Enhancing roadway safety and traffic management has become an essential focus
area for a broad range of modern cyber-physical systems and intelligent
transportation systems. Vehicle Trajectory Prediction is a pivotal element
within numerous applications for highway and road safety. These applications
encompass a wide range of use cases, spanning from traffic management and
accident prevention to enhancing work-zone safety and optimizing energy
conservation. The ability to implement intelligent management in this context
has been greatly advanced by the developments in the field of Artificial
Intelligence (AI), alongside the increasing deployment of surveillance cameras
across road networks. In this paper, we introduce a novel transformer-based
approach for vehicle trajectory prediction for highway safety and surveillance,
denoted as VT-Former. 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.
Combining these two core components culminates in a precise approach for
vehicle trajectory prediction. Our study on three benchmark datasets with three
different viewpoints demonstrates the State-of-The-Art (SoTA) performance of
VT-Former in vehicle trajectory prediction and its generalizability and
robustness. We also evaluate VT-Former's efficiency on embedded boards and
explore its potential for vehicle anomaly detection as a sample application,
showcasing its broad applicability.

在现代网络物理系统和智能交通系统中，提高道路安全和交通管理已成为一个重要的关注领域。通过引入一种新的基于 transformer 的方法 —VT-Former，并结合 Graph Attentive Tokenization (GAT) 模块，我们提出了一种精确的车辆轨迹预测方法。在三个基准数据集上的研究表明，VT-Former 在车辆轨迹预测方面具有最先进的性能，并且具有普适性和鲁棒性。我们还评估了 VT-Former 在嵌入式板上的效率，并探讨了其作为样本应用的车辆异常检测潜力，展示了其广泛的适用性。