Car networking crowdsourcing method facing urban spatial information collection

Abstract

The invention, which belongs to the field of the vehicle-mounted self-organizing network technology, relates to a car networking crowdsourcing method facing urban spatial information collection. A car driving habit is excavated by using a trajectory prediction algorithm; according to the car driving habit, a specific car is selected to perform a task; and collection of relevant task information is completed under the circumstance that normal car driving is not affected. A location prediction algorithm based on tensor decomposition is put forward; according to historical trajectory data, a ternary group is constructed; a three-order tensor is constructed based on the ternary group; the tensor is decomposed based on a BPRC specification by means of PITF; iterative parameter optimization is carried out to complement tensor elements; and ranking is carried out based on tensor element values to complete prediction. According to a prediction result, a bipartite graph of a car and a road task is constructed; and maximum matching of the car with the road task is solved by using a Kuhn-Munkres algorithm, so that the matching success probability is maximized. Compared with the prior art, the car networking crowdsourcing method has the following beneficial effects: with combination of the car driving characteristics, information collection redundancy can be reduced and the information collection efficiency can be enhanced.

Description

technical field [0001] The invention relates to a crowdsourcing method for the Internet of Vehicles, in particular to a crowdsourcing method for the Internet of Vehicles oriented to the collection of urban spatial information, and belongs to the technical field of vehicle-mounted self-organizing networks. Background technique [0002] With the continuous advancement of urbanization and the continuous enhancement of vehicle intelligent applications, vehicle ad hoc networks have always been a hot issue in the research of intelligent transportation systems. The vehicle self-organizing network can not only realize the interconnection between vehicles, but also realize the interconnection between vehicles and external infrastructure. Similar to the mobile network, the vehicle is regarded as a node in the network, which can transmit information and realize the communication between the vehicle and the outside world. Optimize urban space through vehicle network attributes to reali...

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Problems solved by technology

[0003] At present, there are few studies on applying the crowdsourcing mechanism to the Internet of Vehicles. The crowdsourcing method for the Internet of Vehicles oriented to the collection of urban spatial information in the present invention mainly involves two aspects of vehicle trajectory prediction and crowdsourcing. In the field of vehicle trajectory prediction, the application More forecasting algorithms are based on Markov chain sequence forecasting. This algorithm cannot solve the common cold start problem in forecasting problems. The use of Markov chain forecasting must be supported by historical behavior data, otherwise it will degenerate into random forecasting

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