Entropy (Jul 2023)

A Novel Trajectory Feature-Boosting Network for Trajectory Prediction

  • Qingjian Ni,
  • Wenqiang Peng,
  • Yuntian Zhu,
  • Ruotian Ye

DOI
https://doi.org/10.3390/e25071100
Journal volume & issue
Vol. 25, no. 7
p. 1100

Abstract

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Trajectory prediction is an essential task in many applications, including autonomous driving, robotics, and surveillance systems. In this paper, we propose a novel trajectory prediction network, called TFBNet (trajectory feature-boosting network), that utilizes trajectory feature boosting to enhance prediction accuracy. TFBNet operates by mapping the original trajectory data to a high-dimensional space, analyzing the change rules of the trajectory in this space, and finally aggregating the trajectory goals to generate the final trajectory. Our approach presents a new perspective on trajectory prediction. We evaluate TFBNet on five real-world datasets and compare it to state-of-the-art methods. Our results demonstrate that TFBNet achieves significant improvements in the ADE (average displacement error) and FDE (final displacement error) indicators, with increases of 46% and 52%, respectively. These results validate the effectiveness of our proposed approach and its potential to improve the performance of trajectory prediction models in various applications.

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