Vehicle anomaly detection plays a vital role in highway safety applications such as accident prevention, rapid response, traffic flow optimization, and work zone safety. With the surge of the Internet of Things (IoT) in recent years, there has arisen a pressing demand for Artificial Intelligence (AI) based anomaly detection methods designed to meet the requirements of IoT devices. Catering to this futuristic vision, we introduce a lightweight approach to vehicle anomaly detection by utilizing the power of trajectory prediction. Our proposed design identifies vehicles deviating from expected paths, indicating highway risks from different camera-viewing angles from real-world highway datasets. On top of that, we present VegaEdge - a sophisticated AI confluence designed for real-time security and surveillance applications in modern highway settings through edge-centric IoT-embedded platforms equipped with our anomaly detection approach. Extensive testing across multiple platforms and traffic scenarios showcases the versatility and effectiveness of VegaEdge. This work also presents the Carolinas Anomaly Dataset (CAD), to bridge the existing gap in datasets tailored for highway anomalies. In real-world scenarios, our anomaly detection approach achieves an AUC-ROC of 0.94, and our proposed VegaEdge design, on an embedded IoT platform, processes 738 trajectories per second in a typical highway setting. The dataset is available at https://github.com/TeCSAR-UNCC/Carolinas_Dataset#chd-anomaly-test-set .

翻译：车辆异常检测在高速公路安全应用中扮演着关键角色，例如事故预防、快速响应、交通流优化以及施工区安全。随着近年来物联网的蓬勃发展，针对物联网设备需求、基于人工智能的异常检测方法迫切涌现。面向这一未来愿景，我们提出了一种轻量化车辆异常检测方法，利用轨迹预测的能力实现检测。所提出的设计方案通过识别偏离预期路径的车辆，基于真实世界高速公路数据集从不同摄像头视角检测道路风险。此外，我们提出了VegaEdge——一种先进的AI融合系统，通过搭载所提异常检测方法的边缘物联网嵌入平台，专为现代高速公路环境中的实时安全监控应用而设计。跨多平台及交通场景的广泛测试验证了VegaEdge的通用性与有效性。本研究还提出了卡罗来纳异常数据集，以填补现有针对高速公路异常数据集的空白。在真实场景中，我们的异常检测方法实现了0.94的AUC-ROC指标；所提出的VegaEdge设计在典型高速公路环境下，于嵌入式物联网平台上每秒可处理738条轨迹。数据集下载地址：https://github.com/TeCSAR-UNCC/Carolinas_Dataset#chd-anomaly-test-set。