  Accurately depicting the complex traffic scene is a vital component for
autonomous vehicles to execute correct judgments. However, existing benchmarks
tend to oversimplify the scene by solely focusing on lane perception tasks.
Observing that human drivers rely on both lanes and traffic signals to operate
their vehicles safely, we present OpenLane-V2, the first dataset on topology
reasoning for traffic scene structure. The objective of the presented dataset
is to advance research in understanding the structure of road scenes by
examining the relationship between perceived entities, such as traffic elements
and lanes. Leveraging existing datasets, OpenLane-V2 consists of 2,000
annotated road scenes that describe traffic elements and their correlation to
the lanes. It comprises three primary sub-tasks, including the 3D lane
detection inherited from OpenLane, accompanied by corresponding metrics to
evaluate the model's performance. We evaluate various state-of-the-art methods,
and present their quantitative and qualitative results on OpenLane-V2 to
indicate future avenues for investigating topology reasoning in traffic scenes.
