Online map generation and trajectory prediction are critical components of the autonomous driving perception-prediction-planning pipeline. While modern vectorized mapping models achieve high geometric accuracy, they typically treat map estimation as a deterministic task, discarding structural uncertainty. Existing probabilistic approaches often rely on diagonal covariance matrices, which assume independence between points and fail to capture the strong spatial correlations inherent in road geometry. To address this, we propose a structured probabilistic formulation for online map generation. Our method explicitly models intra-element dependencies by predicting a dense covariance matrix, parameterized via a Low-Rank plus Diagonal (LRPD) covariance decomposition. This formulation represents uncertainty as a combination of a low-rank component, which captures global spatial structure, and a diagonal component representing independent local noise, thereby capturing geometric correlations without the prohibitive computational cost of full covariance matrices. Evaluations on the nuScenes dataset demonstrate that our uncertainty-aware framework yields consistent improvements in online map generation quality compared to deterministic baselines. Furthermore, our approach establishes new state-of-the-art performance for map-based motion prediction, highlighting the critical role of uncertainty in planning tasks. Code is published under link-available-soon.

翻译：在线地图生成与轨迹预测是自动驾驶感知-预测-规划流程中的关键组成部分。尽管现代向量化地图模型能够实现较高的几何精度，但它们通常将地图估计视为确定性任务，忽略了结构性不确定性。现有的概率方法多依赖于对角协方差矩阵，这假设了点之间的独立性，无法捕捉道路几何中固有的强空间相关性。为解决这一问题，我们提出了一种面向在线地图生成的结构化概率模型。该方法通过预测稠密协方差矩阵来显式建模图元内部依赖关系，并采用低秩加对角（LRPD）协方差分解进行参数化。该分解将不确定性表示为低秩分量（捕捉全局空间结构）与对角分量（表征独立局部噪声）的组合，从而在避免全协方差矩阵过高计算代价的前提下，捕捉了几何相关性。在nuScenes数据集上的评估表明，与确定性基线相比，我们的不确定性感知框架在线地图生成质量上取得了一致性改进。此外，我们的方法在基于地图的运动预测任务上确立了新的最优性能，突显了不确定性在规划任务中的关键作用。代码已公开，链接即将提供。