OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping
This addresses the need for more comprehensive benchmarks in autonomous driving by integrating lane and traffic signal data, though it is incremental as it builds on existing datasets.
The authors introduced OpenLane-V2, a dataset of 2,000 annotated road scenes for topology reasoning in traffic scenes, focusing on relationships between lanes and traffic elements, and evaluated state-of-the-art methods to benchmark performance.
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.