Environmental perception in Automated Valet Parking (AVP) has been a
challenging task due to severe occlusions in parking garages. Although
Collaborative Perception (CP) can be applied to broaden the field of view of
connected vehicles, the limited bandwidth of vehicular communications restricts
its application. In this work, we propose a BEV feature-based CP network
architecture for infrastructure-assisted AVP systems. The model takes the
roadside camera and LiDAR as optional inputs and adaptively fuses them with
onboard sensors in a unified BEV representation. Autoencoder and downsampling
are applied for channel-wise and spatial-wise dimension reduction, while
sparsification and quantization further compress the feature map with little
loss in data precision. Combining these techniques, the size of a BEV feature
map is effectively compressed to fit in the feasible data rate of the NR-V2X
network. With the synthetic AVP dataset, we observe that CP can effectively
increase perception performance, especially for pedestrians. Moreover, the
advantage of infrastructure-assisted CP is demonstrated in two typical
safety-critical scenarios in the AVP setting, increasing the maximum safe
cruising speed by up to 3m/s in both scenarios.

自动泊车系统中加强环境感知一直是一个困难的任务，但是通过协同感知可以扩大车辆的视野，本文提出了一种基于 BEV 特征的协同感知网络架构，通过将道路边缘摄像机和激光雷达与车载传感器融合，有效地压缩 BEV 特征图的大小以适应 NR-V2X 网络的数据速率。通过合成的 AVP 数据集，我们观察到协同感知可以有效地提高感知性能，特别是对于行人。此外，在两种典型的安全关键场景中展示了基础设施辅助式协同感知的优势，在这两种场景中，最大安全巡航速度提高了 3m/s。