Remote Vehicle Image Segmentation Under Bandwidth Constraints
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Solution Overview
Problem
Current vehicle image segmentation systems face challenges in processing and exchanging large amounts of image data efficiently, leading to high latency and reduced image quality due to the need for high-compression rates, which are not effectively addressed by existing V2V, V2I, and V2N architectures.
Innovation Solution
A remote vehicle image segmentation system that employs an image encoder on the vehicle to convert high-resolution raw images into low-volume masked images, which are then processed by a convolutional neural network (CNN) and decoded using a matrix completion algorithm to recover segmented target objects, reducing computational power and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If image data is transmitted at high compression rates to reduce bandwidth usage, then data transmission efficiency is improved, but image quality deteriorates
Solution Approach 1:
The image is divided into multiple patches, and only selected patches are transmitted at full resolution while others are transmitted at lower resolution or skipped entirely. This selective segmentation allows the system to reduce overall data transmission volume while preserving critical image quality in important regions.
Solution Approach 2:
Different regions of the image are assigned different quality levels based on their importance. Critical regions (such as those containing traffic signals, pedestrians, or obstacles) are transmitted with high quality, while less important regions are transmitted with lower quality or skipped, optimizing the trade-off between data volume and image quality.
2Manufacturing precision
If high-resolution raw images are transmitted to maintain image quality, then image quality is preserved, but data transmission bandwidth requirements increase
Solution Approach 1:
Instead of transmitting the complete high-resolution image, the system transmits only a partial set of image patches that are sufficient for the intended application. This partial action approach maintains adequate image quality for autonomous driving decisions while significantly reducing the data volume that would be required to transmit the full-resolution image.
3Loss of time
If image segmentation is performed locally in the vehicle to reduce data transmission, then processing speed is improved, but computational power requirements increase
Solution Approach 1:
The image processing task is segmented into multiple stages: initial processing and patch selection are performed locally in the vehicle with limited computational resources, while more computationally intensive segmentation and reconstruction are performed remotely on the server. This distributed segmentation approach balances the computational load between the vehicle and server.
Solution Approach 2:
A server acts as an intermediary between the vehicle and the final segmentation result. The server receives compressed image data from the vehicle, performs the computationally intensive segmentation processing, and returns the segmented results. This intermediary approach allows the vehicle to offload heavy computational tasks while maintaining processing speed.
Data Source
AI summary
A vehicle communication and control system includes a first vehicle in signal communication with a remote computing system and/or a second vehicle. The first vehicle includes a sensor configured to capture a raw image having a first image volume and including at least one target object. An image encoder included in the vehicle converts the raw image into a masked image having a second image volume that is less than the first image volume. A segmentation unit included in the remote computing system and/or the second vehicle determines the at least one target object from the masked image, generates a masked segmented image including a sparse segmentation of the at least one target object, and converts the sparse segmentation of the at least one target object into at least one recovered segmented target object indicative of the at least one target object.