Automatic driving service unloading method based on edge calculation

Abstract

The invention relates to an automatic driving service unloading method based on edge calculation. The method comprises the steps of S1, enabling a vehicle with an automatic driving function to sense the surrounding environment through various sensors and obtains data; S2, querying and detecting a current network state and available resources through an API before the vehicle runs; S3, enabling theedge or the cloud to calculate unloading strategies in different network states through an ILP integer linear programming model, and store the unloading strategies as a lookup table; S4, when the vehicle runs, registering the current network state with the stored lookup table, and when the matching is unsuccessful, performing adaptive dynamic scheduling by using a heuristic algorithm, and takingthe nearest task unloading strategy in the lookup table as the reference of the heuristic algorithm for iteration; and S5, returning a calculation result to the unmanned vehicle by the edge server andthe cloud, and repeating the operation in the step S4 at the beginning of the next period. According to the method, the low-delay performance required by unmanned driving is ensured, the service quality is improved, and the response time of an automatic driving calculation task is effectively shortened.

Description

technical field [0001] The present invention relates to the task offloading strategy of edge computing and the technical field of automatic driving, and more particularly, relates to an automatic driving service offloading method based on edge computing. Background technique [0002] In recent years, unmanned driving technology has gradually become a research hotspot in universities and enterprises at home and abroad, and its frequent appearances in commercial application tests have attracted public attention. In the process of automatic driving of unmanned vehicles, the information perception subsystem of the autonomous driving system and the real-time calculation and feedback of data are the key basis for the automatic driving of unmanned vehicles. [0003] With the improvement of the demand for unmanned driving technology and the improvement of various functions and technologies of sensors, more and more sensors are deployed and applied on the unmanned vehicle platform. H...

AI Technical Summary

Problems solved by technology

[0004] For the Vehicle Cloud Computing (VCC) model, there is a long and unpredictable delay between the On-Board Unit (OBU) and the cloud platform

However, the method based on multiple MEC-BC (Mobile Edge Computing Base Station) cooperation to enhance MEC (Mobile Edge Computing) computing transfer service focuses more on energy saving and cannot meet the requirements of low latency.

So far, no research has applied edge computing to practical autonomous driving applications, which can be deployed on inexpensive and lightweight on-board computing platforms while meeting stringent low-latency requirements

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