On-Board Data Aggregator for Vehicle Service Prediction
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Solution Overview
Problem
Conventional on-board vehicle computers lack the ability for third-parties to assess a vehicle's condition and valuation, making it difficult for users to predict maintenance needs and schedule services conveniently, and providing limited analytics for preserving vehicle valuation.
Innovation Solution
A processor-implemented vehicle tracking and prediction method that aggregates data from the on-board vehicle computer and user devices via an OBD port, predicting service events and generating a vehicle valuation metric, with notifications sent to user devices and the on-board computer to facilitate timely maintenance and valuation management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional on-board vehicle computers are used, then basic vehicle control functions are provided, but third-parties cannot assess vehicle condition and valuation
Solution Approach 1:
An on-board data aggregator acts as an intermediary component that collects data from multiple vehicle systems and makes it accessible to third-parties through remote access, without requiring complex modifications to the existing vehicle computer architecture
Solution Approach 2:
The data aggregator serves multiple functions: collecting data from various vehicle systems, storing it locally, transmitting it remotely, and providing it to different users including third-parties, thereby eliminating the need for separate systems for each function
2Measurement precision
If users manually track vehicle maintenance needs, then no additional hardware is required, but users cannot predict when next service event is needed
Solution Approach 1:
The system performs preliminary analysis of vehicle data to predict future service events before they occur, allowing users to schedule maintenance in advance based on predicted needs rather than waiting for manual observation of deterioration
Solution Approach 2:
The system continuously monitors vehicle data, compares it against service thresholds, and provides feedback to users about upcoming service needs, creating a closed-loop system that improves prediction accuracy over time
3Loss of information
If users want to preserve vehicle valuation, then detailed analytics are needed, but users do not have access to analytics that explain how to change servicing or driving habits
Solution Approach 1:
The system automatically analyzes vehicle data and generates valuation analytics without requiring users to manually collect or process information, providing insights directly to users about factors affecting their vehicle's value
Solution Approach 2:
Instead of requiring users to understand complex analytics to preserve valuation, the system inverts the approach by providing actionable insights that tell users exactly what changes to make in their servicing or driving habits to maintain or improve vehicle value
Data Source
AI summary
This disclosure relates generally to vehicle diagnostics, and more particularly to systems and methods for vehicle tracking and service prediction. In one embodiment, a processor-implemented vehicle tracking and prediction method is disclosed. The method includes sending an electronic signal to activate in-vehicle data collection by an on-board data aggregator disposed in communication with each of an on-board vehicle computer, via an on-board diagnostic port, and one or more user devices. The method further includes receiving aggregated in-vehicle data from the on-board data aggregator, predicting a future vehicle service event based on the aggregated data, and generating a vehicle valuation metric based on the aggregated data. The method further includes sending a notification to at least one of the on-board vehicle computer and the one or more user devices, where the notification is based on the predicted future vehicle service event and the generated vehicle valuation metric.