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.

翻译：预测道路智能体未来运动是自动驾驶流程中的关键任务。本文针对多智能体驾驶场景中生成一组场景级（即联合）未来轨迹预测的问题展开研究。为此，我们提出FJMP——一种面向多智能体交互驾驶场景的因子化联合运动预测框架。FJMP将未来场景交互动态建模为稀疏有向交互图，其中边表示智能体间的显式交互。随后，我们将该图剪枝为有向无环图（DAG），并根据DAG的偏序关系将联合预测任务分解为一系列边缘预测与条件预测序列。联合未来轨迹通过有向无环图神经网络（DAGNN）进行解码。我们在INTERACTION和Argoverse 2数据集上开展实验，结果表明FJMP相比非因子化方法能生成更准确且场景一致性更强的联合轨迹预测，尤其对于交互性最强、运动学最复杂的智能体。FJMP在INTERACTION数据集的多智能体测试排行榜中位列第一。