Trajectory augmentation serves as a means to mitigate distributional shift in
imitation learning. However, imitating trajectories that inadequately represent
the original expert data can result in undesirable behaviors, particularly in
safety-critical scenarios. We propose a trajectory augmentation method designed
to maintain similarity with expert trajectory data. To accomplish this, we
first cluster trajectories to identify minority yet safety-critical groups.
Then, we combine the trajectories within the same cluster through geometrical
transformation to create new trajectories. These trajectories are then added to
the training dataset, provided that they meet our specified safety-related
criteria. Our experiments exhibit that training an imitation learning model
using these augmented trajectories can significantly improve closed-loop
performance.

我们提出了一种轨迹增强方法，旨在通过几何变换将位于同一簇中的轨迹组合起来，与专家轨迹数据保持相似性，从而创建新的轨迹。这些增强的轨迹被加入训练数据集中，在满足指定的安全相关标准的前提下。我们的实验证明，使用这些增强轨迹来训练仿真学习模型可以显著提高闭环性能。

增加安全关键行驶场景，同时保持与专家轨迹的相似性

Augmenting Safety-Critical Driving Scenarios while Preserving Similarity  to Expert Trajectories

Deep neural networks have recently achieved considerable improvements in
learning human behavioral patterns and individual preferences from massive
spatial-temporal trajectories data. However, most of the existing research
concentrates on fusing different semantics underlying sequential trajectories
for mobility pattern learning which, in turn, yields a narrow perspective on
comprehending human intrinsic motions. In addition, the inherent sparsity and
under-explored heterogeneous collaborative items pertaining to human check-ins
hinder the potential exploitation of human diverse periodic regularities as
well as common interests. Motivated by recent advances in disentanglement
learning, in this study we propose a novel disentangled solution called SSDL
for tackling the next POI prediction problem. SSDL primarily seeks to
disentangle the potential time-invariant and time-varying factors into
different latent spaces from massive trajectories data, providing an
interpretable view to understand the intricate semantics underlying human
diverse mobility representations. To address the data sparsity issue, we
present two realistic trajectory augmentation approaches to enhance the
understanding of both the human intrinsic periodicity and constantly-changing
intents. In addition, we devise a POI-centric graph structure to explore
heterogeneous collaborative signals underlying historical check-ins. Extensive
experiments conducted on four real-world datasets demonstrate that our proposed
SSDL significantly outperforms the state-of-the-art approaches -- for example,
it yields up to 8.57% improvements on ACC@1.

通过提出一种名为 SSDL 的新算法，该算法使用深度神经网络来处理大规模空间 - 时间轨迹数据，利用离散学习的方法提取潜在的时间不变和时间变化因素，通过增强不同的轨迹增强方法来解决稀疏性问题和探索历史签入背后的异构协作信号，以解决下一个 POI（点 of interest）预测问题。在四个真实世界数据集上进行的广泛实验表明，SSDL 在 ACC@1 上的表现显著优于现有的同类方法，例如在 ACC@1 上提高了 8.57％。