Unsupervised Anomaly Detection in Multi-Agent Trajectory Prediction via Transformer-Based Models

• URL: http://arxiv.org/abs/2601.20367v1
• Date: Wed, 28 Jan 2026 08:33:10 GMT
• Title: Unsupervised Anomaly Detection in Multi-Agent Trajectory Prediction via Transformer-Based Models
• Authors: Qing Lyu, Zhe Fu, Alexandre Bayen,
• Abstract summary: We propose an unsupervised anomaly detection framework based on a multi-agent Transformer.<n>A dual evaluation scheme has been proposed to assess both detection stability and physical alignment.<n>Our framework identifies 388 unique anomalies missed by Time-to-Collision and statistical baselines.
• Score: 45.08545174556591
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying safety-critical scenarios is essential for autonomous driving, but the rarity of such events makes supervised labeling impractical. Traditional rule-based metrics like Time-to-Collision are too simplistic to capture complex interaction risks, and existing methods lack a systematic way to verify whether statistical anomalies truly reflect physical danger. To address this gap, we propose an unsupervised anomaly detection framework based on a multi-agent Transformer that models normal driving and measures deviations through prediction residuals. A dual evaluation scheme has been proposed to assess both detection stability and physical alignment: Stability is measured using standard ranking metrics in which Kendall Rank Correlation Coefficient captures rank agreement and Jaccard index captures the consistency of the top-K selected items; Physical alignment is assessed through correlations with established Surrogate Safety Measures (SSM). Experiments on the NGSIM dataset demonstrate our framework's effectiveness: We show that the maximum residual aggregator achieves the highest physical alignment while maintaining stability. Furthermore, our framework identifies 388 unique anomalies missed by Time-to-Collision and statistical baselines, capturing subtle multi-agent risks like reactive braking under lateral drift. The detected anomalies are further clustered into four interpretable risk types, offering actionable insights for simulation and testing.

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