DROGON: A Trajectory Prediction Model based on Intention-Conditioned Behavior Reasoning

Abstract

We propose a Deep RObust Goal-Oriented trajectory prediction Network (DROGON) for accurate vehicle trajectory prediction by considering behavioral intentions of vehicles in traffic scenes. Our main insight is that the behavior (i.e., motion) of drivers can be reasoned from their high level possible goals (i.e., intention) on the road. To succeed in such behavior reasoning, we build a conditional prediction model to forecast goal-oriented trajectories with the following stages: (i) relational inference where we encode relational interactions of vehicles using the perceptual context; (ii) intention estimation to compute the probability distributions of intentional goals based on the inferred relations; and (iii) behavior reasoning where we reason about the behaviors of vehicles as trajectories conditioned on the intentions. To this end, we extend the proposed framework to the pedestrian trajectory prediction task, showing the potential applicability toward general trajectory prediction.

arXiv commentConference on Robot Learning (CoRL) 2020

References in corpus (5)

• Social Force Model for Pedestrian Dynamics
• The Cityscapes Dataset for Semantic Urban Scene Understanding
• TNT: Target-driveN Trajectory Prediction
• Argoverse: 3D Tracking and Forecasting with Rich Maps
• Multiple Futures Prediction

Cited by in corpus (8)

• Stepwise Goal-Driven Networks for Trajectory Prediction
• A Novel Graph based Trajectory Predictor with Pseudo Oracle
• DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets
• BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation
• Multi-modal anticipation of stochastic trajectories in a dynamic environment with Conditional Variational Autoencoders
• CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning
• Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
• Risk-Aware Lane Selection on Highway with Dynamic Obstacles