Vectorized high-definition (HD) map construction is an important and challenging task for autonomous driving. End-to-end models have been developed recently to enable online map construction. Existing works have difficulty in generating complex geometric shapes and lack comprehensive evaluation metrics. To tackle these challenges, we introduce buffered IoU as a novel metric for vectorized map construction, which is clearly defined and applicable to real-world situations. Inspired by methods of rotated object detection, we further propose a novel technique called Buffered Gaussian Modeling. We extend 1D line segments into 2D Gaussian distributions, making them easier to learn. With Gaussian-based losses, map elements are learned by their geometric features rather than coordinates only. Experiments performed on nuScenes dataset show that our method significantly improves the quality of map generation, using both distance-based and IoU-based metrics.