Deep learning-based mobility prediction method of nodes in vehicle-mounted Ad Hoc network

Abstract

The invention provides a deep learning-based mobility prediction method of network nodes in a vehicle-mounted Ad Hoc network. The method effectively utilizes traffic regulations to realize mobility prediction of multiple future time points of the vehicle nodes on mobility constraints of the nodes, history travel data of the vehicle nodes and personalized information of vehicles and drivers. The method includes: combining different types of vehicle motion models to establish a vehicle history travel data sample library and a traffic regulation constraint database, and simultaneously agreeing onsample travel data features; then utilizing a recurrent neural network to extract vehicle sample mobility deep-layer features, and establishing a mobility prediction model; then using a gradient-descent back-propagation algorithm for training of model parameters; and finally, utilizing real-time data information of current movement of vehicles to predict mobility. The invention relates to vehiclemovement model data analysis and neural-network model construction and parameter training realization methods. The prediction method utilizes non-linear prediction capability of deep learning, maps the vehicle running data features to vehicle movement, and realizes mobility prediction of the nodes in the vehicle-mounted Ad Hoc network.

Description

technical field [0001] The invention relates to a network node mobility prediction method based on deep learning in a vehicle-mounted ad hoc network, which belongs to the field of communication technology, and in particular to a vehicle node mobility prediction method in a vehicle-mounted ad hoc network based on a deep learning method. Background technique [0002] In recent years, vehicle ad hoc networks have been paid close attention by research institutions and researchers all over the world. Vehicle Ad Hoc Network (VANET) is a new type of mobile ad hoc network, which consists of communication between vehicles on the road (vehicle-to-vehicle, V2V) and communication between vehicles and infrastructure (vehicle- to-infrastructure (V2I), a self-organizing, easy-to-deploy, low-cost, and open-structure wireless communication network is built on the road. The mobility prediction of vehicle ad hoc network is of great significance to the realization of safe and intelligent trave...

AI Technical Summary

Problems solved by technology

Due to the characteristics of rapid mobility, frequent topology changes, brief connectivity, and large network scale of the vehicle ad hoc network, it poses great challenges to the mobility prediction of vehicle nodes.

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