Vehicle Sensor Data Compression for Forecast Network Degradation
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Solution Overview
Problem
Existing data communication systems in autonomous or remote vehicle control face challenges in maintaining reliable data delivery due to network variability, leading to potential disruptions that can impact vehicle safety.
Innovation Solution
A method and system that utilize a network prediction module to forecast future network conditions, allowing for proactive data management and compression of sensor data feeds to ensure continuous and reliable communication to a remote location, even during network degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensor data is transmitted at full resolution and rate, then data quality and completeness are improved, but network bandwidth consumption increases and reliability decreases under variable network conditions
Solution Approach 1:
The system dynamically adjusts compression parameters and data transmission rates based on forecasted network conditions. The compression engine modifies its operation in real-time according to predicted bandwidth availability, transforming a static data transmission system into a dynamic one that adapts to changing network environments while maintaining optimal data delivery reliability
Solution Approach 2:
The system changes key parameters including compression ratio, data sampling rate, and transmission priority based on forecast network conditions. When network resources are predicted to be limited, the system increases compression and reduces data rate; when resources are abundant, it maintains higher data quality, thus resolving the contradiction between data completeness and delivery reliability
2Quantity of substance
If data compression is increased to reduce bandwidth usage, then network resource consumption is reduced, but data quality and information content decrease
Solution Approach 1:
The system performs preliminary compression of sensor data before transmission based on forecast network conditions. By pre-compressing data according to predicted bandwidth requirements, the system reduces the data size to fit anticipated network capacity while preserving essential information, thus resolving the contradiction between bandwidth usage and data quality
Solution Approach 2:
The system replaces physical data reduction (selectively transmitting only critical data) with computational compression (algorithmically reducing data size while preserving information). The compression engine uses sophisticated algorithms to maintain data quality while significantly reducing bandwidth consumption, overcoming the traditional trade-off
3Speed
If reactive data management is used to respond to current network conditions, then response time is reduced, but data delivery reliability decreases due to network variability and disruptions
Solution Approach 1:
The system performs preliminary adjustments to data transmission parameters based on forecasted network conditions before actual network degradation occurs. By proactively modifying compression and transmission settings in advance, the system maintains continuous reliable data delivery without waiting for network disruptions, thus resolving the contradiction between response time and delivery reliability
Solution Approach 2:
The system takes preliminary anti-action by preparing alternative data transmission strategies in advance of predicted network failures. When network degradation is forecasted, the system has already adjusted its data management approach, preventing disruptions before they occur and maintaining reliable continuous delivery
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.