Vehicle Data Extraction Service Reducing Bandwidth
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Solution Overview
Problem
Modern vehicles generate vast amounts of data from various sensors, leading to bandwidth and storage challenges, especially in fleets with heterogeneous communication formats, where redundant data collection is common due to vehicles moving in platoons or waves, necessitating an efficient data reduction method to optimize data transfer and storage.
Innovation Solution
A vehicle information extraction service that decouples vehicle model configuration from in-vehicle communication signal configuration, allowing for dynamic data reduction based on vehicle density, mobility patterns, and environmental factors, using a unified signal representation across different vehicle models, and applies data reduction factors through vehicle scheme packets to minimize redundant data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is collected from all vehicles in the fleet continuously, then complete data coverage is achieved, but network bandwidth and storage resources are overwhelmed due to redundant data from vehicles moving in platoons or waves
Solution Approach 1:
The system applies data reduction factors to collect only a portion of the data that would otherwise be collected from all vehicles. By determining that vehicles in platoons or waves generate redundant data, the system selectively reduces data collection from these groups while maintaining collection from isolated vehicles, achieving partial action that optimizes resource usage without sacrificing critical information coverage
Solution Approach 2:
The data reduction factors are dynamically adjusted based on real-time vehicle density, mobility patterns, and environmental factors. The system continuously monitors vehicle trajectories and communication formats, adapting the reduction factors to current fleet conditions, thereby optimizing the balance between data coverage and resource consumption under varying operational scenarios
2Measurement precision
If data collection scheme is customized for each vehicle model, then data extraction accuracy is improved, but system complexity increases due to heterogeneous communication formats across different manufacturers
Solution Approach 1:
The system implements a universal data extraction service that handles multiple vehicle models and communication formats through a single unified interface. By developing a framework that can process different signal formats (CAN, LIN, Ethernet, proprietary protocols) and vehicle architectures through common data reduction algorithms and trajectory analysis, the system achieves multi-functionality that eliminates the need for separate customization processes for each vehicle model while maintaining extraction accuracy
3Productivity
If data reduction factors are applied dynamically based on vehicle density and mobility patterns, then resource optimization is achieved, but computational overhead increases for tracking and analyzing fleet patterns
Solution Approach 1:
The system performs preliminary analysis of vehicle trajectories and mobility patterns to pre-identify platoons and waves before applying data reduction. By detecting correlated movement patterns and grouping vehicles into logical clusters in advance, the system prepares the data structure needed for efficient reduction factor application, reducing the computational burden during real-time operation and minimizing processing time overhead
Data Source
AI summary
A system comprising one or more computing devices implements a vehicle information extraction service. The vehicle information extraction service enables customers to optimize an amount of relevant vehicle sensor information extracted from vehicles by reducing instances of collection of redundant data. The vehicle information extraction service additionally, or alternatively, enables customers to maintain a model of a fleet of vehicles and determine number of the vehicles of the fleet in a certain partition to calculate a data reduction factor that will filter out sensor data. The vehicle information extraction service communicates the reduction factor to the vehicles in the geographical region using a vehicle scheme to indicate to the vehicles the probability with which the vehicle is to transmit a particular type of sensor data.