Understanding the complex traffic environment is crucial for self-driving
vehicles. Existing benchmarks in autonomous driving mainly cast scene
understanding as perception problems, e.g., perceiving lanelines with vanilla
detection or segmentation methods. As such, we argue that the perception
pipeline provides limited information for autonomous vehicles to drive in the
right way, especially without the aid of high-definition (HD) map. For
instance, following the wrong traffic signal at a complicated crossroad would
lead to a catastrophic incident. By introducing Road Genome (OpenLane-V2), we
intend to shift the community's attention and take a step further beyond
perception - to the task of topology reasoning for scene structure. The goal of
Road Genome is to understand the scene structure by investigating the
relationship of perceived entities among traffic elements and lanes. Built on
top of prevailing datasets, the newly minted benchmark comprises 2,000
sequences of multi-view images captured from diverse real-world scenarios. We
annotate data with high-quality manual checks in the loop. Three subtasks
compromise the gist of Road Genome, including the 3D lane detection inherited
from OpenLane. We have/will host Challenges in the upcoming future at
top-tiered venues.

提出了一种名为 Road Genome 的新的、针对场景结构的自动驾驶数据集，着眼于感知元素和车道之间的关系，包括三个子任务：从 OpenLane 继承的 3D 车道检测、拓扑推理和场景分割。

路网基因组：自动驾驶场景理解拓扑推理基准

Road Genome: A Topology Reasoning Benchmark for Scene Understanding in  Autonomous Driving

Understanding the road genome is essential to realize autonomous driving.
This highly intelligent problem contains two aspects - the connection
relationship of lanes, and the assignment relationship between lanes and
traffic elements, where a comprehensive topology reasoning method is vacant. On
one hand, previous map learning techniques struggle in deriving lane
connectivity with segmentation or laneline paradigms; or prior lane
topology-oriented approaches focus on centerline detection and neglect the
interaction modeling. On the other hand, the traffic element to lane assignment
problem is limited in the image domain, leaving how to construct the
correspondence from two views an unexplored challenge. To address these issues,
we present TopoNet, the first end-to-end framework capable of abstracting
traffic knowledge beyond conventional perception tasks. To capture the driving
scene topology, we introduce three key designs: (1) an embedding module to
incorporate semantic knowledge from 2D elements into a unified feature space;
(2) a curated scene graph neural network to model relationships and enable
feature interaction inside the network; (3) instead of transmitting messages
arbitrarily, a scene knowledge graph is devised to differentiate prior
knowledge from various types of the road genome. We evaluate TopoNet on the
challenging scene understanding benchmark, OpenLane-V2, where our approach
outperforms all previous works by a great margin on all perceptual and
topological metrics. The code would be released soon.

TopoNet 是第一个能够抽象交通知识的端到端框架，通过引入嵌入模块、场景图神经网络和场景知识图等三个设计来捕捉驾驶场景拓扑结构，解决了车道连通性和交通元素到车道的分配问题，达到了在 OpenLane-V2 数据集上的最佳表现。