Robust multi-agent trajectory prediction is essential for the safe control of
robotic systems. A major challenge is to efficiently learn a representation
that approximates the true joint distribution of contextual, social, and
temporal information to enable planning. We propose Latent Variable Sequential
Set Transformers which are encoder-decoder architectures that generate
scene-consistent multi-agent trajectories. We refer to these architectures as
"AutoBots". The encoder is a stack of interleaved temporal and social
multi-head self-attention (MHSA) modules which alternately perform equivariant
processing across the temporal and social dimensions. The decoder employs
learnable seed parameters in combination with temporal and social MHSA modules
allowing it to perform inference over the entire future scene in a single
forward pass efficiently. AutoBots can produce either the trajectory of one
ego-agent or a distribution over the future trajectories for all agents in the
scene. For the single-agent prediction case, our model achieves top results on
the global nuScenes vehicle motion prediction leaderboard, and produces strong
results on the Argoverse vehicle prediction challenge. In the multi-agent
setting, we evaluate on the synthetic partition of TrajNet++ dataset to
showcase the model's socially-consistent predictions. We also demonstrate our
model on general sequences of sets and provide illustrative experiments
modelling the sequential structure of the multiple strokes that make up symbols
in the Omniglot data. A distinguishing feature of AutoBots is that all models
are trainable on a single desktop GPU (1080 Ti) in under 48h.

提出了基于自动机器人（AutoBots）的潜变量序列集变压器模型，具有单代理预测和多代理预测的能力，在全局 nuScenes 车辆运行预测领域取得了最佳成果，使用了学习可并处理的时间和社会 MHSA 模块的编码器，并结合可学习的种子参数，使用临时和社会 MHSA 模块的解码器能够高效地对整个未来场景进行推断。