OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping
Huijie Wang, Tianyu Li, Yang Li, Li Chen, Chonghao Sima, Zhenbo Liu, Bangjun Wang, Peijin Jia
Abstract
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.
BibTeX
@inproceedings{
wang2023openlanev,
title={OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D {HD} Mapping},
author={Huijie Wang and Tianyu Li and Yang Li and Li Chen and Chonghao Sima and Zhenbo Liu and Bangjun Wang and Peijin Jia and Yuting Wang and Shengyin Jiang and Feng Wen and Hang Xu and Ping Luo and Junchi Yan and Wei Zhang and Hongyang Li},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023},
url={https://openreview.net/forum?id=OMOOO3ls6g}
}