ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation
Görkay Aydemir Adil Kaan Akan Fatma Güney
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