Intelligent sensing and distribution network architecture of road traffic data for traffic guidance

Abstract

The invention discloses intelligent sensing and distribution network architecture of road traffic data for traffic guidance. The system carries out estimation and analysis on a road condition state according to current real-time local data, whole-city global data and historical records of all road sections, so that the user can obtain the surrounding road condition state visually; and traffic guidance is completed efficiently and precisely based on the own need of the user. Moreover, a storage unit RSSU is provided and a camera and a low-cost storage function are added; a user that drives thecar to a blocked route or does not drive the car to a dispersed road maliciously can be marked; data of the user with the bad behavior and the collected local data and videos are uploaded to a preliminary data processing module and the historical record fed back by the preliminary data processing module each day is stored and used as the important parameter for estimating the road condition stateby the whole architecture.

Description

technical field [0001] The invention relates to the technical field of the Internet of Vehicles, in particular to a network architecture for intelligent perception and distribution of road traffic data oriented to traffic dredging. Background technique [0002] At present, the Internet of Vehicles has penetrated into various industries and business functions, and has become an inseparable part of the information age. Regardless of how the rise of the Internet has changed the automotive landscape, the fundamental nature of a car is a vehicle whose most tangible goal is to efficiently and safely transport users to their destinations. However, due to the constraints of the actual weather environment, traffic congestion and frequent traffic accidents, we cannot realize this most basic function. Existing traffic control methods mainly have the following drawbacks: 1. A relatively complete and systematic framework is not formed, but road guide poles, intelligent traffic lights, s...

AI Technical Summary

Problems solved by technology

However, due to the constraints of the actual weather environment, traffic congestion and frequent traffic accidents, we cannot realize this most basic function.

Existing traffic control methods mainly have the following disadvantages: 1. A relatively complete and systematic framework is not formed, and road guide poles, intelligent traffic lights, sensors are only installed, or GPS, GPRS, etc. are introduced

These devices and technologies are expensive, and even some have little effect. It is unrealistic to install a large number of them to achieve traffic diversion

2. The traditional traffic diversion method is to divert traffic only when congestion occurs. If the congestion state of the road section can be estimated, the loss will obviously be reduced a lot

3. Most of the traditional traffic diversion methods are to divert vehicle users to other slightly distant roads, but the congestion situation may not be very serious, and the waiting time at the congested place may be shorter than the time of detouring the long road, which causes loss of user time

4. Most of the collected data needs to be transmitted to the remote control center for data analysis and processing, and then fed back to the user. This mechanism can be said to be very time-consuming and inefficient

Moreover, all massive data must be stored in the control center. Disadvantages such as network congestion and high latency are inevitable. Once the central network collapses, the consequences will be disastrous

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