Autonomous driving requires a structured understanding of the surrounding road network to navigate. One of the most common and useful representation of such an understanding is done in the form of BEV lane graphs. In this work, we use the video stream from an onboard camera for online extraction of the surrounding's lane graph. Using video, instead of a single image, as input poses both benefits and challenges in terms of combining the information from different timesteps. We study the emerged challenges using three different approaches. The first approach is a post-processing step that is capable of merging single frame lane graph estimates into a unified lane graph. The second approach uses the spatialtemporal embeddings in the transformer to enable the network to discover the best temporal aggregation strategy. Finally, the third, and the proposed method, is an early temporal aggregation through explicit BEV projection and alignment of framewise features. A single model of this proposed simple, yet effective, method can process any number of images, including one, to produce accurate lane graphs. The experiments on the Nuscenes and Argoverse datasets show the validity of all the approaches while highlighting the superiority of the proposed method. The code will be made public.

本文主要研究从视频流中提取BEV车道图。通过三种不同的方法，即后处理，空时嵌入变换和显式BEV投影等方法，实现在任意数量的图像上生成准确的车道图，并在Nuscenes和Argoverse数据集上进行了实验并证明了所提出方法的有效性和优越性。

从机载视频中在线提取车道图