自动代客泊车中的基础设施辅助协同感知：安全视角
Infrastructure-Assisted Collaborative Perception in Automated Valet Parking: A Safety Perspective
AI总结：
针对自动代客泊车遮挡与带宽限制问题，提出基于BEV特征的CP网络架构，融合路侧与车载传感器并压缩特征图以适应NR-V2X，显著提升感知性能与安全巡航速度。
摘要：
自动代客泊车（AVP）中的环境感知因停车库内的严重遮挡而成为一项挑战性任务。尽管协同感知（CP）可用于拓宽网联车辆的视野，但车辆通信的有限带宽限制了其应用。在本研究中，我们提出了一种基于BEV特征的CP网络架构，用于基础设施辅助的AVP系统。该模型将路侧摄像头和LiDAR作为可选输入，并在统一的BEV表示中将其与车载传感器自适应融合。应用自编码器和下采样进行通道和空间维度的降维，同时稀疏化和量化进一步压缩特征图，且数据精度损失极小。结合这些技术，BEV特征图的尺寸被有效压缩，以适应NR-V2X网络的可行数据速率。利用合成的AVP数据集，我们观察到CP能有效提升感知性能，尤其是针对行人。此外，在AVP设置的两个典型安全关键场景中，基础设施辅助CP的优势得到了验证，在两种场景下将最大安全巡航速度最多提高了3m/s。
英文摘要：
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.
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