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.
@article{arxiv.2102.10859,
title = {Recursive Least Squares Based Refinement Network for the Rollout Trajectory Prediction Methods},
author = {Qifan Xue and Xuanpeng Li and Weigong Zhang},
journal= {arXiv preprint arXiv:2102.10859},
year = {2021}
}