  The extraction of road network is essential for the generation of
high-definition maps since it enables the precise localization of road
landmarks and their interconnections. However, generating road network poses a
significant challenge due to the conflicting underlying combination of
Euclidean (e.g., road landmarks location) and non-Euclidean (e.g., road
topological connectivity) structures. Existing methods struggle to merge the
two types of data domains effectively, but few of them address it properly.
Instead, our work establishes a unified representation of both types of data
domain by projecting both Euclidean and non-Euclidean data into an integer
series called RoadNet Sequence. Further than modeling an auto-regressive
sequence-to-sequence Transformer model to understand RoadNet Sequence, we
decouple the dependency of RoadNet Sequence into a mixture of auto-regressive
and non-autoregressive dependency. Building on this, our proposed
non-autoregressive sequence-to-sequence approach leverages non-autoregressive
dependencies while fixing the gap towards auto-regressive dependencies,
resulting in success on both efficiency and accuracy. Extensive experiments on
nuScenes dataset demonstrate the superiority of RoadNet Sequence representation
and the non-autoregressive approach compared to existing state-of-the-art
alternatives. The code is open-source on
https://github.com/fudan-zvg/RoadNetworkTRansformer.
