Accurate detection of objects in 3D point clouds is a key problem in
autonomous driving systems. Collaborative perception can incorporate
information from spatially diverse sensors and provide significant benefits for
improving the perception accuracy of autonomous driving systems. In this work,
we consider that the autonomous vehicle uses local point cloud data and
combines information from neighboring infrastructures through wireless links
for cooperative 3D object detection. However, information sharing among vehicle
and infrastructures in predefined communication schemes may result in
communication congestion and/or bring limited performance improvement. To this
end, we propose a novel collaborative 3D object detection framework that
consists of three components: feature learning networks that map point clouds
into feature maps; an efficient communication block that propagates compact and
fine-grained query feature maps from vehicle to support infrastructures and
optimizes attention weights between query and key to refine support feature
maps; a region proposal network that fuses local feature maps and weighted
support feature maps for 3D object detection. We evaluate the performance of
the proposed framework using a synthetic cooperative dataset created in two
complex driving scenarios: a roundabout and a T-junction. Experiment results
and bandwidth usage analysis demonstrate that our approach can save
communication and computation costs and significantly improve detection
performance under different detection difficulties in all scenarios.

本文提出一种新的协同三维物体检测框架，通过利用空间分布式传感器所提供的信息来辅助自动驾驶系统中的感知精度，同时避免因事先的通信方案导致通信拥堵和 / 或性能受限的问题。通过两个复杂的驾驶场景的实验结果和带宽使用分析，证明了该方法可以在各种检测困难情况下节省通信和计算成本，并显著提高检测性能。

可学习通信的自动驾驶系统协同式三维物体检测

Collaborative 3D Object Detection for Automatic Vehicle Systems via Learnable Communications

We extend the classical tracking-by-detection paradigm to this
tracking-any-object task. Solid detection results are first extracted from TAO
dataset. Some state-of-the-art techniques like \textbf{BA}lanced-\textbf{G}roup
\textbf{S}oftmax (\textbf{BAGS}\cite{li2020overcoming}) and
DetectoRS\cite{qiao2020detectors} are integrated during detection. Then we
learned appearance features to represent any object by training feature
learning networks. We ensemble several models for improving detection and
feature representation. Simple linking strategies with most similar appearance
features and tracklet-level post association module are finally applied to
generate final tracking results. Our method is submitted as \textbf{AOA} on the
challenge website. Code is available at
this https URL

该研究通过跟踪任意对象的任务，扩展了经典的 “跟踪 - 检测” 方法，采用多种技术结合实现了更好的检测和特征表示，并应用简单的链接策略和关联模块生成最终的跟踪结果。