Dangerous road section traffic accident early-warning method for vehicle-mounted short-distance communication network

Abstract

The invention discloses a dangerous road section traffic accident early-warning method for a vehicle-mounted short-distance communication network. According to the invention, a roadside node is arranged beside a dangerous road section. Meanwhile, the information containing the speed and the type of a vehicle and periodically sent by the vehicle is collected through a communication channel of the vehicle-mounted short-distance communication network. By utilizing a bayesian network having the historical data learning function, the occurrence probability of traffic accidents of the vehicle is predicted. The implementation method comprises five parts. During the first part, a factor for determining accident prediction and a value range of the factor are determined. During the second part, a bayesian prediction model is determined according to the prediction factor and the value range of the factor. During the third part, the learning of the bayesian network is carried out and the probability of each condition is determined based on the historical data of traffic accidents and expert experiences. During the fourth part, the reasoning method of the bayesian network is determined. Duringthe fifth part, the method for collecting the vehicle information and the method for predicting the vehicle traffic accident are realized through the roadside node.

Description

technical field [0001] The invention relates to a method for early warning of traffic accidents on dangerous road sections applicable to a vehicle-mounted short-distance communication network, which belongs to the field of communication and information, in particular to the technical field of vehicle-mounted communication. Background technique [0002] Historical data and experience play a big role in traffic accident-prone road sections. If vehicles can learn the causes of traffic accidents in history and make judgments based on their current driving status, similar traffic accidents can be effectively avoided. However, a large number of vehicles passing through dangerous road sections do not understand the causes of traffic accidents that occurred in history. Even if there are signage reminders on some road sections, the amount of information is limited, and it is difficult for drivers to make judgments based on the current vehicle status in a short period of time. . [0...

AI Technical Summary

Problems solved by technology

Although this method is simple, it will interfere with normal driving vehicles, and the pertinence is not strong

It can predict the vehicles that are prone to traffic accidents in time, that is, it can not interfere with normal driving, and can effectively reduce traffic accidents, but the roadside nodes need to have the ability to collect vehicle information and make intelligent judgments

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