Autonomous Vehicle Digital Twin for Edge Computing Maintenance
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Solution Overview
Problem
Autonomous vehicles often lack sufficient edge computing resources to effectively handle various contextual situations on the road, such as road conditions, weather, and vehicle availability, leading to inefficiencies and safety concerns.
Innovation Solution
Utilizing digital twin technology to simulate the journey of autonomous vehicles and analyze edge computing requirements, providing proactive maintenance plans to enhance computing capabilities based on contextual needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles rely on onboard edge computing resources, then computational decisions can be made locally and quickly, but the computing power and resources are insufficient to handle complex contextual situations
Solution Approach 1:
The patent combines onboard edge computing resources with external cloud computing resources through a hybrid architecture. The vehicle's onboard system handles immediate local processing while connecting to external cloud infrastructure for complex computations, thereby merging limited onboard power with abundant external power to resolve the contradiction between reliability and computing power.
Solution Approach 2:
The patent creates a multi-functional computing system that can operate in multiple modes: standalone onboard edge computing for basic functions, cloud-connected mode for complex scenarios, and collaborative multi-vehicle edge computing. This universal system adapts its computing resources based on situational requirements, resolving the contradiction by providing sufficient computational power across different operational contexts.
2Productivity
If more vehicles participate in edge computing collaboration, then computational effectiveness improves, but system complexity and coordination overhead increase
Solution Approach 1:
The patent segments the edge computing system into hierarchical levels: individual vehicle onboard units, local multi-vehicle clusters, and regional cloud platforms. This segmentation allows vehicles to collaborate in small manageable groups rather than requiring all vehicles to coordinate simultaneously, improving computing effectiveness while controlling complexity through modular organization.
Solution Approach 2:
The patent introduces intermediary components including edge servers and cloud platforms that mediate between individual vehicles. These intermediaries manage coordination, resource allocation, and data sharing, thereby enabling multiple vehicles to participate in collaborative computing without each vehicle needing to directly coordinate with all others, reducing overall system complexity.
3Productivity
If computational resources are allocated dynamically based on contextual needs, then computing efficiency improves, but resource management complexity increases
Solution Approach 1:
The patent implements dynamic resource allocation where computing resources are flexibly assigned based on real-time contextual assessment of road conditions, weather, traffic, and vehicle states. The system continuously adapts its computational priorities and resource distribution, achieving high computational efficiency while the underlying complexity is managed through automated dynamic adjustment rather than static complex management structures.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors computational workload, resource availability, and contextual requirements, then adjusts resource allocation accordingly. This closed-loop feedback system automates the complex resource management decisions, improving computational efficiency while reducing the burden of manual or static resource management complexity.
Data Source
AI summary
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.