Dynamic Geofence Creation for Fleet Vehicle Onboarding
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Solution Overview
Problem
Current fleet management systems face inefficiencies in managing vehicle fleets due to manual and error-prone processes for associating on-board units with vehicles and creating geofences, leading to increased operational costs and reduced efficiency.
Innovation Solution
A system that correlates fleet management data with telematics data using machine learning to automate the association of on-board units with vehicles and create geofences, reducing the need for manual input and minimizing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for associating on-board units with vehicles and creating geofences, then human control and decision-making are maintained, but operational costs increase and efficiency decreases
Solution Approach 1:
The system enables automated self-service by using machine learning algorithms to automatically associate on-board units with vehicles and dynamically create geofences based on telematics data, eliminating the need for manual human intervention in these repetitive tasks while maintaining high accuracy through continuous learning from data patterns
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Machine learning models substitute human operators in analyzing telematics data, identifying vehicle-OBU associations, and generating geofence boundaries, thereby transforming manual labor into automated algorithmic processing that improves efficiency and reduces operational costs
2Measurement precision
If manual processes are used for associating on-board units with vehicles and creating geofences, then system implementation is simpler, but errors increase and precision decreases
Solution Approach 1:
The system implements continuous feedback loops where machine learning models are trained on historical telematics data and continuously refined based on new data from vehicle sensors, GPS locations, and operational patterns. This feedback mechanism ensures increasingly accurate associations between OBUs and vehicles while dynamically adjusting geofence boundaries based on actual vehicle behavior patterns
Solution Approach 2:
The automated system performs self-validation and error correction by cross-referencing multiple data sources including telematics sensor data, GPS trajectories, and vehicle identification information. The machine learning algorithms automatically detect and correct association errors without human intervention, maintaining high precision through self-learning from data inconsistencies
3Ease of operation
If automated machine learning processes are used to associate on-board units with vehicles and create geofences, then operational efficiency increases and errors are minimized, but system complexity increases
Solution Approach 1:
The system provides fully automated self-service onboarding where new vehicles and on-board units are automatically associated through machine learning analysis of telematics data patterns. Geofences are dynamically created and updated without manual configuration, making the operational process simple for users while the complex machine learning infrastructure handles the automation in the background
Solution Approach 2:
The machine learning platform serves multiple functions simultaneously: it associates OBUs with vehicles, creates geofences, validates data accuracy, and continuously learns from new patterns. This multi-functional universal system consolidates what would otherwise require separate manual processes into a single automated platform, improving ease of operation despite the underlying complexity
Data Source
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AI summary
The present teachings relate to a method of creating a geofence for a first location comprising receiving a time that a first vehicle was at the first location from a fleet management system, receiving a time that the first vehicle was at a second location from an on-board telematics unit of the first vehicle, determining if the first vehicle was at the second location for a predetermined period of time before or after being at the first location, and creating the geofence for the first location as including the second location if it is determined that the first vehicle was at the second location for the predetermined period of time before or after being at the first location.