Vehicle Sensor Data Coding for High-Throughput Wireline Links
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Solution Overview
Problem
Autonomous vehicles face challenges in processing high volumes of sensor data in real-time due to high data throughput requirements, leading to outdated data processing and increased material costs.
Innovation Solution
A device that generates coded packets from multiple data streams and transmits them through a wireline network to AI accelerator modules and memory units, utilizing vector packet coding to increase data throughput without increasing material costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensors generate high amount of data and stream to processing elements, then data processing capability is improved, but time expenditure increases and material costs increase
Solution Approach 1:
The patent segments data streams by type (e.g., camera data, LIDAR data, radar data) and routes them through different processing paths. Each data type is handled by specialized processing elements optimized for that specific data format, reducing overall processing time and enabling parallel processing of multiple data streams simultaneously.
Solution Approach 2:
The patent introduces a new dimension of data handling by implementing a multi-layer processing architecture with different processing priorities. Time-critical data (e.g., obstacle detection) is processed in a high-priority path with lower latency, while less time-sensitive data (e.g., environmental mapping) is processed in a lower-priority path, effectively adding a temporal dimension to data processing.
2Productivity
If sensors generate high amount of data and stream to processing elements, then data processing capability is improved, but material costs increase
Solution Approach 1:
The patent implements universal processing elements that can handle multiple data types through software configuration rather than requiring dedicated hardware for each sensor type. A single processing element can be dynamically allocated to process camera data, LIDAR data, or radar data based on real-time needs, reducing the total number of processing elements required and thereby reducing material costs.
Solution Approach 2:
The patent merges multiple data streams of the same type into consolidated processing pipelines. Instead of having separate processing paths for each individual sensor output, multiple camera feeds are combined into a single processing stream, and multiple LIDAR streams are aggregated, reducing redundancy in processing infrastructure and lowering material costs.
3Loss of information
If data processing is performed in real-time, then data currency is improved, but processing speed requirements increase leading to higher costs
Solution Approach 1:
The patent performs preliminary processing actions on data streams before they reach the main processing elements. Data pre-processing steps such as filtering, noise reduction, and feature extraction are executed in advance, reducing the computational burden on real-time processing elements and enabling faster processing without sacrificing data currency.
Data Source
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AI summary
A device for a vehicle may include a first wireline interface configured to receive a first data stream from a first sensor having a first sensor type for perceiving a surrounding of the vehicle, the first data stream including raw sensor data detected by the first sensor; a second wireline interface configured to receive a second data stream from a second sensor having a second sensor type for perceiving the surrounding of the vehicle, the second data stream including raw sensor data detected by the second sensor; one or more processors configured to generate a coded packet including the received first data stream and the received second data stream by employing vector packet coding on the first data stream and the second data stream; and an output wireline interface configured to transmit the generated coded packet to one or more target units of the vehicle.