US 12,586,041 B2Granted

Computational capability based on vehicle maintenance

AssigneeInternational Business Machines Corporation

InventorsShailendra Moyal, Nitika Sharma, Sarbajit K. Rakshit, Akash U. Dhoot

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Abstract

An approach for reducing edge computing resources associated with autonomous vehicle infrastructure is disclosed. The approach utilizes digital twin computing to create a digital copy of the autonomous vehicle and edge computing demand. The approach can simulate the entire journey of the autonomous vehicle(s) on a travel path to determine the edge computing resources requirements. And based on the generated data set, the approach provides recommendations of proactive vehicle maintenance plan to improve edge computing capability of the vehicle based on the selected route. Other recommendation can include identifying whether road maintenance is to be performed for optimum usage of edge computing capability.

Claims20

Dependent claims

A computer-implemented method for reducing edge computing resources associated with autonomous vehicle, the computer-implemented method comprising:

receiving data associated with autonomous vehicle;
generating a digital twin copy of the autonomous vehicle based on the received data, wherein the digital twin copy leverages artificial intelligence and machine learning and further comprising:
creating a first digital twin version of the autonomous vehicle on a digital twin platform;
creating a second digital twin version of an edge computing demand associated with the autonomous vehicle, wherein the edge computing demand requires computational capability of one or more edge computing resources associated with one or more travel route by the autonomous vehicle;
and
updating the first digital twin version and the second digital twin version with real-time data;
analyzing the digital twin copy, further comprising:
performing one or simulations based on travel requirement;
determining results of the one or more simulations meets the travel requirement;
and
determining a maintenance plan associated with he autonomous vehicle;
creating an action plan based on analysis;
and
executing the action plan.

11 more elements

A computer program product for reducing edge computing resources associated with autonomous vehicle, the computer program product comprising:

one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising:
program instructions to receive data associated with autonomous vehicle;
program instructions to generate a digital twin copy of the autonomous vehicle based on the received data, wherein the digital twin copy leverages artificial intelligence and machine learning and further comprising:
program instructions to create a first digital twin version of the autonomous vehicle on a digital twin platform;
program instructions to create a second digital twin version of an edge computing demand associated with the autonomous vehicle, wherein the edge computing demand requires computational capability of one or more edge computing resources associated with one or more travel route by the autonomous vehicle;
and
program instructions to update the first digital twin version and the second digital twin version with real-time data;
program instructions to analyze the digital twin copy, further comprising:
program instructions to perform one or simulations based on travel requirement;
program instructions to determine results of the one or more simulations meets the travel requirement;
and
program instructions to determine a maintenance plan associated with he autonomous vehicle;
program instructions to create an action plan based on analysis;
and
program instructions to execute the action plan.

12 more elements

A computer system for reducing edge computing resources associated with autonomous vehicle, the computer system comprising:

one or more computer processors;
one or more computer readable storage media;
and
program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising:
program instructions to receive data associated with autonomous vehicle;
program instructions to generate a digital twin copy of the autonomous vehicle based on the received data, wherein the digital twin copy leverages artificial intelligence and machine learning and further comprising:
program instructions to create a first digital twin version of the autonomous vehicle on a digital twin platform;
program instructions to create a second digital twin version of an edge computing demand associated with the autonomous vehicle, wherein the edge computing demand requires computational capability of one or more edge computing resources associated with one or more travel route by the autonomous vehicle;
and
program instructions to update the first digital twin version and the second digital twin version with real-time data;
program instructions to analyze the digital twin copy, further comprising:
program instructions to perform one or simulations based on travel requirement;
program instructions to determine results of the one or more simulations meets the travel requirement;
and
program instructions to determine a maintenance plan associated with he autonomous vehicle;
program instructions to create an action plan based on analysis;
and
program instructions to execute the action plan.

15 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/741,705Filed 11 May 2022
Published: US 2023/0368153 A116 Nov 2023
Granted: 24 Mar 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification3 codes

G06Q 10/20
Administration; Management: administration of product repair or maintenance
G06Q 10/047
Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem":…
G06Q
Information and communication technology [ict] specially adapted for administrative, commercial, financial, managerial