A Utilization Method of Vehicle Computing Resources Based on Trajectory Prediction

Abstract

In order to solve the problem in the prior art that the computing resources provided by the near-end edge server are limited and unable to adapt to the exponentially increasing number of vehicles, the present invention provides a vehicle computing resource utilization method based on trajectory prediction. When the task request vehicle has a computing task demand , first predict whether the future driving trajectories of the surrounding vehicles are similar to the future driving trajectories of the task issuing vehicles according to the historical driving trajectories of the vehicles; then, screen out the vehicles similar to the future trajectories of the task issuing vehicles and classify them; Then, confirm the vehicles participating in the task and formulate corresponding contracts for them. Finally, after obtaining the contract that maximizes the benefits of the task requesting vehicle, the task requesting vehicle starts to upload all its tasks to the task execution vehicle in turn, and then the task execution vehicle processes them sequentially. After the task is processed, the task processing results are returned to the task requesting vehicle in turn, and the task ends.

Description

technical field [0001] The invention relates to the field of resource utilization in the Internet of Vehicles technology, in particular to a method for utilizing vehicle computing resources based on trajectory prediction. Background technique [0002] The Internet of Vehicles (IOV), as a promising application domain of the Internet of Things (IOT), has attracted extensive attention from both industry and academia. IOV has become a research field for the development of industrial applications in smart cities, and the key research contents include: traffic management and road safety. Many countries are focusing on establishing IOV systems, such as ERTICO-ITS in Europe. In the industrial field, global automakers such as Volvo and BMW have developed test bench systems based on vehicle-to-vehicle (V2V) communication. [0003] In 2018, the global vehicle population has reached 1.34 billion, and it is expected to reach 2 billion by 2035. Future vehicle services will inevitably r...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem in the prior art that the computing resources provided by the near-end edge server are limited and unable to adapt to the exponentially increasing number of vehicles, the present invention provides a method for utilizing vehicle computing resources based on trajectory prediction

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