  Enhancing roadway safety has become an essential computer vision focus area
for Intelligent Transportation Systems (ITS). As a part of ITS, Vehicle
Trajectory Prediction (VTP) aims to forecast a vehicle's future positions based
on 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 focus
on autonomous driving, with the growing number of surveillance cameras, another
sub-field emerges for surveillance VTP with its own set of challenges. In this
paper, we introduce VT-Former, a novel transformer-based VTP approach for
highway safety and surveillance. In addition to utilizing transformers to
capture long-range temporal patterns, a new Graph Attentive Tokenization (GAT)
module has been proposed to capture intricate social interactions among
vehicles. This study seeks to explore both the advantages and the limitations
inherent in combining transformer architecture with graphs for VTP. Our
investigation, conducted across three benchmark datasets from diverse
surveillance viewpoints, showcases the State-of-the-Art (SotA) or comparable
performance of VT-Former in predicting vehicle trajectories. This study
underscores the potential of VT-Former and its architecture, opening new
avenues for future research and exploration.
