Infrastructure-Assisted Collaborative Perception in Automated Valet
  Parking: A Safety Perspective
Yukuan Jia Jiawen Zhang Shimeng Lu Baokang Fan Ruiqing Mao Sheng Zhou Zhisheng Niu
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.