To ensure safe urban driving for autonomous platforms, it is crucial not only
to develop high-performance object detection techniques but also to establish a
diverse and representative dataset that captures various urban environments and
object characteristics. To address these two issues, we have constructed a
multi-class 3D LiDAR dataset reflecting diverse urban environments and object
characteristics, and developed a robust 3D semi-supervised object detection
(SSOD) based on a multiple teachers framework. This SSOD framework categorizes
similar classes and assigns specialized teachers to each category. Through
collaborative supervision among these category-specialized teachers, the
student network becomes increasingly proficient, leading to a highly effective
object detector. We propose a simple yet effective augmentation technique,
Pie-based Point Compensating Augmentation (PieAug), to enable the teacher
network to generate high-quality pseudo-labels. Extensive experiments on the
WOD, KITTI, and our datasets validate the effectiveness of our proposed method
and the quality of our dataset. Experimental results demonstrate that our
approach consistently outperforms existing state-of-the-art 3D semi-supervised
object detection methods across all datasets. We plan to release our
multi-class LiDAR dataset and the source code available on our Github
repository in the near future.

为了确保自主驾驶平台的安全行驶，除了开发高性能的物体检测技术外，还需要建立一个反映各种城市环境和物体特征的多样化和代表性的数据集。为了解决这两个问题，我们构建了一个多类别的 3D LiDAR 数据集，反映了多样化的城市环境和物体特征，并开发了基于多个教师框架的强大的 3D 半监督目标检测器。我们提出了一种简单而有效的增强技术，基于饼状点补偿（PieAug）的增强，以使教师网络生成高质量的伪标签。对 WOD、KITTI 和我们的数据集进行的大量实验验证了我们提出的方法的有效性和数据集的质量。实验结果表明，我们的方法始终优于现有的最先进的 3D 半监督目标检测方法。我们计划在不久的将来在我们的 Github 存储库上发布我们的多类别 LiDAR 数据集和源代码。

合作监督的力量：增强的三维半监督目标检测的多教师框架

Power of Cooperative Supervision: Multiple Teachers Framework for  Enhanced 3D Semi-Supervised Object Detection

Trajectory prediction aims to forecast agents' possible future locations
considering their observations along with the video context. It is strongly
needed for many autonomous platforms like tracking, detection, robot
navigation, and self-driving cars. Whether it is agents' internal personality
factors, interactive behaviors with the neighborhood, or the influence of
surroundings, all of them might represent impacts on agents' future plannings.
However, many previous methods model and predict agents' behaviors with the
same strategy or feature distribution, making them challenging to give
predictions with sufficient style differences. This manuscript proposes the
Multi-Style Network (MSN), which utilizes style proposal and stylized
prediction two sub-networks, to give agents multi-style predictions in a novel
categorical way adaptively. The proposed network contains a series of style
channels, and each channel is bound to a unique and specific behavior style. In
detail, we use agents' end-point plannings and their interaction context as the
basis for the behavior classification, so as to adaptively learn multiple
diverse behavior styles through these channels. Then, we assume that the target
agents will plan their future behaviors according to each of these categorized
styles, thus utilizing different style channels to give potential predictions
with significant style differences in parallel. Experiments show that MSN
outperforms current state-of-the-art methods up to 10\% quantitatively on two
widely used datasets, and presents better multi-style characteristics
qualitatively.

提出了多样式网络（MSN），该网络利用风格建议和风格化预测两个子网络，以提供智能代理的多样式预测。此网络包含一系列具有独特和特定行为风格的风格通道，并通过这些通道来适应性地学习多种不同行为风格。该网络优于目前广泛使用的数据集，并在多样式特性上得到较好的性能表现。

MSN: 轨迹预测的多样式网络

MSN: Multi-Style Network for Trajectory Prediction

This two-part review examines how automation has contributed to different
aspects of discovery in the chemical sciences. In this second part, we reflect
on a selection of exemplary studies. It is increasingly important to articulate
what the role of automation and computation has been in the scientific process
and how that has or has not accelerated discovery. One can argue that even the
best automated systems have yet to ``discover'' despite being incredibly useful
as laboratory assistants. We must carefully consider how they have been and can
be applied to future problems of chemical discovery in order to effectively
design and interact with future autonomous platforms.
The majority of this article defines a large set of open research directions,
including improving our ability to work with complex data, build empirical
models, automate both physical and computational experiments for validation,
select experiments, and evaluate whether we are making progress toward the
ultimate goal of autonomous discovery. Addressing these practical and
methodological challenges will greatly advance the extent to which autonomous
systems can make meaningful discoveries.

这篇论文主要研究了在化学科学领域自动化技术的应用，探讨了目前自动科学发现的局限性，并提出了未来自主发现的实际应用和方法论方向。