智能科学与技术学报 ›› 2021, Vol. 3 ›› Issue (2): 149-160.doi: 10.11959/j.issn.2096-6652.202115

• 专题:智能交通系统与应用 • 上一篇    下一篇

移动目标轨迹预测方法研究综述

刘文1,2, 胡琨林1, 李岩1, 刘钊1,2   

  1. 1 武汉理工大学航运学院,湖北 武汉 430063
    2 国家水运安全工程技术研究中心,湖北 武汉 430063
  • 修回日期:2021-05-12 出版日期:2021-06-01 发布日期:2021-06-01
  • 作者简介:刘文(1987- ),男,博士,武汉理工大学航运学院副教授,主要研究方向为海事智能交通系统、计算机视觉、海事大数据挖掘与可视分析等
    胡琨林(1998- ),男,武汉理工大学航运学院硕士生,主要研究方向为海事智能交通系统、海事数据挖掘
    李岩(1995- ),男,武汉理工大学航运学院硕士生,主要研究方向为深度学习、船舶轨迹数据挖掘、并行计算
    刘钊(1986- ),男,博士,武汉理工大学航运学院副教授,主要研究方向为水上交通系统建模与仿真、海事大数据挖掘与可视分析等
  • 基金资助:
    国家自然科学基金资助项目(51809207)

A review of prediction methods for moving target trajectories

Wen LIU1,2, Kunlin HU1, Yan LI1, Zhao LIU1,2   

  1. 1 School of Navigation, Wuhan University of Technology, Wuhan 430063, China
    2 National Engineering Research Center for Water Transportation Safety, Wuhan 430063, China
  • Revised:2021-05-12 Online:2021-06-01 Published:2021-06-01
  • Supported by:
    The National Natural Science Foundation of China(51809207)

摘要:

随着智能交通系统领域大量移动终端设备的涌现,理解并准确预测移动目标轨迹有助于降低交通事故发生的概率,提高基于位置服务的智能交通应用的质量和水平。主要从数据驱动和行为驱动的角度对移动目标轨迹预测方法进行综述,首先对概率统计、神经网络、深度学习和混合建模等数据驱动方法进行比较;其次对动力学建模和目标意图识别等行为驱动方法的基本概念及研究现状进行概述;然后分别对目标轨迹重建、目标异常行为识别和导航路径规划等轨迹预测应用进行简要叙述;最后讨论了移动目标轨迹预测存在的主要问题以及未来的发展方向。

关键词: 智能交通系统, 轨迹预测, 人工智能, 深度学习, 动力学模型

Abstract:

With the rapid emergence of mobile terminal equipment in intelligent transportation system, the deep understanding and accurate prediction of moving target trajectories are capable of reducing the traffic accident probability, and promoting the location service-based intelligent transportation applications.The trajectory prediction methods prediction methods for moving target trajectories were reviewed from the data-driven prediction methods and the behavior-driven trajectories prediction methods.Firstly, the data-driven prediction methods were reviewed, including probabilistic statistics, neural networks, deep learning, and hybrid modeling.Then, the basic conceptions of target behavior-driven trajectories prediction methods were analyzed.The corresponding dynamical modeling and intention recognition methods were reviewed.The trajectory prediction applications were briefly analyzed and reviewed, such as target trajectory reconstruction, target abnormal behavior identification, and navigation route planning.Finally, the main problems and development directions related to prediction of moving target trajectories were discussed.

Key words: intelligent transportation system, trajectory prediction, artificial intelligence, deep learning, dynamic model

中图分类号: 

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