基于深度学习的行人轨迹预测方法综述
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青岛科技大学

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TP391

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国家自然科学基金项目(面上项目,重点项目,重大项目)


A Survey of Pedestrian Trajectory Prediction Methods Based on Deep Learning
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Qingdao University of Science and Technology

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    摘要:

    为了规划合理的路径以规避行人,行人轨迹预测的研究具有广泛的应用价值。基于手工特征的传统方法难以预测复杂场景下的行人轨迹。深度学习以人工神经网络为架构,具有强大的学习能力,在各个领域取得了显著的效果。基于深度学习的行人轨迹预测方法已逐渐发展为一种趋势。为了宏观把握基于深度学习的行人轨迹预测的研究状况,首先,对不同方法进行组织与分类,比较了不同方法的优缺点,讨论了不同方法在行人轨迹预测领域的应用与发展。其次,根据行人轨迹预测模型的设计差异,对比了不同算法对模型性能产生的影响。最后,针对行人轨迹预测中存在的问题,对基于深度学习的行人轨迹预测方法的未来发展进行了展望。

    Abstract:

    In order to plan a reasonable path to avoid pedestrians, the research on pedestrian trajectory prediction has a wide range of application value. Traditional methods based on manual features are difficult to predict pedestrian trajectory in complex scenes. Deep learning is based on artificial neural network, which has strong learning ability and has achieved remarkable results in various fields. The pedestrian trajectory prediction method based on deep learning has gradually developed into a trend. In order to grasp the research status of pedestrian trajectory prediction based on deep learning, firstly, different methods are organized and classified, their advantages and disadvantages are compared, and the application and development of these methods in the field of pedestrian trajectory prediction are discussed. Secondly, according to the design differences of pedestrian trajectory prediction models, effects of different algorithms on the model performance are compared. Finally, in view of existing problems in pedestrian trajectory prediction, the future development of pedestrian trajectory prediction method based on deep learning is prospected.

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  • 收稿日期:2020-12-31
  • 最后修改日期:2021-03-23
  • 录用日期:2021-03-29
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