FJMP: 学習された有向非巡回相互作用グラフ上での因子分解型ジョイントマルチエージェント運動予測
FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs
マルチエージェントの運転シーンにおいて、相互作用を有向非巡回グラフとしてモデル化し、ジョイント予測を因子分解することで、より正確でシーン整合性の高い将来軌道予測を実現した。
著者: Luke Rowe, Martin Ethier, Eli-Henry Dykhne, Krzysztof Czarnecki
分類: cs.CV, cs.RO
原文アブストラクト
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.
