Distance-Based Image Downsampling for Faster AV Perception
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Solution Overview
Problem
Autonomous vehicles face a tradeoff between capturing high-resolution images for detailed environmental data and processing this data efficiently, as high-resolution images require significant computing resources, which can hinder navigation performance.
Innovation Solution
A method and system for downsampling images into multiple resolutions by identifying and processing distinct sections of an image based on distance ranges from the camera, using LiDAR, RADAR, and image analysis to generate distance maps, and applying different resolutions to each section, thereby reducing data volume and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images are captured to obtain detailed environmental data, then measurement precision is improved, but device complexity and processing time increase
Solution Approach 1:
The image processing task is segmented into multiple resolution levels. The system divides the original high-resolution image into multiple sections and processes each section at a different resolution level, rather than processing the entire image at one uniform resolution. This segmentation allows the system to maintain measurement precision for critical areas while reducing overall processing complexity.
Solution Approach 2:
Different sections of the image are assigned different quality levels based on their importance. The system identifies and processes specific regions of interest at higher resolutions while using lower resolutions for less critical areas. This local quality approach ensures that measurement precision is maintained where needed without unnecessarily increasing processing complexity across the entire image.
2Measurement precision
If high-resolution images are captured to obtain detailed environmental data, then measurement precision is improved, but processing time increases
Solution Approach 1:
The processing time is reduced through segmentation by dividing the image into multiple sections that can be processed independently and in parallel. Each section is processed at an appropriate resolution level, allowing the system to maintain measurement precision for important features while significantly reducing the total processing time compared to processing the entire high-resolution image sequentially.
Solution Approach 2:
The system applies partial action by processing only the necessary portions of the image at high resolution rather than the entire image. By identifying regions of interest and processing only those areas at full resolution while using lower resolutions for other areas, the system maintains measurement precision for critical data while reducing overall processing time.
3Loss of information
If high-resolution images are captured to obtain detailed environmental data, then information completeness is improved, but computing resources required increase
Solution Approach 1:
Computing resources are optimized through segmentation by processing different image sections at different resolution levels. The system identifies critical regions that require detailed information and allocates higher computing resources to those specific areas, while using fewer resources for processing less critical regions. This segmented approach maintains information completeness for important environmental data while significantly reducing overall computing resource consumption.
Solution Approach 2:
The system applies local quality by varying the information density and processing quality across different image regions based on their importance. High-information areas that are critical for environmental understanding are processed at full resolution with complete detail, while other areas use reduced resolution. This ensures information completeness is maintained where necessary without unnecessarily consuming computing resources across the entire image.
Data Source
AI summary
Systems and methods are provided of downsampling an image to a plurality of image resolutions. The method comprises capturing, using a camera, an image depicting an environment within view of the camera, identifying a first section of the image depicting an area of the environment spaced within a first distance range from the camera, identifying a second section of the image depicting an area of the environment spaced within a second distance range from the camera, identifying a third section of the image depicting an area of the environment spaced within a third distance range from the camera, and downsampling the first section of the image to a first image resolution, generating a first processed image, the second section of the image to a second image resolution, generating a second processed image, and the third section of the image to a third image resolution, generating a third processed image.