Vehicle Service Update Scheduling for Peak Bandwidth Constraints
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Solution Overview
Problem
Existing methods for delivering updatable services to vehicles are limited by bandwidth constraints during peak traffic times, leading to suboptimal use of backend server data-transmission bandwidth and reduced service delivery efficiency.
Innovation Solution
A method and system for time-controlled delivery of updatable services that optimize network loading by considering vehicle availability and data connection availability, using machine-learning algorithms to identify optimal delivery periods based on historical data and individual vehicle behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If service updates are delivered during peak traffic times when vehicles are being moved, then service updates can be delivered to vehicles that are actively using the system, but the data-transmission bandwidth is overloaded and the backend server rejects further update requests
Solution Approach 1:
The system performs preliminary actions by analyzing historical vehicle movement data and predicting future availability windows before the actual service delivery. The backend server proactively identifies optimal delivery periods and schedules updates in advance, rather than reacting to immediate delivery requests during peak times.
Solution Approach 2:
The delivery schedule is made dynamic by continuously adapting to predicted vehicle availability patterns. The system adjusts delivery timing based on learned vehicle usage behaviors, allowing the same vehicle to receive updates at different times on different days based on its typical movement patterns.
2Quantity of substance
If the backend server distributes network loading evenly over time, then the average network loading is optimized, but service updates cannot be delivered during periods when vehicles are not available
Solution Approach 1:
The system implements feedback loops where the backend server continuously monitors actual vehicle availability against predicted patterns, and uses this feedback to refine future predictions. Historical data on vehicle movement and data connection availability is constantly analyzed to improve the accuracy of delivery scheduling.
Solution Approach 2:
The system changes the temporal parameters of service delivery by shifting updates from fixed schedules to flexible, prediction-based timing. Instead of delivering updates at regular intervals or only during peak times, the system varies delivery parameters based on predicted vehicle availability patterns.
3Reliability
If service updates are delivered only when data connection availability is confirmed, then delivery reliability is improved, but the number of vehicles receiving updates is limited by bandwidth constraints
Solution Approach 1:
The system segments the fleet into different delivery groups based on predicted availability patterns. Instead of attempting to deliver to all vehicles simultaneously during peak times, the backend server divides updates into multiple scheduled deliveries targeting different vehicle subsets at different times.
Data Source
AI summary
A computer-implemented method for a time-controlled delivery of updatable services to on-board systems of vehicles which use the services. The method includes analyzing the detected data to identify delivery time periods for the updatable services being optimal for each vehicle which uses the services, wherein the network usage of the backend server is optimally allotted to the specified time period on the basis of the availability of the data connection of the vehicles using the services to the backend server. A system for a time-controlled delivery of updatable services to on-board systems of vehicles that use the services is also disclosed.