AMEND: A Mixture of Experts Framework for Long-tailed Trajectory
  Prediction
Ray Coden Mercurius Ehsan Ahmadi Soheil Mohamad Alizadeh Shabestary Amir Rasouli
Accurate prediction of pedestrians' future motions is critical for
intelligent driving systems. Developing models for this task requires rich
datasets containing diverse sets of samples. However, the existing naturalistic
trajectory prediction datasets are generally imbalanced in favor of simpler
samples and lack challenging scenarios. Such a long-tail effect causes
prediction models to underperform on the tail portion of the data distribution
containing safety-critical scenarios. Previous methods tackle the long-tail
problem using methods such as contrastive learning and class-conditioned
hypernetworks. These approaches, however, are not modular and cannot be applied
to many machine learning architectures. In this work, we propose a modular
model-agnostic framework for trajectory prediction that leverages a specialized
mixture of experts. In our approach, each expert is trained with a specialized
skill with respect to a particular part of the data. To produce predictions, we
utilise a router network that selects the best expert by generating relative
confidence scores. We conduct experimentation on common pedestrian trajectory
prediction datasets and show that besides achieving state-of-the-art
performance, our method significantly performs better on long-tail scenarios.
We further conduct ablation studies to highlight the contribution of different
proposed components.