  Trajectory prediction plays a crucial role in autonomous driving. Existing
mainstream research and continuoual learning-based methods all require training
on complete datasets, leading to poor prediction accuracy when sudden changes
in scenarios occur and failing to promptly respond and update the model.
Whether these methods can make a prediction in real-time and use data instances
to update the model immediately(i.e., online learning settings) remains a
question. The problem of gradient explosion or vanishing caused by data
instance streams also needs to be addressed. Inspired by Hedge Propagation
algorithm, we propose Expert Attention Network, a complete online learning
framework for trajectory prediction. We introduce expert attention, which
adjusts the weights of different depths of network layers, avoiding the model
updated slowly due to gradient problem and enabling fast learning of new
scenario's knowledge to restore prediction accuracy. Furthermore, we propose a
short-term motion trend kernel function which is sensitive to scenario change,
allowing the model to respond quickly. To the best of our knowledge, this work
is the first attempt to address the online learning problem in trajectory
prediction. The experimental results indicate that traditional methods suffer
from gradient problems and that our method can quickly reduce prediction errors
and reach the state-of-the-art prediction accuracy.
