  Vectorized high-definition (HD) map is essential for autonomous driving,
providing detailed and precise environmental information for advanced
perception and planning. However, current map vectorization methods often
exhibit deviations, and the existing evaluation metric for map vectorization
lacks sufficient sensitivity to detect these deviations. To address these
limitations, we propose integrating the philosophy of rasterization into map
vectorization. Specifically, we introduce a new rasterization-based evaluation
metric, which has superior sensitivity and is better suited to real-world
autonomous driving scenarios. Furthermore, we propose MapVR (Map Vectorization
via Rasterization), a novel framework that applies differentiable rasterization
to vectorized outputs and then performs precise and geometry-aware supervision
on rasterized HD maps. Notably, MapVR designs tailored rasterization strategies
for various geometric shapes, enabling effective adaptation to a wide range of
map elements. Experiments show that incorporating rasterization into map
vectorization greatly enhances performance with no extra computational cost
during inference, leading to more accurate map perception and ultimately
promoting safer autonomous driving.
