Understanding driving scenarios is crucial to realizing autonomous driving.
Previous works such as map learning and BEV lane detection neglect the
connection relationship between lane instances, and traffic elements detection
tasks usually neglect the relationship with lane lines. To address these
issues, the task is presented which includes 4 sub-tasks, the detection of
traffic elements, the detection of lane centerlines, reasoning connection
relationships among lanes, and reasoning assignment relationships between lanes
and traffic elements. We present Separated RoadTopoFormer to tackle the issues,
which is an end-to-end framework that detects lane centerline and traffic
elements with reasoning relationships among them. We optimize each module
separately to prevent interaction with each other and aggregate them together
with few finetunes. For two detection heads, we adopted a DETR-like
architecture to detect objects, and for the relationship head, we concat two
instance features from front detectors and feed them to the classifier to
obtain relationship probability. Our final submission achieves 0.445 OLS, which
is competitive in both sub-task and combined scores.

提出了一个包括四个子任务的任务，包括交通要素检测、车道中心线检测、车道之间的关系推理以及车道和交通要素之间的关系推理，提出了一个端到端框架，名为 Separated RoadTopoFormer，可以检测出车道中心线和交通要素，并实现它们之间的推理关系，其中采用了 DETR-like 的体系结构来检测对象

分离式 RoadTopoFormer

Separated RoadTopoFormer

We present a new method for multi-modal, long-term vehicle trajectory
prediction. Our approach relies on using lane centerlines captured in rich maps
of the environment to generate a set of proposed goal paths for each vehicle.
Using these paths -- which are generated at run time and therefore dynamically
adapt to the scene -- as spatial anchors, we predict a set of goal-based
trajectories along with a categorical distribution over the goals. This
approach allows us to directly model the goal-directed behavior of traffic
actors, which unlocks the potential for more accurate long-term prediction. Our
experimental results on both a large-scale internal driving dataset and on the
public nuScenes dataset show that our model outperforms state-of-the-art
approaches for vehicle trajectory prediction over a 6-second horizon. We also
empirically demonstrate that our model is better able to generalize to road
scenes from a completely new city than existing methods.

通过使用环境中富含地图的车道中心线来提高多模态、长期车辆轨迹预测方法的准确性，实现了汽车行驶的目标方向建模，生成一组已提议的车辆目标路径，并将其用作空间锚点，进而预测基于目标的轨迹和对目标的分类分布，在 6 秒的预测角度内，在内部驾驶数据集和公共 nuScenes 数据集上实验结果表明，该模型的性能优于先进的车辆轨迹预测方法。