Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication

• URL: http://arxiv.org/abs/2501.17329v1
• Date: Tue, 28 Jan 2025 22:41:06 GMT
• Title: Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication
• Authors: Ashish Bastola, Hao Wang, Abolfazl Razi,
• Abstract summary: We propose a cooperative-perception-based anomaly detection framework (CPAD) CPAD is a robust architecture that remains effective under communication interruptions. Empirical results demonstrate that our approach outperforms standard anomaly classification methods in F1-score, AUC.
• Score: 4.575903181579272
• License:
• Abstract: 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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