Identification and early warning method of security features of vehicle short-distance communication network

Abstract

The present invention provides an identification and early warning method for security features of a vehicle-mounted short-distance communication network. The vehicle-mounted nodes process and obtain identification feature data according to the acquired heartbeat information of surrounding nodes, and use the identification feature data as training data to construct a security feature classification tree. For each security feature of the security warning, multiple security feature classification trees are constructed and integrated to form a security feature classification tree set, and corresponding multiple weak classifiers are determined. For all security features of the security warning, each weak classifier and its associated Accounting for the weight value, constructing a strong classifier. During the driving process, using real-time identification feature data as input data, using the strong classifier for safety feature identification and safety warning, and adjusting the weight of each weak classifier according to the quality of the warning, and updating strong classifier. The vehicle-mounted node of the present invention can have a safety early warning capability adaptive to the driving environment by collecting comprehensive and accurate data, classifying, judging, and continuously learning the data.

Description

technical field [0001] The invention relates to an identification and early warning method for safety features of a vehicle-mounted short-distance communication network, and belongs to the technical field of vehicle-mounted network communication. Background technique [0002] Vehicle short-distance communication (Vehicle to X: V2X) network is realized by short-distance communication technologies such as wireless communication, GPS / GIS, and sensors. Communication between the workshop (Vehicle-2-Vehicle), the outside of the vehicle (vehicle-2-Infrastructure), and the vehicle (Vehicle-2-Person). [0003] In the process of driving the vehicle, the driver can make judgments based on the observed environment and surrounding vehicle conditions based on experience, and take corresponding measures to avoid possible dangers, and with the accumulation of driving experience, the judgments made will become more and more accurate . With the rapid development of V2X technology, the infor...

AI Technical Summary

Problems solved by technology

Existing machine learning algorithms, such as the boosting algorithm, feature classification is relatively simple, and the classification results are not evaluated, while the safety features (such as road conditions, weather, vehicle speed, location, etc.) used by vehicle nodes for safety warnings are complex and changeable. Therefore, Applying existing machine learning algorithms to vehicle nodes cannot achieve the ideal safety warning effect

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