traffic safety of the expressway, and is suitable for expressway traffic safety management and control under the condition that future network connection vehicles and non-network connection vehicles are mixed.

Description

technical field [0001] The invention belongs to the technical field of intelligent traffic management and control, and in particular relates to a dynamic speed limit control method, system, terminal and readable storage medium based on intelligent networked vehicles. Background technique [0002] Driven by the rapid economic development of our country and the gradual improvement of automobile regulation, the number of motor vehicles in our country is increasing continuously. According to relevant data from the Traffic Management Bureau of the Ministry of Public Security, as of September 2020, the number of motor vehicles nationwide has reached 365 million, and it still maintains a relatively high growth rate. The sharp increase in motor vehicles has brought serious traffic problems. According to the data of the Ministry of Communications, the number of traffic accidents nationwide in 2019 was 247,646, an increase of 2,709 from 2018 and a year-on-year increase of 1.11%. Amon...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01G08G1/0967G08G1/00G06N3/08
CPCG08G1/0125G08G1/096725G08G1/22G06N3/084
Inventor 李烨潘冰史云涛
Owner CENT SOUTH UNIV
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