Understanding multi-agent behavior is critical across various fields. The
conventional approach involves analyzing agent movements through three primary
tasks: trajectory prediction, imputation, and spatial-temporal recovery.
Considering the unique input formulation and constraint of these tasks, most
existing methods are tailored to address only one specific task. However, in
real-world applications, these scenarios frequently occur simultaneously.
Consequently, methods designed for one task often fail to adapt to others,
resulting in performance drops. To overcome this limitation, we propose a
Unified Trajectory Generation model, UniTraj, that processes arbitrary
trajectories as masked inputs, adaptable to diverse scenarios. Specifically, we
introduce a Ghost Spatial Masking (GSM) module embedded within a Transformer
encoder for spatial feature extraction. We further extend recent successful
State Space Models (SSMs), particularly the Mamba model, into a Bidirectional
Temporal Mamba to effectively capture temporal dependencies. Additionally, we
incorporate a Bidirectional Temporal Scaled (BTS) module to comprehensively
scan trajectories while maintaining the temporal missing relationships within
the sequence. We curate and benchmark three practical sports game datasets,
Basketball-U, Football-U, and Soccer-U, for evaluation. Extensive experiments
demonstrate the superior performance of our model. To the best of our
knowledge, this is the first work that addresses this unified problem through a
versatile generative framework, thereby enhancing our understanding of
multi-agent movement. Our datasets, code, and model weights are available at
this https URL

通过引入嵌入于 Transformer 编码器中的 Ghost Spatial Masking 模块进行空间特征提取，将 State Space Models (SSMs) 扩展为双向时间 Mamba 以捕捉时间依赖性，以及结合双向时间比例变换 (Bidirectional Temporal Scaled) 模块来全面扫描轨迹，本研究通过多功能生成框架首次解决了这个统一问题，增进了我们对多智能体运动的理解。

解码运动：多智能体统一轨迹生成模型

Deciphering Movement: Unified Trajectory Generation Model for  Multi-Agent

Simulation is pivotal in evaluating the performance of autonomous driving
systems due to the advantages in efficiency and cost compared to on-road
testing. Realistic multi-agent behavior~(e.g., interactive and long-term) is
needed to narrow the gap between the simulation and the reality. The existing
work has the following shortcomings in achieving this goal:~(1) log replay
offers realistic scenarios but leads to unrealistic collisions due to lacking
dynamic interactions, and~(2) model-based and learning-based solutions
encourage interactions but often deviate from real-world data in long horizons.
In this work, we propose LitSim, a long-term interactive simulation approach
that maximizes realism while avoiding unrealistic collisions. Specifically, we
replay the log for most scenarios and intervene only when LitSim predicts
unrealistic conflicts. We then encourage interactions among the agents and
resolve the conflicts, thereby reducing the likelihood of unrealistic
collisions. We train and validate our model on the real-world dataset NGSIM,
and the experimental results demonstrate that LitSim outperforms the current
popular approaches in realism and reactivity.

LitSim 是一种长期交互式模拟方法，通过重新播放记录来模拟大多数场景，并仅在 LitSim 预测到不真实冲突时进行干预，从而鼓励代理之间的交互并解决冲突，从而减少不真实碰撞的可能性。我们在真实世界的数据集 NGSIM 上训练和验证我们的模型，实验结果表明 LitSim 在逼真性和响应性方面优于当前流行的方法。

LitSim：长期交互式交通模拟的冲突感知策略

LitSim: Conflict-aware Policy for Long-term Interactive Traffic  Simulation

Real-world behavior is often shaped by complex interactions between multiple
agents. To scalably study multi-agent behavior, advances in unsupervised and
self-supervised learning have enabled a variety of different behavioral
representations to be learned from trajectory data. To date, there does not
exist a unified set of benchmarks that can enable comparing methods
quantitatively and systematically across a broad set of behavior analysis
settings. We aim to address this by introducing a large-scale, multi-agent
trajectory dataset from real-world behavioral neuroscience experiments that
covers a range of behavior analysis tasks. Our dataset consists of trajectory
data from common model organisms, with 9.6 million frames of mouse data and 4.4
million frames of fly data, in a variety of experimental settings, such as
different strains, lengths of interaction, and optogenetic stimulation. A
subset of the frames also consist of expert-annotated behavior labels.
Improvements on our dataset corresponds to behavioral representations that work
across multiple organisms and is able to capture differences for common
behavior analysis tasks.

本文介绍了一个包含多个模型物种，覆盖了一系列不同行为分析任务的实验数据，通过引入大规模的多代理轨迹数据集，改进了行为表征。