Ni Qingjian, Peng Wenqiang, Zhu Yuntian, Ye Ruotian
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China.
Entropy (Basel). 2023 Jul 23;25(7):1100. doi: 10.3390/e25071100.
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.
轨迹预测是许多应用中的一项重要任务，包括自动驾驶、机器人技术和监控系统。在本文中，我们提出了一种新颖的轨迹预测网络，称为TFBNet（轨迹特征增强网络），它利用轨迹特征增强来提高预测准确性。TFBNet的工作方式是将原始轨迹数据映射到高维空间，分析该空间中轨迹的变化规则，最后汇总轨迹目标以生成最终轨迹。我们的方法为轨迹预测提供了一个新的视角。我们在五个真实世界的数据集上评估了TFBNet，并将其与当前的先进方法进行了比较。我们的结果表明，TFBNet在平均位移误差（ADE）和最终位移误差（FDE）指标上取得了显著改进，分别提高了46%和52%。这些结果验证了我们提出的方法的有效性及其在各种应用中提高轨迹预测模型性能的潜力。
