Vehicular Network Data Sampling for Differential Compression
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Solution Overview
Problem
Current data compression methods are inadequate for efficiently transmitting large volumes of high-definition digital audio and video data, leading to delays due to increased processor complexity and inability to compress Lidar data effectively.
Innovation Solution
A method and system for sampling and converting vehicular network data that involves differential sampling using a reference time sequence to generate a differential data table, which is then compressed and uploaded, reducing data volume and processor complexity while enabling Lidar data compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data compression methods are used on high-definition digital audio and video data, then data transmission speed is limited, but transmission time becomes too long resulting in delay
Solution Approach 1:
The patent applies preliminary action by performing differential sampling and data table generation before compression. The system pre-processes the original data into a differential data table format, which reduces the amount of data requiring compression and transmission, thereby decreasing overall transmission time and delay
Solution Approach 2:
The patent changes data representation parameters by converting original data into differential values relative to a reference time sequence. This parameter transformation reduces data redundancy and enables more efficient compression, allowing faster transmission without quality loss
2Productivity
If CAN data is grouped in multiple groups by using one Byte as a unit for compression, then compression is achieved, but the complexity of the processor pre-operation is increased
Solution Approach 1:
The patent extracts only the essential differential information from the original data, storing only changes relative to a reference sequence. This extraction approach reduces the data volume requiring processing while maintaining compression efficiency, thereby reducing processor pre-operation complexity
Solution Approach 2:
Instead of grouping data by fixed byte units as in conventional methods, the patent inverts the approach by organizing data around reference time sequences and storing differential values. This inversion simplifies the processing structure and reduces computational complexity while achieving effective compression
3Adaptability or versatility
If conventional compression methods are applied to Lidar data, then compression may be achieved, but the methods cannot be applied to Lidar data compression
Solution Approach 1:
The patent creates a universal compression framework based on differential sampling that can handle multiple data types including CAN data and Lidar data. The method uses a reference time sequence approach that is applicable to various vehicular data formats, achieving both versatility and efficiency across different data types
Data Source
AI summary
A method for sampling and converting vehicular network data is executed by a vehicle host. The vehicle host selects one of multiple data signals from an original signal, and establishes a data table. The vehicle host further determines whether the original signal includes any data signal remaining unselected. When the original signal does not include any data signal remaining unselected, the vehicle host differentially samples data in the data table corresponding to other time sequences by using the data in the data table corresponding to a first time sequence as a reference to generate a differential data table, and compresses the differential data table. The method can reduce the amount of data by performing differential sampling, so that the compression ratio of the data can be effectively improved, and the delay of data transmission can be avoided.