移动目标轨迹预测方法研究综述

doi:10.11959/j.issn.2096-6652.202115

摘要/Abstract

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:

TP391

刘文, 胡琨林, 李岩, 等. 移动目标轨迹预测方法研究综述[J]. 智能科学与技术学报, 2021, 3(2): 149-160.

Wen LIU, Kunlin HU, Yan LI, et al. A review of prediction methods for moving target trajectories[J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(2): 149-160.

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分类	方法类别	参考文献	优点	缺点
数据驱动	卡尔曼滤波	[5-10]	线性，无偏，精度较高	依赖原始数据质量，无法长时预测
	差分自回归移动平均	[11-16]	模型简单，应用广泛	需要大量数据，精度较低
	隐马尔可夫	[17-20]	对过程的状态预测效果良好	鲁棒性较差，参数设置复杂
	高斯混合模型	[21-27]	短轨迹预测精度较高	易受数据复杂度影响，实用性低
	贝叶斯网络	[28-31]	高效，易于训练	易受先验概率、输入变量影响
	神经网络	[32-36]	自适应能力强	收敛速度慢，存在局部极小化问题
	深度学习	[13,15,37-50]	准确率高，实时性强	模型训练时间较长，可解释性较差
	混合模型	[40,48,51-54]	精度高，泛化能力强	训练时间较长，易过拟合
行为驱动	动力学模型	[37,55-61]	可解释性强，精度较高	依赖理想的环境和状态假设
	意图识别	[13,62-67]	实时性强，方法新颖	仅限意图明确的特定场景

应用	参考文献	研究意义
目标轨迹重建	[68-70]	利用预测的完整轨迹进行轨迹重建，可提升交通管理部门风险管控和数据分析能力
异常行为识别	[2,71-75]	可规避潜在风险，保障交通安全，实现交通智能化监管
导航路径规划	[76-83]	提供便利与服务，及时调整最佳路线，有效减少交通阻塞和交通事故