Prior Based Online Lane Graph Extraction from Single Onboard Camera
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Yigit Baran Can Alexander Liniger Danda Pani Paudel Luc Van Gool
The local road network information is essential for autonomous navigation.
This information is commonly obtained from offline HD-Maps in terms of lane
graphs. However, the local road network at a given moment can be drastically
different than the one given in the offline maps; due to construction works,
accidents etc. Moreover, the autonomous vehicle might be at a location not
covered in the offline HD-Map. Thus, online estimation of the lane graph is
crucial for widespread and reliable autonomous navigation. In this work, we
tackle online Bird's-Eye-View lane graph extraction from a single onboard
camera image. We propose to use prior information to increase quality of the
estimations. The prior is extracted from the dataset through a transformer
based Wasserstein Autoencoder. The autoencoder is then used to enhance the
initial lane graph estimates. This is done through optimization of the latent
space vector. The optimization encourages the lane graph estimation to be
logical by discouraging it to diverge from the prior distribution. We test the
method on two benchmark datasets, NuScenes and Argoverse. The results show that
the proposed method significantly improves the performance compared to
state-of-the-art methods.