Autonomous Vehicle Edge Computing for Low-Latency Resource Allocation
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Solution Overview
Problem
Client devices, such as smartphones and IoT sensors, face inefficiencies in performing computing tasks due to limited resources and reliance on cloud computing, which can be latency-prone and inefficient in handling high-demand scenarios, while edge computing lacks the ability to accurately predict and dynamically allocate resources.
Innovation Solution
An edge computing autonomous vehicle infrastructure (ECAVI) leverages autonomous vehicles' computing resources to provide low-latency, high-performance computing by dynamically assigning and reallocating resources based on real-time or predictive analysis of computing needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud computing is used to handle computing tasks, then computing power can be accessed, but latency increases and efficiency decreases in high-demand scenarios
Solution Approach 1:
The patent transitions from centralized cloud computing to distributed edge computing by utilizing autonomous vehicles as mobile computing nodes. This dimensional shift from a single remote location to multiple distributed locations near end-users reduces latency while maintaining access to substantial computing power through vehicle-mounted GPUs and processors.
Solution Approach 2:
Autonomous vehicles serve as intermediaries between cloud data centers and end-user devices. They receive computing tasks from nearby devices, process them using their onboard computing resources, and return results, thereby reducing the distance data must travel and minimizing latency while still leveraging powerful computing capabilities.
2Loss of time
If edge computing is implemented to reduce latency, then response time improves, but resource allocation accuracy decreases without predictive analysis
Solution Approach 1:
The system performs preliminary actions by using machine learning models to predict future computing resource demands at different locations. Based on these predictions, autonomous vehicles are proactively routed to anticipated high-demand areas before实际需求 occur, ensuring both low latency and accurate resource allocation when computing tasks are needed.
Solution Approach 2:
The system implements feedback loops where actual computing task requests and resource usage patterns are continuously monitored and fed back to the machine learning models. This feedback refines prediction accuracy over time, improving both response time and resource allocation precision by adapting to changing demand patterns.
3Adaptability or versatility
If autonomous vehicles are deployed as mobile data centers, then computing resource availability increases, but system complexity increases
Solution Approach 1:
Autonomous vehicles are designed with multi-functionality, serving both their primary transportation function and as mobile edge computing nodes. By integrating computing resources, communication systems, and task management capabilities into existing vehicle architectures, the system increases computing resource availability without requiring entirely separate infrastructure, thereby managing complexity.
4Ease of operation
If client devices perform computing tasks locally, then autonomy is maintained, but computing efficiency decreases due to limited resources
Solution Approach 1:
The system merges the computing capabilities of multiple autonomous vehicles to create a distributed computing cluster that serves local client devices. This combination provides client devices with access to substantially greater computing power than they possess individually, while maintaining local processing advantages by eliminating the need to communicate with distant cloud data centers.
Data Source
AI summary
A computer resource disparity is detected. The computer resource disparity is related to performing a computing task. The computer resource disparity is located proximate to a first location. A set of one or more autonomous vehicles capable of being adjacent to the first location is identified. An autonomous vehicle computing inquiry is generated. The inquiry is generated based on the first location and based on the computer resource disparity. The autonomous vehicle computing inquiry is transmitted based on the first location. An autonomous vehicle status is received in response to the autonomous vehicle computing inquiry that includes a set of one or more computing resources of the set of autonomous vehicles. A first autonomous vehicle of the set of autonomous vehicles is assigned to perform the computing task. The assignment is based on the set of computing resources of the set of autonomous vehicles.