Predicting the future motion of road agents is a critical task in an
autonomous driving pipeline. In this work, we address the problem of generating
a set of scene-level, or joint, future trajectory predictions in multi-agent
driving scenarios. To this end, we propose FJMP, a Factorized Joint Motion
Prediction framework for multi-agent interactive driving scenarios. FJMP models
the future scene interaction dynamics as a sparse directed interaction graph,
where edges denote explicit interactions between agents. We then prune the
graph into a directed acyclic graph (DAG) and decompose the joint prediction
task into a sequence of marginal and conditional predictions according to the
partial ordering of the DAG, where joint future trajectories are decoded using
a directed acyclic graph neural network (DAGNN). We conduct experiments on the
INTERACTION and Argoverse 2 datasets and demonstrate that FJMP produces more
accurate and scene-consistent joint trajectory predictions than non-factorized
approaches, especially on the most interactive and kinematically interesting
agents. FJMP ranks 1st on the multi-agent test leaderboard of the INTERACTION
dataset.

本文提出了一种用于自动驾驶中多智能体交互驾驶情景下预测未来场景级联联轨迹的方法，并称之为 FJMP。FJMP 将未来场景交互动力学建模为稀疏有向交互图，对联合预测任务进行分解并使用有向无环神经网络进行预测。实验表明 FJMP 在交互最多和动力学最有趣的代理上比非因式分解方法产生了更准确和一致的联合轨迹预测。