  Vehicle trajectory prediction is essential for enabling safety-critical
intelligent transportation systems (ITS) applications used in management and
operations. While there have been some promising advances in the field, there
is a need for modern deep learning algorithms that allow real-time trajectory
prediction on embedded IoT devices. This article presents DeepTrack, a novel
deep learning algorithm customized for real-time vehicle trajectory prediction
and monitoring applications in arterial management, freeway management, traffic
incident management, and work zone management for high-speed incoming traffic.
In contrast to previous methods, the vehicle dynamics are encoded using
Temporal Convolutional Networks (TCNs) to provide more robust time prediction
with less computation. DeepTrack also uses depthwise convolution, which reduces
the complexity of models compared to existing approaches in terms of model size
and operations. Overall, our experimental results demonstrate that DeepTrack
achieves comparable accuracy to state-of-the-art trajectory prediction models
but with smaller model sizes and lower computational complexity, making it more
suitable for real-world deployment.
