Correlated Sensor Data Compression for Near-Lossless Vehicle Streams
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Solution Overview
Problem
Current data compression methods for autonomous vehicles, which rely on multiple unlinked sensors, face inefficiencies in processing and transmitting large datasets, particularly in achieving near-lossless compression without prior knowledge of data distribution, leading to resource constraints and potential information loss.
Innovation Solution
A method and system for near-lossless universal data compression using correlated data sequences from multiple sensors, employing context tree weighted (CTW) compression algorithms to construct encoding and decoding context trees, determining statistical correlations, and formulating mapping functions to transform data sequences, thereby reducing data cardinality and optimizing compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from multiple unlinked sensors is compressed separately using traditional lossless compression methods, then compression is achieved without prior knowledge of data distribution, but the compression ratio is suboptimal and resource consumption increases
Solution Approach 1:
The patent combines multiple independent data sequences from unlinked sensors into a joint compression framework. Instead of compressing each sensor's data separately, the system merges the data streams and applies a unified compression algorithm that exploits correlations between sensors, thereby improving compression efficiency while maintaining universal adaptability.
Solution Approach 2:
The invention employs a universal compression algorithm that does not require prior knowledge of data distribution characteristics. The context tree weighted coding mechanism is designed to adapt to any data source universally, making the compression system multi-functional and applicable to various sensor types without reconfiguration.
2Reliability
If traditional lossless compression is applied to sensor data, then no information is lost, but the data size remains large requiring more storage and transmission resources
Solution Approach 1:
By merging multiple correlated data sequences into a joint compression process, the system achieves better compression ratios while maintaining lossless reconstruction. The correlations between sensors are exploited to reduce the total data size without sacrificing any information, thereby resolving the contradiction between information preservation and data size reduction.
3Productivity
If distributed compression is used where multiple sensors compress data independently, then processing can be parallelized, but the overall compression performance is limited by lack of inter-sensor correlation exploitation
Solution Approach 1:
The patent introduces a central coordinator or intermediary that collects data from multiple sensors and performs joint compression. This intermediary enables the system to exploit inter-sensor correlations that would be missed in fully distributed compression, achieving optimal compression performance while still allowing parallel data collection at the sensor level.
Data Source
AI summary
A method of near-lossless universal data compression using correlated data sequences includes detecting first target surroundings via a first sensor, encoding a first data sequence indicative of the detected target surroundings, and communicating to an electronic controller, the encoded first data sequence. The method additionally includes detecting the first target surroundings via a second sensor, and encoding a second data sequence indicative of the target surroundings detected by the second sensor. The method also includes communicating the encoded second data sequence to the controller. The method additionally includes decoding, via the controller, the encoded first and second data sequences. The method also includes, via the controller, determining a statistical correlation between the decoded first and second data sequences and formulating a mapping function having reduced cardinality and indicative of the determined statistical correlation. Furthermore, the method includes feeding back the mapping function by the controller to the first processor.