对比车端与路侧感知，发现路侧数据能显著提升3D检测精度。
Rethinking the Role of Infrastructure in Collaborative Perception
- 比较车端与路侧作为感知主体的差异，分析路侧数据作用
- 路侧数据使3D检测准确率最高提升10.30%
- 路侧主导感知更抗噪声，精度比车端高46.47%
协同感知（CP）是指主车辆从周围车辆和基础设施接收并融合传感器信息以增强感知能力。为评估配备传感器的基础设施的必要性，对基础设施数据在现有以车辆为中心的协同感知中的作用进行深入且量化的分析至关重要，但目前仍缺乏研究。为此，我们首先定量评估了基础设施数据在以车辆为中心的协同感知中的重要性；随后，将车端主导的协同感知与路侧主导的协同感知（即基础设施作为主代理）进行对比，评估两种方法的有效性。结果表明，引入基础设施数据可使3D目标检测准确率最高提升10.30%，而路侧主导的协同感知在抗噪声方面表现更优，其检测准确率相比车端主导方式最高提升46.47%。
原文摘要 · Abstract (English)
Collaborative Perception (CP) is a process in which an ego agent receives and fuses sensor information from surrounding vehicles and infrastructure to enhance its perception capability. To evaluate the need for infrastructure equipped with sensors, extensive and quantitative analysis of the role of infrastructure data in CP is crucial, yet remains underexplored. To address this gap, we first quantitatively assess the importance of infrastructure data in existing vehicle-centric CP, where the ego agent is a vehicle. Furthermore, we compare vehicle-centric CP with infra-centric CP, where the ego agent is now the infrastructure, to evaluate the effectiveness of each approach. Our results demonstrate that incorporating infrastructure data improves 3D detection accuracy by up to 10.30%, and infra-centric CP shows enhanced noise robustness and increases accuracy by up to 46.47% compared with vehicle-centric CP.
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