Collaborative Perception (CP) has shown a promising technique for autonomous driving, where multiple connected and autonomous vehicles (CAVs) share their perception information to enhance the overall perception performance and expand the perception range. However, in CP, ego CAV needs to receive messages from its collaborators, which makes it easy to be attacked by malicious agents. For example, a malicious agent can send harmful information to the ego CAV to mislead it. To address this critical issue, we propose a novel method, \textbf{CP-Guard}, a tailored defense mechanism for CP that can be deployed by each agent to accurately detect and eliminate malicious agents in its collaboration network. Our key idea is to enable CP to reach a consensus rather than a conflict against the ego CAV's perception results. Based on this idea, we first develop a probability-agnostic sample consensus (PASAC) method to effectively sample a subset of the collaborators and verify the consensus without prior probabilities of malicious agents. Furthermore, we define a collaborative consistency loss (CCLoss) to capture the discrepancy between the ego CAV and its collaborators, which is used as a verification criterion for consensus. Finally, we conduct extensive experiments in collaborative bird's eye view (BEV) tasks and our results demonstrate the effectiveness of our CP-Guard.

翻译：协同感知（CP）已成为自动驾驶领域一项前景广阔的技术，其中多辆网联自动驾驶车辆通过共享感知信息以提升整体感知性能并扩展感知范围。然而，在协同感知中，本车需接收来自协作方的信息，这使其易受恶意智能体攻击。例如，恶意智能体可向本车发送有害信息以误导其决策。为应对这一关键问题，我们提出一种新方法——\textbf{CP-Guard}，这是一种专为协同感知设计的防御机制，可部署于每个智能体以精准检测并剔除其协作网络中的恶意智能体。我们的核心思想是使协同感知达成共识而非与本车感知结果产生冲突。基于此，我们首先提出一种概率无关的样本共识方法，可在无需恶意智能体先验概率的情况下，有效采样部分协作方并验证共识。此外，我们定义了协同一致性损失函数，用于捕捉本车与协作方之间的差异，并将其作为共识验证准则。最后，我们在协同鸟瞰图感知任务中进行了大量实验，结果证明了CP-Guard的有效性。