High Definition (HD) maps are maps with precise definitions of road lanes
with rich semantics of the traffic rules. They are critical for several key
stages in an autonomous driving system, including motion forecasting and
planning. However, there are only a small amount of real-world road topologies
and geometries, which significantly limits our ability to test out the
self-driving stack to generalize onto new unseen scenarios. To address this
issue, we introduce a new challenging task to generate HD maps. In this work,
we explore several autoregressive models using different data representations,
including sequence, plain graph, and hierarchical graph. We propose HDMapGen, a
hierarchical graph generation model capable of producing high-quality and
diverse HD maps through a coarse-to-fine approach. Experiments on the Argoverse
dataset and an in-house dataset show that HDMapGen significantly outperforms
baseline methods. Additionally, we demonstrate that HDMapGen achieves high
scalability and efficiency.

介绍了一种通过自动递归生成方法生成高清地图的任务和被称为 HDMapGen 的层次图生成模型，该模型能够通过粗到精细的方法制作高质量且多样化的高清地图，并在 Argoverse 数据集和内部数据集上显示出在基线方法上明显的提高，同时表明了该模型的可扩展性和高效性。

HDMapGen：一种高清地图的分层图形生成模型

HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps

Predicting multiple plausible future trajectories of the nearby vehicles is
crucial for the safety of autonomous driving. Recent motion prediction
approaches attempt to achieve such multimodal motion prediction by implicitly
regularizing the feature or explicitly generating multiple candidate proposals.
However, it remains challenging since the latent features may concentrate on
the most frequent mode of the data while the proposal-based methods depend
largely on the prior knowledge to generate and select the proposals. In this
work, we propose a novel transformer framework for multimodal motion
prediction, termed as mmTransformer. A novel network architecture based on
stacked transformers is designed to model the multimodality at feature level
with a set of fixed independent proposals. A region-based training strategy is
then developed to induce the multimodality of the generated proposals.
Experiments on Argoverse dataset show that the proposed model achieves the
state-of-the-art performance on motion prediction, substantially improving the
diversity and the accuracy of the predicted trajectories. Demo video and code
are available at this https URL

该论文提出了一种新的基于 transformer 的系统 ——mmTransformer，它使用独立提案集生成和选择提案，并使用基于区域的训练策略诱导所生成提案的多样性。实验表明，该模型在运动预测方面具有最先进的性能，大大提高了预测轨迹的多样性和准确性。