Safety Field-Based Vehicle-Infrastructure Cooperative Perception for Autonomous Driving Using 3D Point Clouds

Abstract

Cooperative perception, using vehicle-to-everything (V2X) technologies for perceptual data sharing between autonomous vehicles (AVs) and intelligent infrastructure, is considered a solution to many single-agent perception challenges. Early fusion, a data fusion scheme for the cooperative perception of AVs, provides a universally available data-sharing approach but has been criticized for its huge bandwidth consumption. This paper proposes a safety field (SF)-based vehicle-infrastructure cooperative perception approach by quantifying the driving risk in complex traffic scenarios. Leveraging the SF theory and point cloud downsampling, we design a delay-aware early fusion framework with adaptive communication volume control. We propose a latency-compensation error (LCE) for performance evaluation considering data transmission delay. The proposed framework is tested and verified in simulated city environments and simulated and real-world datasets. The experimental results show that the proposed approach increases the average precision (AP) and reduces the LCE compared with base models within a limited communication budget.