QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory
  Prediction
Zikang Zhou Zihao Wen Jianping Wang Yung-Hui Li Yu-Kai Huang
Estimating the joint distribution of on-road agents' future trajectories is
essential for autonomous driving. In this technical report, we propose a
next-generation framework for joint multi-agent trajectory prediction called
QCNeXt. First, we adopt the query-centric encoding paradigm for the task of
joint multi-agent trajectory prediction. Powered by this encoding scheme, our
scene encoder is equipped with permutation equivariance on the set elements,
roto-translation invariance in the space dimension, and translation invariance
in the time dimension. These invariance properties not only enable accurate
multi-agent forecasting fundamentally but also empower the encoder with the
capability of streaming processing. Second, we propose a multi-agent DETR-like
decoder, which facilitates joint multi-agent trajectory prediction by modeling
agents' interactions at future time steps. For the first time, we show that a
joint prediction model can outperform marginal prediction models even on the
marginal metrics, which opens up new research opportunities in trajectory
prediction. Our approach ranks 1st on the Argoverse 2 multi-agent motion
forecasting benchmark, winning the championship of the Argoverse Challenge at
the CVPR 2023 Workshop on Autonomous Driving.