Camera Pre-Processing Hardware for Multi-Camera Calibration
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Solution Overview
Problem
Autonomous driving vehicles face significant processing power challenges due to the varying calibrations and formats of multiple cameras, which can lead to missed details or dropped images, especially at high speeds, requiring substantial computation resources to process images in real-time.
Innovation Solution
The implementation of flexible pre-processing hardware, such as field programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), that offloads image processing tasks like region of interest processing, format conversion, and high dynamic range processing, reducing the computational burden on CPUs and GPUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If processors in the AD computer process images from multiple cameras with varying calibrations and formats, then image processing accuracy is improved, but processing speed deteriorates due to substantial computation resources required
Solution Approach 1:
The patent divides the image processing system into two segments: a hardware preprocessing stage using FPGAs/ASICs that handles format conversion and calibration for multiple cameras, and a software processing stage using CPUs/GPUs that handles high-level autonomous driving algorithms. This segmentation allows parallel processing where hardware handles time-consuming format conversions while software handles complex driving decisions, resolving the contradiction between processing accuracy and speed.
Solution Approach 2:
The patent introduces FPGA/ASIC hardware as an intermediary component between the cameras and the CPU/GPU system. This intermediary performs preliminary image processing including format conversion, calibration, and region of interest extraction, thereby reducing the computational burden on the main processors and enabling faster overall processing while maintaining accuracy.
2Reliability
If the number of cameras and images per second is increased, then detection capability is improved, but computational load increases substantially
Solution Approach 1:
The patent extracts and removes the preprocessing functions (format conversion, calibration, ROI extraction) from the main CPU/GPU processing pipeline and assigns them to dedicated hardware (FPGAs/ASICs). This extraction allows the system to handle multiple camera inputs simultaneously with reduced computational load on the main processors, as the hardware preprocessing occurs in parallel and independently.
Solution Approach 2:
The patent changes the processing parameters by implementing hardware-accelerated preprocessing that operates at different computational levels than software processing. The FPGA/ASIC hardware processes images at the pixel and format level with fixed, optimized parameters, while the CPU/GPU handles higher-level semantic processing, thereby managing computational load efficiently across multiple cameras.
3Adaptability or versatility
If pre-processing functions are performed by CPUs or GPUs, then processing flexibility is maintained, but processing power is insufficient for real-time requirements
Solution Approach 1:
The patent implements a dynamic processing architecture where FPGAs/ASICs provide deterministic, high-speed hardware acceleration for preprocessing tasks, while CPUs/GPUs provide flexible software-based processing for adaptive autonomous driving algorithms. The system dynamically allocates processing tasks between hardware and software components based on real-time requirements, maintaining flexibility while achieving the processing power needed for real-time operation.
Data Source
AI summary
A sensor unit includes a sensor interface, host interface, and pre-processing hardware. The sensor interface is coupled to a plurality of cameras configured to capture images around an autonomous driving vehicle (ADV). The host interface is coupled to a perception and planning system. The pre-processing hardware is coupled to the sensor interface to receive images from the plurality of cameras and to perform one or more pre-processing functions on the images and to transmit pre-processed images to the perception and planning system via the host interface. The perception and planning system is configured to perceive a driving environment surrounding the ADV based on the pre-processed images and to plan a path to control the ADV to navigate through the driving environment. The pre-processing functions can adjust for different calibrations and formats across the plurality of cameras.