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Enhancing roadway safety and traffic management has become an essential focusarea for a broad range of modern cyber-physical systems and intelligenttransportation systems. Vehicle Trajectory Prediction is a pivotal elementwithin numerous applications for highway and road safety. These applicationsencompass a wide range of use cases, spanning from traffic management andaccident prevention to enhancing work-zone safety and optimizing energyconservation. The ability to implement intelligent management in this contexthas been greatly advanced by the developments in the field of ArtificialIntelligence (AI), alongside the increasing deployment of surveillance camerasacross road networks. In this paper, we introduce a novel transformer-basedapproach for vehicle trajectory prediction for highway safety and surveillance,denoted as VT-Former. In addition to utilizing transformers to capturelong-range temporal patterns, a new Graph Attentive Tokenization (GAT) modulehas been proposed to capture intricate social interactions among vehicles.Combining these two core components culminates in a precise approach forvehicle trajectory prediction. Our study on three benchmark datasets with threedifferent viewpoints demonstrates the State-of-The-Art (SoTA) performance ofVT-Former in vehicle trajectory prediction and its generalizability androbustness. We also evaluate VT-Former's efficiency on embedded boards andexplore its potential for vehicle anomaly detection as a sample application,showcasing its broad applicability.

VT-Former: A Transformer-based Vehicle Trajectory Prediction Approach For Intelligent Highway Transportation Systems