Multi-Camera Image Stitching for Bitrate-Limited Remote Driving
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The quality of images transmitted from autonomous vehicles to remote operations systems is often compromised due to network bitrate limitations, affecting the ability of remote operators to make informed decisions for safe vehicle operation, especially in scenarios requiring clear environmental visibility.
Innovation Solution
An image processing system within the autonomous vehicle prioritizes and enhances image quality by stitching images from multiple cameras, applying selective blurring to less important regions, and encoding pixel areas based on complexity levels to manage network bitrate effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If images are transmitted from autonomous vehicles to remote operations systems, then remote operators can make informed decisions for safe vehicle operation, but network bitrate limitations compromise image quality
Solution Approach 1:
The patent applies different quality settings to different pixel areas within the same image. Critical regions (e.g., areas with hazards, pedestrians, or important environmental features) are transmitted at high quality, while less critical regions are transmitted at lower quality. This resolves the contradiction by maintaining high image quality where needed for safety decisions while reducing overall network bitrate consumption.
Solution Approach 2:
The patent segments the image into multiple pixel areas with different complexity levels and transmits them separately with different quality settings. This allows the system to optimize network usage by allocating bitrate resources to high-priority segments while using minimal resources for low-priority segments, thereby maintaining reliability for critical information while managing overall network bitrate.
2Loss of information
If multiple camera images are stitched together to create comprehensive environmental views, then remote operators gain better situational awareness, but processing complexity and transmission data volume increase
Solution Approach 1:
After stitching multiple camera images to provide comprehensive environmental visibility, the system applies local quality optimization by identifying and enhancing only the critical pixel areas that contain important environmental information. This maintains complete environmental visibility while reducing the overall processing complexity and transmission burden by not uniformly processing all image regions at high quality.
Solution Approach 2:
The patent extracts and prioritizes only the essential environmental information from stitched multi-camera images for high-quality transmission. By identifying and separating critical regions (such as areas containing hazards, traffic signals, or pedestrians) from the rest of the image, the system maintains comprehensive environmental awareness while reducing processing complexity for non-critical areas.
3Measurement precision
If high-quality image encoding is applied to all pixel areas, then remote operators receive clear visual information, but network bandwidth consumption increases significantly
Solution Approach 1:
The patent implements differential quality encoding where different pixel areas are encoded at different quality levels based on their importance. Critical areas (containing hazards, pedestrians, or decision-relevant features) are encoded at high clarity, while non-critical areas are encoded at lower quality. This resolves the contradiction by providing clear visual information where needed while significantly reducing overall network bandwidth consumption.
Solution Approach 2:
The system dynamically changes the encoding quality parameter for different regions of the image based on detected content importance. By adjusting the quality parameter locally rather than applying a uniform quality setting across the entire image, the patent achieves high measurement precision for critical visual information while optimizing network bandwidth usage through reduced quality in non-critical regions.
Data Source
AI summary
Techniques for image quality enhancement for autonomous vehicle remote operations are disclosed herein. An image processing system of an autonomous vehicle can obtain images captured by at least two different cameras and stitch the images together to create a combined image. The image processing system can apply region blurring to a portion of the combined image to create an enhanced combined image, e.g., to blur regions/objects determined to be less import (or unimportant) for the remote operations. The image processing system can encode pixel areas of the enhanced combined image using a corresponding quality setting for respective pixel areas to create encoded image files, e.g., based on complexity levels of the respective pixel areas. The image processing system can transmit the encoded image files to a remote operations system associated with the autonomous vehicle for remote operations support.