A v2v-based collaborative urban road congestion detection method

Abstract

The invention discloses a V2V (Vehicle to Vehicle)-based collaborative city road congestion detecting method. According to the V2V-based collaborative city road congestion detecting method disclosed by the invention, a vehicle running velocity v and the traffic flow density rho are used as influence factors of a traffic congestion level and are input in a fuzzy controller, so that a local traffic congestion level is obtained; when a vehicle O detects that traffic congestion happens, a congestion judgment process can be started, i.e., when the vehicle O in congestion sends a congestion query message to a vehicle in a neighbor table is detected, the vehicle in the neighbor table replies a congestion verification message to the vehicle O according to a congestion judgment result; when the traffic congestion is not detected, only position information can be mutually sent between vehicles, so that network overload can be effectively inhibited; through a mechanism that traffic congestion detection is completed through cooperation of the vehicle O and the vehicle in the neighbor table, the accuracy of detecting the traffic congestion by the vehicle is remarkably increased; traffic congestion level values output by the fuzzy controller are continuous values, the grade of the traffic congestion is accurately reflected, and the vehicle O is capable of calculating a congestion zone and the congestion length according to the position information.

Description

technical field [0001] The invention belongs to the field of urban road congestion detection, and in particular relates to a V2V-based cooperative urban road congestion detection method. Background technique [0002] In traffic management, the occurrence of traffic congestion will not only affect traffic safety, but also cause huge economic losses. There are currently two main means to solve traffic safety problems and social and economic losses caused by traffic congestion, one is to improve the construction level of road infrastructure, and the other is to improve traffic efficiency on the basis of existing road infrastructure To ensure driving safety, it is mainly to detect urban road congestion by laying geomagnetic coils or erecting cameras. This method can only detect road traffic information in the area covered by the facility, but laying geomagnetic coils and erecting cameras is subject to cost, land planning, Due to the constraints of environmental protection, unli...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is the lack of a verification mechanism, that is, after each vehicle detects the local traffic congestion, it cannot associate the traffic congestion estimates between different vehicles, which leads to unreliable congestion detection results.

Literature [Bauza R, Gozálvez J.Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications[J].Journal of Network and Computer Applications,2013,36(5):1295-1307.] proposed to detect the traffic congestion based on fuzzy control The traffic congestion level of the vehicle is further verified by the front-to-back propagation method in the congestion queue. This method improves the accuracy of traffic congestion level detection, but this method lacks the association mechanism between local vehicles and congestion queues. At the same time, the judging process of the vehicle in front of the congested queue is increased, which is more complex

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