得益于先进感知技术推动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。尽管现有自监督方法已取得良好性能，但其对确定性对比学习范式及人工设计视图增强策略的依赖，本质上限制了其跨场景泛化能力。为解决上述局限，我们提出 DGCPath——一种面向分布感知的生成式对比学习框架，专用于路径表示学习。该框架在生成建模与分布式对比学习之间建立协同关系，从而获得鲁棒且可迁移的特征嵌入。具体而言，本框架包含：（1）基于扩散模型的视图生成器，能够从高斯噪声中自主生成语义一致但多样化的轨迹视图；（2）变分对比机制，在分布层面强制隐空间特征对齐，超越传统逐样本一致性约束；（3）新型生成式交叉监督模块，通过跨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 在两项不同下游任务中均优于当前最优基线方法，验证了其增强的泛化能力与表示有效性。

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.