Vehicle Sensor Data Compression Using Forecast Network Conditions
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Solution Overview
Problem
Existing data communication systems for vehicles face challenges in reliably transmitting sensor data to remote locations due to variability in network conditions, which can impact safety and functionality.
Innovation Solution
A method and system for dynamic management of vehicle sensor data that involves obtaining sensor data feeds, generating forecast network conditions, and using these conditions to compress data for reliable communication over variable networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is transmitted at high quality and volume, then data accuracy and completeness are improved, but network reliability deteriorates under variable network conditions
Solution Approach 1:
The patent implements dynamic data management by adjusting compression levels, sampling rates, and data transmission priorities based on forecasted network conditions. The system transitions from static data handling to dynamic adaptation, modifying data characteristics in real-time according to predicted network performance to maintain both accuracy and reliability.
Solution Approach 2:
The system performs preliminary compression and data processing based on forecasted network conditions before transmission occurs. By predicting future network state and pre-adjusting data parameters accordingly, the system proactively ensures reliable transmission without sacrificing essential data quality, resolving the contradiction between accuracy and reliability.
2Reliability
If data compression is increased to adapt to network limitations, then network reliability is improved, but data quality deteriorates
Solution Approach 1:
The patent applies different compression levels and quality settings to different portions of sensor data based on their importance and the forecasted network conditions. Critical safety-related data maintains high quality with minimal compression, while non-critical data undergoes higher compression, allowing the system to achieve overall communication reliability without uniformly degrading all data quality.
Solution Approach 2:
The system dynamically changes multiple data parameters including compression ratio, resolution, sampling frequency, and transmission priority based on forecasted network conditions. This multi-parameter adjustment allows the system to optimize the balance between communication reliability and data quality, applying appropriate compression only when and where necessary.
3Speed
If real-time data transmission is maintained during network degradation, then data freshness is improved, but system stability deteriorates
Solution Approach 1:
The patent implements periodic assessment of forecasted network conditions and adjusts data transmission strategies accordingly. Rather than maintaining constant real-time transmission, the system periodically evaluates predicted network state and modifies transmission intervals, batching data when conditions are poor while maintaining freshness through intelligent scheduling, thus preserving system stability.
Solution Approach 2:
The system uses forecasted network condition information as feedback to dynamically adjust transmission behavior. This feedback loop allows the system to anticipate network degradation and proactively modify data flow, preventing instability by adapting transmission speed and frequency based on predicted network performance rather than reacting after degradation occurs.
Data Source
AI summary
Data management of vehicle data for communication to a remote location is provided, including obtaining one or more sensor data feeds from one or more onboard sensors of a vehicle, generating a forecast network condition for a network with which the vehicle is in operative communication, the forecast network condition representing predicted network resources the network is predicted to provide to the vehicle, providing the forecast network condition to a compression engine, and compressing the one or more sensor data feeds into a compressed data feed for communication over the network to a remote location, wherein the compressed data feed is compressed based on the forecast network condition.