DGCPath：面向自监督路径表示学习的分布感知生成对比框架——扩展版
DGCPath: Distribution-Aware Generative Contrastive Framework for Self-supervised Path Representation Learning -- Extended Version
- Aalborg University（奥尔堡大学）
- The Hong Kong Polytechnic University（香港理工大学）
- Chongqing Univeristy of Posts and Telecommunications（重庆邮电大学）
- East China Normal University（华东师范大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
DGCPath提出分布感知生成对比框架，结合扩散视图生成、变分对比与交叉监督，在三个真实轨迹数据集上超越基线，提升路径表示泛化能力。
AI中文摘要：
由于先进传感技术带来的车辆轨迹数据激增，路径表示学习已成为智能交通系统中的关键任务。尽管现有的自监督方法已取得令人瞩目的性能，但其对确定性对比学习范式和手工视图增强策略的依赖，本质上限制了其跨场景泛化能力。为解决这些局限，我们提出了DGCPath，一种创新的面向路径表示的分布感知生成对比学习框架。该框架在生成建模与分布级对比学习之间建立了协同联系，从而能够获取稳健且可迁移的特征嵌入。具体而言，我们的框架包含：（1）一个基于扩散的视图生成器，能够从高斯噪声中自主产生语义连贯且多样化的轨迹视图；（2）一种变分对比机制，在分布层面强制实现潜在特征对齐，超越了传统的实例级一致性；（3）一种新颖的生成式交叉监督模块，通过交叉视图重建学习强化视图级一致性。在三个真实世界轨迹数据集上的全面评估表明，DGCPath在两个不同的下游任务上优于最先进的基线方法，验证了其增强的泛化能力和表示有效性。
英文摘要：
Due to the proliferation of vehicle trajectory data enabled by advanced sensing technologies, path representation learning has become a pivotal task in intelligent transportation systems. Although existing self-supervised approaches have achieved promising performance, their dependence on deterministic contrastive learning paradigms and handcrafted view augmentation strategies inherently restricts their cross-scenario generalization capabilities. To address these limitations, we present DGCPath, an innovative Distribution-aware Generative Contrastive learning framework for Path representation. This framework establishes a synergistic connection between generative modeling and distributional contrastive learning, enabling the acquisition of robust and transferable feature embeddings. Specifically, our framework incorporates: (1) a diffusion-based view generator that autonomously produces semantically coherent yet diverse trajectory views from Gaussian noise; (2) a variational contrastive mechanism that enforces latent feature alignment at the distribution level, transcending conventional instance-wise consistency; and (3) a novel generative cross-supervision module that reinforces view-level consistency through cross-view reconstruction learning. Comprehensive evaluations on three real-world trajectory datasets demonstrate that DGCPath outperforms state-of-the-art baselines on two distinct downstream tasks, validating its enhanced generalization capability and representation effectiveness.
