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This paper aims to tackle the interactive behavior prediction task, andproposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP)framework to jointly generate scene-compliant trajectories of two interactingagents. Our CGTP framework is an end to end and interpretable model, includingthree main stages: context encoding, goal interactive prediction and trajectoryinteractive prediction. First, a Goals-of-Interest Network (GoINet) is designedto extract the interactive features between agent-to-agent and agent-to-goalsusing a graph-based vectorized representation. Further, the Conditional GoalPrediction Network (CGPNet) focuses on goal interactive prediction via acombined form of marginal and conditional goal predictors. Finally, theGoaloriented Trajectory Forecasting Network (GTFNet) is proposed to implementtrajectory interactive prediction via the conditional goal-oriented predictors,with the predicted future states of the other interacting agent taken asinputs. In addition, a new goal interactive loss is developed to better learnthe joint probability distribution over goal candidates between two interactingagents. In the end, the proposed method is conducted on Argoverse motionforecasting dataset, In-house cut-in dataset, and Waymo open motion dataset.The comparative results demonstrate the superior performance of our proposedCGTP model than the mainstream prediction methods.

Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation