Vehicle Content Orchestration Using Route-Predicted Edge Generation
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Solution Overview
Problem
Existing methods for serving vehicles with on-demand content fail to optimize content generation and delivery due to hardware limitations, delivery bottlenecks, and inefficient use of network resources, leading to delays and reduced content relevance.
Innovation Solution
A distributed system adopts a method for solving the technical problem by implementing a distributed communication system, the method comprises: predicting a route of the vehicle from a current location of the destination, using resource information of the initial set of computer systems and the vehicle for selecting from the initial set of computer systems a set of computer systems that can provide machine learning based content to the vehicle along the route; predicting a content that can be requested at a specific set of one or more space-time points along the route; selecting a subset of one or more computer systems for generating the predicted content; offloading a generation of the predicted content to the subset of computer systems; controlling content delivery computer systems of the initial set of computer systems to deliver the generated content to the vehicle in accordance with the specific set of space-time points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If content generation is performed using existing methods, then content can be delivered to the vehicle, but delivery delays occur and content relevance is reduced due to inefficient resource utilization and hardware limitations
Solution Approach 1:
The system performs preliminary actions by predicting the vehicle's future location and content needs before the vehicle actually requests the content. The AI model anticipates content requests based on predicted route and vehicle state, allowing content to be pre-generated or pre-fetched, thereby eliminating delivery delays when the vehicle actually needs the content.
Solution Approach 2:
The system continuously monitors vehicle location, route progress, and resource consumption patterns, using this feedback to refine content predictions and optimize resource allocation. The AI model learns from actual vehicle behavior patterns to improve prediction accuracy over time, enabling more efficient content generation and delivery.
2Reliability
If more computer systems are used to provide content, then content relevance and quality improve, but system complexity and resource requirements increase
Solution Approach 1:
The system segments the content delivery function by assigning specific computer systems to different tasks: some systems perform AI-based content prediction, others handle content generation, and others manage delivery. This segmentation allows each component to be optimized independently while working together, improving content relevance without proportionally increasing overall system complexity.
Solution Approach 2:
The AI prediction model acts as an intermediary between the vehicle and the content generation/delivery systems. It processes vehicle state and route information to determine optimal content requests, shielding the vehicle from the complexity of the underlying content infrastructure while improving content relevance through intelligent mediation.
3Productivity
If existing hardware and network resources are used, then infrastructure requirements are minimized, but delivery bottlenecks occur and resource utilization is inefficient
Solution Approach 1:
The system dynamically adjusts resource allocation and content delivery strategies based on real-time vehicle location, network conditions, and predicted content needs. Resource utilization is optimized by assigning tasks to the most appropriate computer systems at any given moment, and delivery bottlenecks are avoided by proactively managing content transmission timing and routing.
Data Source
AI summary
Disclosed is a method for serving by a distributed communication system a vehicle traveling from an origin to a destination. The distributed communication system comprises an initial set of computer systems. The method comprises: predicting a route of the vehicle from a current location of the vehicle to the destination. Resource information may be used for selecting from the initial set of computer systems a set of computer systems that can provide machine learning based content to the vehicle along the route. A subset of one or more computer systems of the set of computer systems may be selected for generating a predicted content. A generation of the predicted content may be offloaded to the subset of computer systems. Content delivery computer systems of the initial set of computer systems may be controlled to deliver the generated content to the vehicle in accordance with the specific set of space-time points.