# Anomaly Detection in Cooperative Vehicle Perception Systems under Imperfect Communication

arXiv: https://arxiv.org/abs/2501.17329 · HTML: https://mlsanity.com/paper/2501.17329/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 58 references that are in the map.
- First posted on arXiv 28 Jan 2025.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning](https://mlsanity.com/paper/2109.12674/) (2021)
- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Anomaly Detection in Autonomous Driving: A Survey](https://mlsanity.com/paper/2204.07974/) (2022)
- [Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges](https://mlsanity.com/paper/2301.06262/) (2023)
- [MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams](https://mlsanity.com/paper/1911.04464/) (2019)
- [Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks](https://mlsanity.com/paper/2207.06819/) (2022)
- [F-FADE: Frequency Factorization for Anomaly Detection in Edge Streams](https://mlsanity.com/paper/2011.04723/) (2020)
- [CMP: Cooperative Motion Prediction with Multi-Agent Communication](https://mlsanity.com/paper/2403.17916/) (2024)
- [Waymo Public Road Safety Performance Data](https://mlsanity.com/paper/2011.00038/) (2020)
- [Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles](https://mlsanity.com/paper/2004.10927/) (2020)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations](https://mlsanity.com/paper/2011.08581/) (2020)
- [Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study](https://mlsanity.com/paper/2201.13229/) (2022)
- [Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles](https://mlsanity.com/paper/2010.14908/) (2020)
- [Deep Masked Graph Matching for Correspondence Identification in Collaborative Perception](https://mlsanity.com/paper/2303.07555/) (2023)
- [FedMIL: Federated-Multiple Instance Learning for Video Analysis with Optimized DPP Scheduling](https://mlsanity.com/paper/2403.17331/) (2024)
- [Anomaly Detection in Graph Structured Data: A Survey](https://mlsanity.com/paper/2405.06172/) (2024)
- [Motion Estimation of Connected and Automated Vehicles under Communication Delay and Packet Loss of V2X Communications](https://mlsanity.com/paper/2101.07756/) (2021)
- [Intelligent Perception System for Vehicle-Road Cooperation](https://mlsanity.com/paper/2208.14052/) (2022)
- [Geographical Information Alignment Boosts Traffic Analysis via Transpose Cross-attention](https://mlsanity.com/paper/2412.02839/) (2024)

## Abstract (arXiv)

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.