ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation
This work addresses the need for efficient and accurate multi-agent trajectory prediction in autonomous driving, representing an incremental improvement over existing methods.
The paper tackles the problem of forecasting future trajectories of agents in traffic scenes by proposing ADAPT, a novel approach that uses dynamic weight learning to jointly predict trajectories for all agents, achieving state-of-the-art performance on Argoverse and Interaction datasets with reduced computational overhead.
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. We attribute the improvement in our performance: first, to the adaptive head augmenting the model capacity without increasing the model size; second, to our design choices in the endpoint-conditioned prediction, reinforced by gradient stopping. Our analyses show that ADAPT can focus on each agent with adaptive prediction, allowing for accurate predictions efficiently. https://KUIS-AI.github.io/adapt