Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving

TL;DR

This paper introduces an edge network-assisted framework for real-time object detection in autonomous vehicles, reducing latency by transmitting only regions of interest, thus ensuring timely and accurate detection despite variable channel conditions.

Contribution

The proposed EODF framework enables real-time object detection by adaptive image compression based on channel quality, improving latency and accuracy in autonomous driving scenarios.

Findings

EODF achieves real-time object detection with reduced latency.

The framework maintains satisfactory detection accuracy.

It effectively handles dynamic channel quality variations.

Abstract

Autonomous vehicles (AVs) can achieve the desired results within a short duration by offloading tasks even requiring high computational power (e.g., object detection (OD)) to edge clouds. However, although edge clouds are exploited, real-time OD cannot always be guaranteed due to dynamic channel quality. To mitigate this problem, we propose an edge network-assisted real-time OD framework~(EODF). In an EODF, AVs extract the region of interests~(RoIs) of the captured image when the channel quality is not sufficiently good for supporting real-time OD. Then, AVs compress the image data on the basis of the RoIs and transmit the compressed one to the edge cloud. In so doing, real-time OD can be achieved owing to the reduced transmission latency. To verify the feasibility of our framework, we evaluate the probability that the results of OD are not received within the inter-frame duration (i.e., outage probability) and their accuracy. From the evaluation, we demonstrate that the proposed EODF provides the results to AVs in real-time and achieves satisfactory accuracy.