  Trajectory prediction plays a pivotal role in the field of intelligent
vehicles. It currently suffers from several challenges,e.g., accumulative error
in rollout process and weak adaptability in various scenarios. This paper
proposes a parametric-learning recursive least squares (RLS) estimation based
on deep neural network for trajectory prediction. We design a flexible plug-in
module which can be readily implanted into rollout approaches. Goal points are
proposed to capture the long-term prediction stability from the global
perspective. We carried experiments out on the NGSIM dataset. The promising
results indicate that our method could improve rollout trajectory prediction
methods effectively.
