  Trajectory similarity search plays an essential role in autonomous driving,
as it enables vehicles to analyze the information and characteristics of
different trajectories to make informed decisions and navigate safely in
dynamic environments. Existing work on the trajectory similarity search task
primarily utilizes sequence-processing algorithms or Recurrent Neural Networks
(RNNs), which suffer from the inevitable issues of complicated architecture and
heavy training costs. Considering the intricate connections between
trajectories, using Graph Neural Networks (GNNs) for data modeling is feasible.
However, most methods directly use existing mathematical graph structures as
the input instead of constructing specific graphs from certain vehicle
trajectory data. This ignores such data's unique and dynamic characteristics.
To bridge such a research gap, we propose VeTraSS -- an end-to-end pipeline for
Vehicle Trajectory Similarity Search. Specifically, VeTraSS models the original
trajectory data into multi-scale graphs, and generates comprehensive embeddings
through a novel multi-layer attention-based GNN. The learned embeddings can be
used for searching similar vehicle trajectories. Extensive experiments on the
Porto and Geolife datasets demonstrate the effectiveness of VeTraSS, where our
model outperforms existing work and reaches the state-of-the-art. This
demonstrates the potential of VeTraSS for trajectory analysis and safe
navigation in self-driving vehicles in the real world.
