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.

翻译：轨迹相似性搜索在自动驾驶中发挥着关键作用，它使车辆能够分析不同轨迹的信息和特征，从而在动态环境中做出明智决策并安全导航。现有轨迹相似性搜索任务的工作主要利用序列处理算法或循环神经网络，这些方法不可避免地存在架构复杂和训练成本高昂的问题。考虑到轨迹之间错综复杂的关联，使用图神经网络进行数据建模是可行的。然而，大多数方法直接使用现有的数学图结构作为输入，而非从特定车辆轨迹数据构建专用图，这忽视了此类数据独特且动态的特性。为填补这一研究空白，我们提出VeTraSS——一种端到端的车辆轨迹相似性搜索流水线。具体而言，VeTraSS将原始轨迹数据建模为多尺度图，并通过新颖的基于多层注意力的图神经网络生成综合性嵌入。所学习的嵌入可用于搜索相似车辆轨迹。在Porto和Geolife数据集上的广泛实验证明了VeTraSS的有效性，我们的模型优于现有工作并达到最先进水平。这展现了VeTraSS在现实世界自动驾驶车辆中用于轨迹分析与安全导航的潜力。