The lane graph is a key component for building high-definition (HD) maps and
crucial for downstream tasks such as autonomous driving or navigation planning.
Previously, He et al. (2022) explored the extraction of the lane-level graph
from aerial imagery utilizing a segmentation based approach. However,
segmentation networks struggle to achieve perfect segmentation masks resulting
in inaccurate lane graph extraction. We explore additional enhancements to
refine this segmentation-based approach and extend it with a diffusion
probabilistic model (DPM) component. This combination further improves the GEO
F1 and TOPO F1 scores, which are crucial indicators of the quality of a lane
graph, in the undirected graph in non-intersection areas. We conduct
experiments on a publicly available dataset, demonstrating that our method
outperforms the previous approach, particularly in enhancing the connectivity
of such a graph, as measured by the TOPO F1 score. Moreover, we perform
ablation studies on the individual components of our method to understand their
contribution and evaluate their effectiveness.

通过改进和扩展分割方法，结合扩散概率模型，提高道路图的质量，特别是在非交叉区域中的连通性，从而优于先前方法。

带有扩散模型的车道分割细化

Lane Segmentation Refinement with Diffusion Models

SLEDGE is the first generative simulator for vehicle motion planning trained
on real-world driving logs. Its core component is a learned model that is able
to generate agent bounding boxes and lane graphs. The model's outputs serve as
an initial state for traffic simulation. The unique properties of the entities
to be generated for SLEDGE, such as their connectivity and variable count per
scene, render the naive application of most modern generative models to this
task non-trivial. Therefore, together with a systematic study of existing lane
graph representations, we introduce a novel raster-to-vector autoencoder
(RVAE). It encodes agents and the lane graph into distinct channels in a
rasterized latent map. This facilitates both lane-conditioned agent generation
and combined generation of lanes and agents with a Diffusion Transformer. Using
generated entities in SLEDGE enables greater control over the simulation, e.g.
upsampling turns or increasing traffic density. Further, SLEDGE can support
500m long routes, a capability not found in existing data-driven simulators
like nuPlan. It presents new challenges for planning algorithms, evidenced by
failure rates of over 40% for PDM, the winner of the 2023 nuPlan challenge,
when tested on hard routes and dense traffic generated by our model. Compared
to nuPlan, SLEDGE requires 500$\times$ less storage to set up (<4GB), making it
a more accessible option and helping with democratizing future research in this
field.

SLEDGE 是第一个使用真实世界驾驶日志训练的用于车辆运动规划的生成模拟器，其核心组件为一个学习模型，能够生成代理边界框和车道图，通过该模型的输出作为交通仿真的初始状态。

SLEDGE: 利用生成模型合成驾驶代理的仿真环境

SLEDGE: Synthesizing Simulation Environments for Driving Agents with  Generative Models

The local road network information is essential for autonomous navigation.
This information is commonly obtained from offline HD-Maps in terms of lane
graphs. However, the local road network at a given moment can be drastically
different than the one given in the offline maps; due to construction works,
accidents etc. Moreover, the autonomous vehicle might be at a location not
covered in the offline HD-Map. Thus, online estimation of the lane graph is
crucial for widespread and reliable autonomous navigation. In this work, we
tackle online Bird's-Eye-View lane graph extraction from a single onboard
camera image. We propose to use prior information to increase quality of the
estimations. The prior is extracted from the dataset through a transformer
based Wasserstein Autoencoder. The autoencoder is then used to enhance the
initial lane graph estimates. This is done through optimization of the latent
space vector. The optimization encourages the lane graph estimation to be
logical by discouraging it to diverge from the prior distribution. We test the
method on two benchmark datasets, NuScenes and Argoverse. The results show that
the proposed method significantly improves the performance compared to
state-of-the-art methods.

使用单一车载摄像头图像进行在线鸟瞰车道图提取，通过优化潜空间向量，从先验分布派生的先验信息增强了初始车道图估计，并对其进行逻辑优化，结果显示该方法显著提高了性能。

基于先验的单车载摄像头图像在线车道图提取

Prior Based Online Lane Graph Extraction from Single Onboard Camera  Image

Accurately predicting the future motion of surrounding vehicles requires
reasoning about the inherent uncertainty in driving behavior. This uncertainty
can be loosely decoupled into lateral (e.g., keeping lane, turning) and
longitudinal (e.g., accelerating, braking). We present a novel method that
combines learned discrete policy rollouts with a focused decoder on subsets of
the lane graph. The policy rollouts explore different goals given current
observations, ensuring that the model captures lateral variability.
Longitudinal variability is captured by our latent variable model decoder that
is conditioned on various subsets of the lane graph. Our model achieves
state-of-the-art performance on the nuScenes motion prediction dataset, and
qualitatively demonstrates excellent scene compliance. Detailed ablations
highlight the importance of the policy rollouts and the decoder architecture.

本文提出了一种新的方法，它通过对车辆行为的不确定性进行推理，结合对车道图子集的关注解码器，将学习到的离散策略展开，以探索不同的目标，从而确保模型捕捉到横向变异，进而捕捉纵向变异。该模型在 nuScenes 运动预测数据集上实现了最先进的表现，并在定性上展现出优秀的场景合规性。