VeTraSS: Vehicle Trajectory Similarity Search Through Graph Modeling and Representation Learning

arXiv:2404.08021v1 Announce Type: new
Abstract: 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 …

abstract analyze arxiv autonomous autonomous driving cs.ai cs.lg cs.ro decisions driving dynamic environments graph information modeling processing representation representation learning role search the information through trajectory type vehicles work