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.

翻译：自动驾驶需要结构化理解周围道路网络以实现导航。最常用且有效的表征方式之一是鸟瞰视角下的车道图。本研究利用车载摄像头采集的视频流，实现周围环境车道图的在线提取。与单张图像输入相比，使用视频作为输入在跨时间步信息融合方面兼具优势与挑战。我们通过三种不同方法研究这些新出现的挑战。第一种方法采用后处理步骤，可将单帧车道图估计结果合并为统一的车道图；第二种方法利用Transformer中的时空嵌入能力，使网络自主发现最优的时间聚合策略；第三种方法（即本文提出的方法）通过显式的鸟瞰图投影与帧级特征对齐实现早期时间聚合。这种简单高效的方法仅需单一模型即可处理任意数量（包括单张）图像，并生成精确的车道图。在Nuscenes和Argoverse数据集上的实验验证了所有方法的有效性，同时凸显了所提方法的优越性。相关代码将开源。