  Utilizing the latest advances in Artificial Intelligence (AI), the computer
vision community is now witnessing an unprecedented evolution in all kinds of
perception tasks, particularly in object detection. Based on multiple spatially
separated perception nodes, Cooperative Perception (CP) has emerged to
significantly advance the perception of automated driving. However, current
cooperative object detection methods mainly focus on ego-vehicle efficiency
without considering the practical issues of system-wide costs. In this paper,
we introduce VINet, a unified deep learning-based CP network for scalable,
lightweight, and heterogeneous cooperative 3D object detection. VINet is the
first CP method designed from the standpoint of large-scale system-level
implementation and can be divided into three main phases: 1) Global
Pre-Processing and Lightweight Feature Extraction which prepare the data into
global style and extract features for cooperation in a lightweight manner; 2)
Two-Stream Fusion which fuses the features from scalable and heterogeneous
perception nodes; and 3) Central Feature Backbone and 3D Detection Head which
further process the fused features and generate cooperative detection results.
An open-source data experimental platform is designed and developed for CP
dataset acquisition and model evaluation. The experimental analysis shows that
VINet can reduce 84% system-level computational cost and 94% system-level
communication cost while improving the 3D detection accuracy.
