OTA Update Analytics for Vehicle Fleet Optimization
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Solution Overview
Problem
Existing systems for over-the-air (OTA) software updates often require extended periods to ensure all targeted vehicles receive the update, leading to increased server usage and operational costs.
Innovation Solution
A system that analyzes OTA aggregate data to determine an optimized time period for software updates, predicts future OTA push times, and generates instructions for future updates based on these predictions, thereby minimizing the time needed to complete the update process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If OTA software updates are pushed to all targeted vehicles using existing systems, then all vehicles can receive the update, but the update process requires extended periods and increased server usage
Solution Approach 1:
The system performs preliminary actions by analyzing historical OTA aggregate data to predict the optimal time period for future software updates before actually pushing the update. This prediction step allows the system to prepare and schedule updates at the most efficient time, reducing the overall duration needed to complete the update process while ensuring all targeted vehicles receive the update.
Solution Approach 2:
The system uses feedback from historical OTA aggregate data regarding when vehicles are most likely to accept software updates. By continuously analyzing this feedback data and adjusting prediction models, the system optimizes future update schedules to minimize push duration while maintaining high delivery completeness across the vehicle fleet.
2Reliability
If OTA software updates are pushed using existing systems, then updates can be delivered to vehicles, but server usage increases leading to higher operational costs
Solution Approach 1:
The system performs preliminary analysis of OTA aggregate data to predict the optimal time period for software updates before execution. This preliminary prediction enables the system to schedule updates during periods of highest vehicle acceptance probability, thereby delivering updates to all targeted vehicles while minimizing the duration of server operation and reducing overall server usage and operational costs.
Solution Approach 2:
The system changes the temporal parameter of update delivery by predicting and selecting the optimal time period for pushing updates. By adjusting when updates are delivered based on analyzed patterns in vehicle acceptance behavior, the system reduces the total time servers need to operate at high capacity, thereby reducing energy consumption and operational costs while maintaining complete update delivery.
Data Source
AI summary
A system is provided for use with over-the-air (OTA) analytic data corresponding to OTA aggregate data associated with an OTA push of a software update to a plurality of devices. The system includes: a data receiver configured to receive the OTA aggregate data; a memory having instructions stored therein; and a processor configured to execute the instructions stored in the memory to cause the system to: store the received OTA aggregate data into the memory; analyze the OTA aggregate data to determine an optimized time period for the OTA push; predict a future OTA push time period based on the optimized time period; and generate a future OTA push instruction based on the predicted future OTA push time period.