BiFF: Bi-level Future Fusion with Polyline-based Coordinate for Interactive Trajectory Prediction

TL;DR

This paper introduces BiFF, a novel method for joint interactive trajectory prediction in autonomous driving, utilizing a bi-level fusion approach and polyline-based coordinates to improve accuracy and efficiency.

Contribution

The paper presents a bi-level future fusion framework with polyline-based coordinates, explicitly modeling interactions between agents for improved trajectory prediction.

Findings

Achieves state-of-the-art performance on Waymo Open Motion Dataset.

Effectively captures interactions between agents for joint trajectory prediction.

Demonstrates improved data efficiency and robustness in predictions.

Abstract

Predicting future trajectories of surrounding agents is essential for safety-critical autonomous driving. Most existing work focuses on predicting marginal trajectories for each agent independently. However, it has rarely been explored in predicting joint trajectories for interactive agents. In this work, we propose Bi-level Future Fusion (BiFF) to explicitly capture future interactions between interactive agents. Concretely, BiFF fuses the high-level future intentions followed by low-level future behaviors. Then the polyline-based coordinate is specifically designed for multi-agent prediction to ensure data efficiency, frame robustness, and prediction accuracy. Experiments show that BiFF achieves state-of-the-art performance on the interactive prediction benchmark of Waymo Open Motion Dataset.