Telematics Visualization System Using Data Extraction and Segmentation
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Solution Overview
Problem
Conventional telematics systems face challenges in analyzing and visualizing large volumes of vehicle operation data, leading to performance issues and inability to provide meaningful representations for various applications.
Innovation Solution
The use of big data techniques to analyze and visualize telematics data, including driving-related information from onboard sensors or cameras, using imaging-based systems that aggregate and plot data on maps, charts, and other visualizations to identify trends and behaviors, and determine risk profiles for vehicles and drivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional telematics systems collect and store large volumes of vehicle operation data, then the quantity of data available for analysis increases, but the system performance deteriorates and the ability to provide meaningful representations is lost
Solution Approach 1:
The patent extracts and separates the visualization function from the data collection system. A dedicated visualization system retrieves only the necessary aggregated data from the telematics system, leaving the bulk data storage and processing to specialized systems. This extraction resolves the contradiction by allowing large data volumes to be stored without impacting the performance of the visualization system, as it only accesses processed summaries rather than raw data.
2Loss of information
If conventional telematics systems store detailed records for each vehicle with thousands of data points, then the completeness of data is improved, but the difficulty of drawing meaningful conclusions increases
Solution Approach 1:
The patent replaces manual or conventional analysis methods with automated image processing and pattern recognition algorithms. The visualization system automatically processes aggregated telematics data, generates visual representations, and identifies patterns without human intervention. This substitution resolves the contradiction by maintaining complete data while using automated systems to overcome the difficulty of manual analysis of large datasets.
Solution Approach 2:
The patent introduces an intermediary aggregation layer between the raw telematics data and the analysis process. Data is aggregated and summarized into meaningful metrics before visualization, serving as a mediator that preserves the completeness of original data while making it analyzable. This intermediary layer resolves the contradiction by transforming raw data into a form that is both complete and analyzable.
3Measurement precision
If conventional telematics systems monitor individual vehicle movements and operations, then the precision of vehicle tracking is improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the telematics system into distinct functional modules: data collection, data aggregation, data visualization, and analysis. Each module handles specific tasks independently, reducing overall system complexity. The visualization system is one such module that receives processed data and generates representations without needing to handle raw data processing, thus maintaining precision while reducing complexity through functional segmentation.
Data Source
AI summary
Systems and methods are described for the visualization of vehicular-based telematics data. In various aspects, telematics data may be aggregated for a plurality of vehicles where the telematics data can include telematics data observation(s) for each vehicle. Each observation can indicate a coordinate value of the vehicle and a timestamp for the observation, and can further indicate any of a device identifier for a telematics device associated with the vehicle, a speed value of the vehicle, a g-force value of the vehicle, a trip identifier associated with the vehicle, a distance value of the vehicle, or a stop indicator value of the vehicle. A visualization may also be generated based on at least a subset of the telematics data such that the visualization can indicate one or more image features associated with the one or more of the plurality of vehicles.