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Enhancing roadway safety has become an essential computer vision focus areafor Intelligent Transportation Systems (ITS). As a part of ITS, VehicleTrajectory Prediction (VTP) aims to forecast a vehicle's future positions basedon its past and current movements. VTP is a pivotal element for road safety,aiding in applications such as traffic management, accident prevention,work-zone safety, and energy optimization. While most works in this field focuson autonomous driving, with the growing number of surveillance cameras, anothersub-field emerges for surveillance VTP with its own set of challenges. In thispaper, we introduce VT-Former, a novel transformer-based VTP approach forhighway safety and surveillance. In addition to utilizing transformers tocapture long-range temporal patterns, a new Graph Attentive Tokenization (GAT)module has been proposed to capture intricate social interactions amongvehicles. This study seeks to explore both the advantages and the limitationsinherent in combining transformer architecture with graphs for VTP. Ourinvestigation, conducted across three benchmark datasets from diversesurveillance viewpoints, showcases the State-of-the-Art (SotA) or comparableperformance of VT-Former in predicting vehicle trajectories. This studyunderscores the potential of VT-Former and its architecture, opening newavenues for future research and exploration.

VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer