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Predicting the future motion of road agents is a critical task in anautonomous driving pipeline. In this work, we address the problem of generatinga set of scene-level, or joint, future trajectory predictions in multi-agentdriving scenarios. To this end, we propose FJMP, a Factorized Joint MotionPrediction framework for multi-agent interactive driving scenarios. FJMP modelsthe future scene interaction dynamics as a sparse directed interaction graph,where edges denote explicit interactions between agents. We then prune thegraph into a directed acyclic graph (DAG) and decompose the joint predictiontask into a sequence of marginal and conditional predictions according to thepartial ordering of the DAG, where joint future trajectories are decoded usinga directed acyclic graph neural network (DAGNN). We conduct experiments on theINTERACTION and Argoverse 2 datasets and demonstrate that FJMP produces moreaccurate and scene-consistent joint trajectory predictions than non-factorizedapproaches, especially on the most interactive and kinematically interestingagents. FJMP ranks 1st on the multi-agent test leaderboard of the INTERACTIONdataset.

FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs