  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 .
