Integrating Metric Collection with Vehicle Request Fulfillment
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Solution Overview
Problem
Current systems lack integration of metric collection with vehicle request fulfillment, making it difficult for vehicle providers to obtain relevant metrics on vehicle features and usage, especially in scenarios involving autonomous vehicles or specific travel conditions.
Innovation Solution
A method and system that integrate metric collection with vehicle request fulfillment by suggesting vehicles based on criteria matching from both vehicle requestors and data requestors, using machine learning to match vehicle and data criteria, and providing automatically and manually recorded metrics, allowing for feedback and combination of metrics from multiple vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicle providers use traditional separate systems for vehicle request fulfillment and metric collection, then system simplicity is maintained, but metric collection efficiency and relevance deteriorate
Solution Approach 1:
The patent merges the vehicle request fulfillment system with the metric collection system into a single integrated platform. The vehicle request system accepts both vehicle selection criteria from requestors and data collection criteria from data requestors, then matches vehicles based on both sets of criteria simultaneously. This consolidation eliminates the need for separate metric collection initiatives and automatically collects relevant metrics during normal vehicle operations.
Solution Approach 2:
The integrated system performs multiple functions: it fulfills vehicle requests by matching vehicles to requestor criteria, collects metrics based on data requestor criteria, and provides data to multiple stakeholders. The same vehicle matching algorithm serves both vehicle assignment and metric collection purposes, making the system multi-functional and reducing overall system complexity.
2Measurement precision
If vehicle providers collect comprehensive vehicle metrics, then data accuracy improves, but data collection costs and processing complexity increase
Solution Approach 1:
The system performs preliminary action by having data requestors specify their data criteria in advance before vehicle deployment. The system pre-processes these criteria and integrates them with vehicle request criteria. This preliminary setup ensures that only relevant metrics are collected during vehicle operations, avoiding the need to collect and then filter large volumes of unnecessary data.
Solution Approach 2:
The system applies local quality by collecting different sets of metrics for different vehicles based on specific data requestor requirements and vehicle usage patterns. Rather than uniformly collecting all possible metrics from all vehicles, the system tailors metric collection to local needs - collecting specific metrics from specific vehicles based on the matched criteria, thereby reducing overall data processing complexity.
3Measurement precision
If vehicle requestors specify detailed vehicle criteria, then vehicle matching precision improves, but request processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing and storing vehicle criteria and data criteria in structured formats before requests arrive. The matching algorithm is pre-configured with available vehicles and their attributes. When a request comes in, the system quickly queries pre-organized data structures rather than processing raw information, significantly reducing processing time while maintaining high matching precision.
4Loss of information
If the system integrates multiple data criteria from both vehicle requestors and data requestors, then metric relevance improves, but system complexity increases
Solution Approach 1:
The system merges vehicle request criteria and data collection criteria into a unified matching framework. Both sets of criteria are processed together through a single algorithm that evaluates vehicles against combined requirements. This unified approach ensures that selected vehicles satisfy both the requestor's operational needs and the data requestor's metric collection requirements, maximizing metric relevance without requiring separate complex processing pipelines.
Data Source
AI summary
A system and method to integrate metric collection with vehicle request fulfillment include obtaining a vehicle request from a vehicle requestor, the vehicle request including one or more vehicle criteria, and obtaining a data request from a data requestor, the data request including one or more data criteria corresponding with vehicle metrics. The method includes suggesting to the vehicle requestor one or more suggested vehicles from a fleet of vehicles based on their match with the one or more vehicle criteria and the one or more data criteria. The method also includes obtaining the vehicle metrics based on usage of one of the one or more suggested vehicles by the vehicle requestor, and providing the vehicle metrics to the data requestor.