Dynamic Vehicle Data Configuration Adjustment
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Solution Overview
Problem
Current vehicle data collection and reporting systems incur significant costs due to unnecessary data gathering and transmission, especially in fleet management, where not all vehicles require intensive data collection at all times, leading to inefficient resource utilization and increased overhead.
Innovation Solution
A system where a vehicle processor dynamically adjusts data-gathering and reporting parameters based on context-specific configurations received wirelessly, using data snapshots and geofencing to correlate gathered data with predefined conditions, allowing for on-demand reconfiguration of data collection and transmission protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If intensive data gathering is continuously performed by all vehicles, then data availability for fleet management is improved, but data overhead costs and resource utilization increase significantly
Solution Approach 1:
The system dynamically adjusts data gathering and reporting parameters based on real-time context conditions. Vehicles transition between different data collection modes (intensive vs. minimal) depending on whether they are within defined contexts such as geofences, near points of interest, or experiencing specific events. This dynamic adaptation ensures data is collected intensively only when necessary, reducing overall data overhead while maintaining data availability when needed.
Solution Approach 2:
The system changes data gathering parameters (frequency, volume, type of data) and reporting parameters based on contextual conditions. Configuration data defines multiple parameter sets that are applied depending on the vehicle's current context. For example, a vehicle may switch from continuous high-frequency data collection to periodic low-frequency collection based on location, task status, or environmental conditions, thereby optimizing the balance between data availability and resource consumption.
2Reliability
If all vehicles report data continuously to fleet central location, then real-time fleet tracking is improved, but network resource consumption and operational costs increase
Solution Approach 1:
Instead of continuous data reporting, the system implements periodic reporting based on contextual triggers. Vehicles report data at intervals defined by configuration parameters, but these intervals are dynamically adjusted based on whether the vehicle is within a defined context. When outside contexts, reporting may be minimal or periodic; when inside contexts (such as entering a geofence or detecting a significant event), reporting frequency increases. This periodic action maintains fleet tracking reliability while reducing network resource consumption during normal operations.
Solution Approach 2:
The system pre-defines contexts and associated reporting parameters before vehicles operate in the field. Configuration data is provided in advance that specifies multiple contexts (geofences, points of interest, task conditions) and the corresponding data gathering and reporting parameters for each. This preliminary configuration allows vehicles to autonomously determine when to increase or decrease reporting based on their current context, ensuring real-time tracking is maintained when needed without requiring continuous network communication.
3Device complexity
If data gathering parameters are fixed for all vehicles, then system complexity is reduced, but adaptability to different operational contexts is limited
Solution Approach 1:
The system provides universal configuration data that can be applied across all vehicles in the fleet, yet this configuration includes multiple parameter sets for different contexts. Each vehicle receives a comprehensive configuration that defines various contexts (geofences, points of interest, task types) and the corresponding data gathering and reporting parameters for each. This universal configuration enables any vehicle to adapt to any context it encounters, providing context-specific adaptability without requiring vehicle-specific customization, thereby maintaining relatively simple system architecture while achieving high versatility.
Data Source
AI summary
A vehicle wirelessly receives configuration data defining data-gathering and data-reporting parameters associated with at least one context. The vehicle defines data to be gathered by the vehicle related to the context and, as the vehicle travels, gathers the defined data. The vehicle compares the gathered data to the at least one context to determine if there is a correlation between at least one of the at least one contexts and the gathered data, and responsive to there being a correlation, sets vehicle data-gathering and data-reporting based on the parameters associated with the context to which the gathered data correlates.