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.

翻译：局部道路网络信息对于自主导航至关重要。这些信息通常以车道图形式从离线高清地图中获得。然而，由于施工、事故等原因，某一时刻的局部道路网络可能与离线地图中的信息存在显著差异。此外，自主车辆可能处于离线高清地图未覆盖的区域。因此，车道图的在线估计对于广泛且可靠的自主导航至关重要。本研究针对单个车载摄像头图像，解决在线鸟瞰视角车道图提取问题。我们提出利用先验信息来提高估计质量。该先验通过基于Transformer的Wasserstein自编码器从数据集中提取。随后，自编码器用于增强初始车道图估计，这一过程通过对潜在空间向量进行优化实现。优化通过抑制估计偏离先验分布，促使车道图估计更加合理。我们在NuScenes和Argoverse两个基准数据集上测试了该方法。结果表明，所提方法相比现有最先进方法显著提升了性能。