用视觉模型实现车道级交通异常检测，无需昂贵传感器。
Lane-Wise Highway Anomaly Detection
- 通过摄像头提取车道车流特征，结合深度学习与规则逻辑
- 在73,139个样本上实现高精度异常识别，F1优于现有方法
- 适合智能交通系统部署，支持真实场景可解释性检测
本文提出一种可扩展且可解释的车道级高速公路交通异常检测框架，利用从监控摄像头提取的多模态时间序列数据。与依赖传感器的传统方法不同，该方法基于人工智能视觉模型提取车道特异性特征，包括车辆数量、占有率和卡车占比，无需昂贵硬件或复杂的道路建模。我们构建了一个包含73,139个车道样本的新数据集，标注了四类专家验证的异常：三类交通相关异常（车道阻塞及恢复、异物侵入、持续拥堵）和一类传感器相关异常（摄像头角度偏移）。多分支检测系统融合深度学习、规则逻辑与机器学习，提升鲁棒性与精度。大量实验表明，本框架在精确率、召回率和F1-score上均优于现有最先进方法，为智能交通系统提供低成本、可扩展的解决方案。
原文摘要 · Abstract (English)
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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