Multi-Source Vehicle Data Correlation for Manipulation Detection
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Solution Overview
Problem
Existing vehicle monitoring systems rely on a single source of information for evaluating driving data, which is costly, tamper-prone, and lacks reliability for determining manipulation, failing to meet regulatory requirements for multiple data sources.
Innovation Solution
A method and controller that process vehicle data from multiple sources, grouping and correlating data to create indices indicating manipulation levels, with dynamic weighting based on relevance and importance, ensuring secure storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single source of information is used to evaluate vehicle speed, then the system is simpler and less costly, but the reliability and tamper-resistance of the data deteriorates
Solution Approach 1:
The patent combines multiple independent speed measurement sources (tachograph, ELD, and vehicle sensors) into a unified evaluation system. By merging data from these diverse sources, the system achieves higher reliability and tamper-resistance, as the contradiction shows that no single source is fully trusted but together they provide robust verification.
Solution Approach 2:
The controller performs multiple functions: it acts as a tachograph, an ELD, and a central correlation engine simultaneously. This multi-functionality allows the system to use existing infrastructure while adding the capability to cross-validate data from multiple sources, resolving the contradiction between reliability and complexity.
2Measurement precision
If multiple sources of vehicle data are processed and correlated, then the determination of manipulation becomes more reliable, but the processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the data processing task by creating separate evaluation modules for different data sources (tachograph evaluation, ELD evaluation, sensor evaluation). Each module independently processes its specific data type and generates intermediate results, which are then correlated. This segmentation reduces processing complexity by breaking down the complex task into manageable, specialized components.
Solution Approach 2:
The controller acts as an intermediary that receives data from multiple sources, correlates them against each other, and produces a unified manipulation determination. This intermediary role simplifies the overall system architecture by centralizing the complex correlation logic in a single component rather than requiring complex interactions between multiple independent systems.
3Measurement precision
If dynamic weighting of data sources is implemented, then the evaluation adapts to different conditions and becomes more accurate, but the computational overhead increases
Solution Approach 1:
The patent implements dynamic weighting where the importance of each data source varies based on operating conditions. For example, certain sensors may be weighted higher during specific vehicle operations or when certain data sources are known to be more reliable in particular conditions. This dynamic adaptation improves evaluation accuracy without requiring constant recalculation of all weights, as the system can adjust weights based on pre-defined criteria or gradual learning.
Data Source
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AI summary
The invention relates to a method of collecting and evaluating multiple sources of vehicle driving data and system thereof. The multiple sources of vehicle data is correlated based on associated groups, a first index is generated for each associated group and the first indexes are weighted, a second overall index representative for the level of manipulation of vehicle data is at last created based on the weighted indexes.