Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks

TL;DR

This paper introduces a graph neural network model for autonomous driving that jointly predicts vehicle trajectories and interaction modes, improving accuracy and interpretability in traffic scene prediction.

Contribution

The work presents a novel GNN-based approach that models pairwise interactions and jointly predicts trajectories and interaction types, with an auto-labeling method for training.

Findings

Lower trajectory prediction error compared to baselines

Interaction modes are semantically meaningful

Model effectively captures long-term agent interactions

Abstract

In this work, we aim to predict the future motion of vehicles in a traffic scene by explicitly modeling their pairwise interactions. Specifically, we propose a graph neural network that jointly predicts the discrete interaction modes and 5-second future trajectories for all agents in the scene. Our model infers an interaction graph whose nodes are agents and whose edges capture the long-term interaction intents among the agents. In order to train the model to recognize known modes of interaction, we introduce an auto-labeling function to generate ground truth interaction labels. Using a large-scale real-world driving dataset, we demonstrate that jointly predicting the trajectories along with the explicit interaction types leads to significantly lower trajectory error than baseline methods. Finally, we show through simulation studies that the learned interaction modes are semantically meaningful.