ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation

Understand

Forecasting future trajectories of agents in complex traffic scenes requires reliable and efficient predictions for all agents in the scene.

• However, existing methods for trajectory prediction are either inefficient or sacrifice accuracy.
• To address this challenge, we propose ADAPT, a novel approach for jointly predicting the trajectories of all agents in the scene with dynamic weight learning.
• Our approach outperforms state-of-the-art methods in both single-agent and multi-agent settings on the Argoverse and Interaction datasets, with a fraction of their computational overhead.