Self-Driving Vehicle Fleet Status Logging for Performance Reporting
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Solution Overview
Problem
Current systems lack detailed performance metric analysis and reporting capabilities for fleets of self-driving industrial vehicles, relying on costly human observation and lacking real-time data for productivity improvements.
Innovation Solution
A method and system for measuring self-driving vehicle fleets using a fleet management system that transmits operation instructions, records vehicle status logs, and generates performance reports based on incident duration and count, enabling autonomous operation and data-driven insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human observers are employed to monitor vehicle performance, then detailed performance metric analysis is achieved, but operational costs increase significantly
Solution Approach 1:
The self-driving vehicles autonomously monitor and record their own performance metrics through onboard sensors and processors, eliminating the need for human observers. The vehicle status logs are automatically generated and stored, providing detailed performance data without additional labor costs.
Solution Approach 2:
The patent replaces the mechanical system of human observation with an automated electronic monitoring system. Sensors, processors, and computer-readable media capture and store vehicle status data automatically, substituting human labor with electronic measurement and recording mechanisms.
2Measurement precision
If traditional material-transport vehicles are used, then operational simplicity is maintained, but detailed performance measurements remain unattainable
Solution Approach 1:
The fleet management system serves multiple functions: it controls vehicle operations, monitors performance metrics, stores status logs, and generates performance reports. This multi-functional approach enables detailed measurements without requiring separate specialized systems for each function.
Solution Approach 2:
The system continuously collects vehicle status data through sensors and processors, stores it in structured logs, and generates performance reports that provide feedback on fleet efficiency. This feedback loop enables ongoing performance measurement and optimization.
3Productivity
If real-time vehicle status monitoring is implemented, then productivity insights are improved, but data processing requirements increase
Solution Approach 1:
The system performs preliminary data organization by structuring vehicle status logs with standardized formats and metadata before analysis is needed. Performance reports are generated proactively from pre-processed data, reducing the computational burden during actual productivity analysis.
Data Source
AI summary
Systems and methods for measuring a fleet of self-driving vehicles are disclosed. The system comprises one or more self-driving vehicles, non-transitory computer-readable media in communication with the vehicles, a fleet-management system in communication with the media and vehicles, and a server in communication with the media. The fleet-management system is configured to store vehicle status records comprising a vehicle status pertaining to each of the one or more vehicles, and a timestamp in a vehicle status log on the media. The server has a processor that is configured to generate a fleet-performance report based on the vehicle status log.