3D multi-object tracking (3D MOT) stands as a pivotal domain within
autonomous driving, experiencing a surge in scholarly interest and commercial
promise over recent years. Despite its paramount significance, 3D MOT confronts
a myriad of formidable challenges, encompassing abrupt alterations in object
appearances, pervasive occlusion, the presence of diminutive targets, data
sparsity, missed detections, and the unpredictable initiation and termination
of object motion trajectories. Countless methodologies have emerged to grapple
with these issues, yet 3D MOT endures as a formidable problem that warrants
further exploration. This paper undertakes a comprehensive examination,
assessment, and synthesis of the research landscape in this domain, remaining
attuned to the latest developments in 3D MOT while suggesting prospective
avenues for future investigation. Our exploration commences with a systematic
exposition of key facets of 3D MOT and its associated domains, including
problem delineation, classification, methodological approaches, fundamental
principles, and empirical investigations. Subsequently, we categorize these
methodologies into distinct groups, dissecting each group meticulously with
regard to its challenges, underlying rationale, progress, merits, and demerits.
Furthermore, we present a concise recapitulation of experimental metrics and
offer an overview of prevalent datasets, facilitating a quantitative comparison
for a more intuitive assessment. Lastly, our deliberations culminate in a
discussion of the prevailing research landscape, highlighting extant challenges
and charting possible directions for 3D MOT research. We present a structured
and lucid road-map to guide forthcoming endeavors in this field.

3D 多目标跟踪是自动驾驶领域中的一个重要问题，本文对该领域的研究现状进行了综述和总结，提出了未来的研究方向，包括问题界定、分类、方法论、挑战、实验指标和数据集等方面的系统讲解和比较，为未来的研究提供了指导。

自动驾驶中的三维多目标跟踪：文献综述

3D Multiple Object Tracking on Autonomous Driving: A Literature Review

The paper looks at the role of large language models in academic knowledge
creation based on a scoping review (2018 to January 2023) of how researchers
have previously used the language model GPT to assist in the performance of
academic knowledge creation tasks beyond data analysis. These tasks include
writing, editing, reviewing, dataset creation and curation, which have been
difficult to perform using earlier ML tools. Based on a synthesis of these
papers, this study identifies pathways for a future academic research landscape
that incorporates wider usage of large language models based on the current
modes of adoption in published articles as a Co-Writer, Research Assistant and
Respondent.

本文通过对 2018 年到 2023 年的系统回顾，研究了大型语言模型在学术知识创建中的作用，并通过合成这些论文，确定了一种未来学术研究的路径，该路径基于目前已发表的论文中使用大型语言模型作为合作者、研究助手和被访者等角色的采用方式。该研究显示，GPT 等大型语言模型为学术知识创造任务提供了有效的协助。

算法幽灵在研究外壳中：大型语言模型和管理研究中学术知识创造

Algorithmic Ghost in the Research Shell: Large Language Models and Academic Knowledge Creation in Management Research

AI alignment research is the field of study dedicated to ensuring that
artificial intelligence (AI) benefits humans. As machine intelligence gets more
advanced, this research is becoming increasingly important. Researchers in the
field share ideas across different media to speed up the exchange of
information. However, this focus on speed means that the research landscape is
opaque, making it difficult for young researchers to enter the field. In this
project, we collected and analyzed existing AI alignment research. We found
that the field is growing quickly, with several subfields emerging in parallel.
We looked at the subfields and identified the prominent researchers, recurring
topics, and different modes of communication in each. Furthermore, we found
that a classifier trained on AI alignment research articles can detect relevant
articles that we did not originally include in the dataset. We are sharing the
dataset with the research community and hope to develop tools in the future
that will help both established researchers and young researchers get more
involved in the field.

通过分析现有的 AI 对齐研究，我们发现领域正在迅速发展，并出现了几个子领域。我们查看了子领域并确定了突出的研究人员、经常出现的话题和每种不同的交流方式。此外，我们发现，一个分类器可以检测出 AI 对齐研究文章中没有最初包含在数据集中的相关文章。我们希望向研究社区共享数据集，并希望未来能够开发工具来帮助既有研究人员和年轻的研究人员更多地参与这一领域。