Lane-Wise Highway Anomaly Detection

• URL: http://arxiv.org/abs/2505.02613v1
• Date: Mon, 05 May 2025 12:32:23 GMT
• Title: Lane-Wise Highway Anomaly Detection
• Authors: Mei Qiu, William Lorenz Reindl, Yaobin Chen, Stanley Chien, Shu Hu,
• Abstract summary: This paper proposes a scalable and interpretable framework for lane-wise highway traffic anomaly detection.<n>Unlike traditional sensor-dependent methods, our approach uses AI-powered vision models to extract lane-specific features.<n>Our framework outperforms state-of-the-art methods in precision, recall, and F1-score.
• Score: 8.086502588472783
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper proposes a scalable and interpretable framework for lane-wise highway traffic anomaly detection, leveraging multi-modal time series data extracted from surveillance cameras. Unlike traditional sensor-dependent methods, our approach uses AI-powered vision models to extract lane-specific features, including vehicle count, occupancy, and truck percentage, without relying on costly hardware or complex road modeling. We introduce a novel dataset containing 73,139 lane-wise samples, annotated with four classes of expert-validated anomalies: three traffic-related anomalies (lane blockage and recovery, foreign object intrusion, and sustained congestion) and one sensor-related anomaly (camera angle shift). Our multi-branch detection system integrates deep learning, rule-based logic, and machine learning to improve robustness and precision. Extensive experiments demonstrate that our framework outperforms state-of-the-art methods in precision, recall, and F1-score, providing a cost-effective and scalable solution for real-world intelligent transportation systems.

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