  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 Kalman filter based on deep neural network for
trajectory prediction. We design a flexible plug-in module which can be readily
implanted into most rollout approaches. Kalman 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.
