Vehicle dispatching method and device, and storage medium

By working collaboratively between cloud servers and edge computing nodes, the effective travel time is calculated based on vehicle driving status and road conditions. The scheduling hierarchy is divided and computing tasks are transferred, which solves the scheduling delay problem caused by the increase in cloud server computing load and improves the traffic efficiency and resource utilization of intersections.

CN122416752APending Publication Date: 2026-07-17SAIC GM WULING AUTOMOBILE CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SAIC GM WULING AUTOMOBILE CO LTD
Filing Date
2026-05-13
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

When cloud servers handle a large number of vehicle dispatches, the computational load increases, causing delays in the issuance of dispatch instructions. Vehicles then struggle to receive instructions in a timely manner, reducing the efficiency of traffic flow at intersections.

Method used

By working collaboratively between cloud servers and edge computing nodes, the effective travel time is calculated based on the vehicle's driving status and road conditions ahead. The scheduling hierarchy is then divided, and high-urgency vehicle computing tasks are transferred to edge nodes for execution, thereby optimizing the computing cycle and reducing computing resource consumption.

Benefits of technology

This enables vehicles to receive dispatch instructions in a timely manner, reducing unnecessary deceleration or stopping and waiting, and improving the overall traffic efficiency and computing resource utilization of the intersection.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application provides a vehicle scheduling method and device and a storage medium. The method comprises: acquiring current state information of all vehicles in a preset range; determining an effective passing time of each vehicle according to the driving state and the road condition in front of the vehicle; dividing each vehicle into a corresponding scheduling level according to the effective passing time; acquiring the number of vehicles in the scheduling level with the highest urgency and the current load of the edge computing node; when the number of vehicles exceeds a preset number threshold and the current load is less than a preset load threshold, transferring at least part of the scheduling calculation tasks in the scheduling level with the highest urgency to the edge computing node for execution; and performing scheduling calculation on the corresponding vehicles in each scheduling level according to the respective corresponding calculation period. It can be understood that by dividing the scheduling levels of multiple different calculation periods and dispersing part of the scheduling calculation tasks to the edge computing node, the instruction issuing delay is reduced and the overall passing efficiency is improved.
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