Edge-Assisted Lightweight Region-of-Interest Extraction and Transmission for Vehicle Perception

TL;DR

This paper presents an edge-assisted, lightweight method for extracting and transmitting regions of interest in high-resolution vehicle camera videos, improving accuracy and reducing data transmission for autonomous driving perception.

Contribution

It introduces a novel CAM-based lightweight RoI extraction method combined with an adaptive offloading algorithm tailored for limited on-board and edge resources.

Findings

Improves perception accuracy by up to 16%.

Reduces data transmission by up to 49%.

Enhances real-time processing for autonomous vehicles.

Abstract

To enhance on-road environmental perception for autonomous driving, accurate and real-time analytics on high-resolution video frames generated from on-board cameras be-comes crucial. In this paper, we design a lightweight object location method based on class activation mapping (CAM) to rapidly capture the region of interest (RoI) boxes that contain driving safety related objects from on-board cameras, which can not only improve the inference accuracy of vision tasks, but also reduce the amount of transmitted data. Considering the limited on-board computation resources, the RoI boxes extracted from the raw image are offloaded to the edge for further processing. Considering both the dynamics of vehicle-to-edge communications and the limited edge resources, we propose an adaptive RoI box offloading algorithm to ensure prompt and accurate inference by adjusting the down-sampling rate of each box. Extensive experimental results on four high-resolution video streams demonstrate that our approach can effectively improve the overall accuracy by up to 16% and reduce the transmission demand by up to 49%, compared with other benchmarks.