This paper aims to tackle the interactive behavior prediction task, and
proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP)
framework to jointly generate scene-compliant trajectories of two interacting
agents. Our CGTP framework is an end to end and interpretable model, including
three main stages: context encoding, goal interactive prediction and trajectory
interactive prediction. First, a Goals-of-Interest Network (GoINet) is designed
to extract the interactive features between agent-to-agent and agent-to-goals
using a graph-based vectorized representation. Further, the Conditional Goal
Prediction Network (CGPNet) focuses on goal interactive prediction via a
combined form of marginal and conditional goal predictors. Finally, the
Goaloriented Trajectory Forecasting Network (GTFNet) is proposed to implement
trajectory interactive prediction via the conditional goal-oriented predictors,
with the predicted future states of the other interacting agent taken as
inputs. In addition, a new goal interactive loss is developed to better learn
the joint probability distribution over goal candidates between two interacting
agents. In the end, the proposed method is conducted on Argoverse motion
forecasting dataset, In-house cut-in dataset, and Waymo open motion dataset.
The comparative results demonstrate the superior performance of our proposed
CGTP model than the mainstream prediction methods.

本文提出了一种新的 CGTP 框架来进行交互行为预测，在 Argoverse 和 Waymo 数据集上进行实验并与其他传统方法进行比较，结果表明我们的模型具有更好的预测能力。