Vector maps are essential in autonomous driving for tasks like localization
and planning, yet their creation and maintenance are notably costly. While
recent advances in online vector map generation for autonomous vehicles are
promising, current models lack adaptability to different sensor configurations.
They tend to overfit to specific sensor poses, leading to decreased performance
and higher retraining costs. This limitation hampers their practical use in
real-world applications. In response to this challenge, we propose a modular
pipeline for vector map generation with improved generalization to sensor
configurations. The pipeline leverages probabilistic semantic mapping to
generate a bird's-eye-view (BEV) semantic map as an intermediate
representation. This intermediate representation is then converted to a vector
map using the MapTRv2 decoder. By adopting a BEV semantic map robust to
different sensor configurations, our proposed approach significantly improves
the generalization performance. We evaluate the model on datasets with sensor
configurations not used during training. Our evaluation sets includes larger
public datasets, and smaller scale private data collected on our platform. Our
model generalizes significantly better than the state-of-the-art methods.

我们提出了一种用于自动驾驶中向量地图生成的模块化流水线，通过采用鲁棒的俯视语义地图，并利用 MapTRv2 解码器将其转换为向量地图，从而显著提高了模型的泛化性能。在未经训练的传感器配置数据集上进行的评估结果表明，我们的模型泛化能力明显优于最先进的方法。