Edge-Driven Object Detection for Low-Latency Vehicle MR
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Solution Overview
Problem
Current methods for mixed reality (MR) applications in vehicle environments face challenges with high latency and bandwidth usage due to the need for remote processing of computationally intensive tasks like object detection, which can lead to resource over-utilization and degrade user experience.
Innovation Solution
The implementation of an edge-driven object detection system that utilizes edge computing to perform object detection tasks close to the data source, reducing the need for large data transmission and minimizing the computation results sent back to the reality devices, thereby optimizing resource utilization and reducing latency and bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If remote cloud processing is used for object detection, then computational power is improved, but latency and bandwidth usage increase
Solution Approach 1:
An edge server is introduced as an intermediary between reality devices and remote cloud servers. The edge server performs object detection locally, reducing the distance data must travel and minimizing latency while maintaining access to cloud-based computational resources when needed.
Solution Approach 2:
The system segments processing tasks by handling object detection at the edge rather than requiring all data to travel to remote cloud servers. This segmentation allows critical real-time processing to occur locally while non-critical tasks can use remote resources.
2Power
If remote cloud processing is used for object detection, then computational power is improved, but bandwidth usage increases
Solution Approach 1:
The edge server acts as a local processing intermediary that handles object detection tasks without requiring continuous data transmission to remote cloud servers, thereby significantly reducing bandwidth consumption while maintaining detection capabilities.
Solution Approach 2:
The edge server enables self-service processing by handling object detection locally without requiring constant communication with remote servers, reducing the need for bandwidth-intensive data transmission while maintaining autonomous detection functionality.
3Loss of time
If local processing is used in reality devices, then latency is reduced, but device complexity increases
Solution Approach 1:
The edge server serves as an intermediary that offloads complex object detection processing from reality devices, allowing low-latency local inference while maintaining simpler device architecture by leveraging external computational resources when needed.
Data Source
AI summary
System and method for reducing latency and bandwidth usage include a reality device and one or more processors. The reality device includes a camera to operably capture a frame of a view external to a vehicle. The one or more processors are operable to send the frame to an edge server, receive object detection data from the edge server, wherein the object detection data includes object information in the frame, and instruct the reality device to render a mixed reality environment with the object detection data.