提出抗通信中断的车路协同异常检测框架，提升自动驾驶安全性。
Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication
- 基于车路协同共享信息，设计抗通信中断的异常检测架构
- 在9万条轨迹数据上，F1和AUC均优于传统方法
- 适合低带宽、通信不稳定的智能驾驶场景使用
异常检测是保障自动驾驶安全的关键。本文利用车路协同感知，通过共享周边车辆信息，在复杂交通场景中实现更精准的异常行为识别与共识。针对真实世界中通信不完善的挑战，提出一种基于协同感知的异常检测框架（CPAD），该框架在通信中断情况下仍保持有效，适用于低带宽环境。由于缺乏多智能体车辆轨迹异常检测数据集，我们通过规则化车辆动力学分析生成了包含15,000种不同场景、共90,000条轨迹的基准数据集。实验表明，所提方法在F1-score和AUC指标上均优于标准异常分类方法，并对智能体连接中断表现出强鲁棒性。
原文摘要 · Abstract (English)
Anomaly detection is a critical requirement for ensuring safety in autonomous driving. In this work, we leverage Cooperative Perception to share information across nearby vehicles, enabling more accurate identification and consensus of anomalous behaviors in complex traffic scenarios. To account for the real-world challenge of imperfect communication, we propose a cooperative-perception-based anomaly detection framework (CPAD), which is a robust architecture that remains effective under communication interruptions, thereby facilitating reliable performance even in low-bandwidth settings. Since no multi-agent anomaly detection dataset exists for vehicle trajectories, we introduce 15,000 different scenarios with a 90,000 trajectories benchmark dataset generated through rule-based vehicle dynamics analysis. Empirical results demonstrate that our approach outperforms standard anomaly classification methods in F1-score, AUC and showcase strong robustness to agent connection interruptions.
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