情景感知驱动的移动对象多模式轨迹预测技术综述
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韩楠,hannan@cuit.edu.cn

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国家自然科学基金(61772091,61802035,61962006,61962038,U1802271,U2001212,62072311);四川省科技计划项目(2021JDJQ0021,2020YFG0153,20YYJC2785,2020YJ0481,2020YFS0466,2020YJ0430,2020JDR0164,2020YFS0399,2019YFS0067);CCF-华为数据库创新研究计划(CCF-HuaweiDBIR2020004A);广西自然科学基金项目(2018GXNSFDA138005);广东省基础与应用基础研究基金(2020B1515120028);广西八桂学者创新团队(201979)


Multiple-Motion-Pattern Trajectory Prediction of Moving Objects with Context Awareness: A Survey
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    摘要:

    如何利用多源异构时空数据进行准确的轨迹预测并且反映移动对象的移动特性是轨迹预测领域的核心问题.现有的大多数轨迹预测方法是长序列轨迹模式预测模型,根据历史轨迹的特点进行预测,或将当前移动对象的轨迹位置放入时空语义场景根据历史移动对象轨迹预测位置.综述当前常用的轨迹预测模型和算法,涉及不同的研究领域.首先,阐述了多模式轨迹预测的主流工作,轨迹预测的基本模型类;其次,对不同类的预测模型进行总结,包括数学统计类、机器学习类、滤波算法,以及上述领域具有代表性的算法;再次,对情景感知技术进行了介绍,描述了不同领域的学者对情景感知的定义,阐述了情景感知技术所包含的关键技术点,诸如情景感知计算、情景获取和情景推理的不同类模型,分析了情景感知的不同分类、过滤、存储和融合以及它们的实现方法等.详细介绍了情景感知驱动的轨迹预测模型技术路线及各阶段任务的工作原理.给出了情景感知技术在真实场景中的应用,包括位置推荐,兴趣点推荐等,通过与传统算法对比,分析情景感知技术在此类应用中的优劣.详细介绍了情景感知结合LSTM (Long Short-Term Memory)技术应用于行人轨迹预测领域的新方法.最后,总结了轨迹预测和情景感知研究的当前问题和未来发展趋势.

    Abstract:

    How to utilize multi-source and heterogeneous spatio-temporal data to achieve accurate trajectory prediction as well as reflect the movement characteristics of moving objects is a core issue in the research field of trajectory prediction. Most of the existing trajectory prediction models are used to predict long sequential trajectory patterns according to the characteristics of historical trajectories, or the current locations of moving objects are integrated into spatio-temporal semantic scenarios to predict trajectories based on historical trajectories of moving objects. This survey summarizes the currently commonly-used trajectory prediction models and algorithms, involving different research fields. Firstly, the state-of-the-are works of multiple-motion trajectory prediction and the basic models of trajectory prediction are described. Secondly, the prediction models of different categories are summarized, including mathematical statistics, machine learning, Filtering algorithm, as well as the representative methods in these research fields. Thirdly, the context awareness techniques are introduced, the definition of context awareness by different scholars from different research fields are described, the key technical points of context awareness techniques are presented, such as the different kinds of models on context awareness computing, context acquisition and context reasoning, and the different categories, filtering, storage and fusion of context awareness and their implementation methods are analyzed. The technical roadmap of multiple-motion-pattern trajectory prediction of moving objects with context awareness and the working mechanism of each task is introduced in detail. This survey presents the real-world application scenarios of context awareness techniques, for example, location recommendation, point of interest recommendation. By comparing them with traditional algorithms, the advantages and disadvantages of context awareness techniques in the aforementioned applications are discussed. The new methods for pedestrian trajectory prediction based on context awareness and LSTM(Long Short-Term Memory) techniques are introduced in detail. Lastly, the current problems and future trends of trajectory prediction and context awareness are summarized.

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乔少杰,吴凌淳,韩楠,黄发良,毛睿,元昌安,Louis Alberto GUTIERREZ.情景感知驱动的移动对象多模式轨迹预测技术综述.软件学报,,():0

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  • 收稿日期:2021-03-16
  • 最后修改日期:2021-05-05
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  • 在线发布日期: 2021-08-02
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