论文标题
FJMP：对学到的定向无环相互作用图的分解关节多代理运动预测
FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs
论文作者
论文摘要
预测道路代理的未来运动是自动驾驶管道中的关键任务。在这项工作中，我们解决了在多代理驾驶场景中生成一组场景级别或联合未来轨迹预测的问题。为此，我们提出了FJMP，这是一个分解的联合运动预测框架，用于多代理交互式驾驶场景。 FJMP将未来的场景交互动力学建模为稀疏的定向相互作用图，其中边缘表示代理之间的显式相互作用。然后，根据DAG的部分排序，我们将图将图切成定向的无环图（DAG），并将关节预测任务分解为边缘和条件预测的序列，在该序列中，使用有向的acyclic图形图神经网络（DAGNN）将关节未来轨迹解码。我们对相互作用进行了实验，并进行了2个数据集进行实验，并证明FJMP比非物质化方法，尤其是在最互动和运动学上有趣的代理上，产生更准确和场景的关节轨迹预测。 FJMP在交互数据集的多代理测试排行榜上排名第一。
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.
