Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception

TL;DR

This paper presents ECOD, an edge-enabled collaborative perception framework for CAVs that improves real-time multi-vehicle object detection accuracy through data aggregation and consensus voting, outperforming traditional methods.

Contribution

The paper introduces a novel edge-based framework with algorithms PACE and VOTE for real-time collaborative perception in autonomous vehicles, addressing occlusion and latency issues.

Findings

ECOD improves object classification accuracy by up to 75%.

The framework operates with low latency suitable for real-time autonomous driving.

Experimental results validate the effectiveness of edge collaboration in perception tasks.

Abstract

Accurate and reliable object detection is critical for ensuring the safety and efficiency of Connected Autonomous Vehicles (CAVs). Traditional on-board perception systems have limited accuracy due to occlusions and blind spots, while cloud-based solutions introduce significant latency, making them unsuitable for real-time processing demands required for autonomous driving in dynamic environments. To address these challenges, we introduce an innovative framework, Edge-Enabled Collaborative Object Detection (ECOD) for CAVs, that leverages edge computing and multi-CAV collaboration for real-time, multi-perspective object detection. Our ECOD framework integrates two key algorithms: Perceptive Aggregation and Collaborative Estimation (PACE) and Variable Object Tally and Evaluation (VOTE). PACE aggregates detection data from multiple CAVs on an edge server to enhance perception in scenarios where individual CAVs have limited visibility. VOTE utilizes a consensus-based voting mechanism to improve the accuracy of object classification by integrating data from multiple CAVs. Both algorithms are designed at the edge to operate in real-time, ensuring low-latency and reliable decision-making for CAVs. We develop a hardware-based controlled testbed consisting of camera-equipped robotic CAVs and an edge server to evaluate the efficacy of our framework. Our experimental results demonstrate the significant benefits of ECOD in terms of improved object classification accuracy, outperforming traditional single-perspective onboard approaches by up to 75%, while ensuring low-latency, edge-driven real-time processing. This research highlights the potential of edge computing to enhance collaborative perception for latency-sensitive autonomous systems.