Key problems and progress of pedestrian trajectory prediction methods: the state of the art and prospects

Quancheng DU; Xiao WANG; Lingxi LI; Huansheng NING

doi:10.11959/j.issn.2096-6652.202315

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Key problems and progress of pedestrian trajectory prediction methods: the state of the art and prospects

Surveys and Prospectives | 更新时间：2024-04-04

- Key problems and progress of pedestrian trajectory prediction methods: the state of the art and prospects
- Chinese Journal of Intelligent Science and Technology Vol. 5, Issue 2, Pages: 143-162(2023)
- 作者机构：
  
  1. 北京科技大学计算机与通信工程学院，北京 100083
  2. 安徽大学人工智能学院，安徽合肥 230601
  3. 青岛智能产业技术研究院，山东青岛 266109
  4. 美国印第安纳大学-普渡大学印第安纳波利斯联合分校电子与计算机工程系，美国印第安纳州 IN 46204
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62173329)
- DOI：10.11959/j.issn.2096-6652.202315
  CLC： TP29
- Revised：2023-03-24，
  
  Online First：2023-06，
  
  Published：15 June 2023
- 稿件说明：
移动端阅览
Quancheng DU, Xiao WANG, Lingxi LI, et al. Key problems and progress of pedestrian trajectory prediction methods: the state of the art and prospects[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(2): 143-162.
DOI：

Quancheng DU, Xiao WANG, Lingxi LI, et al. Key problems and progress of pedestrian trajectory prediction methods: the state of the art and prospects[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(2): 143-162. DOI： 10.11959/j.issn.2096-6652.202315.

摘要

行人轨迹预测旨在利用观察到的人类历史轨迹和周围环境信息来预测目标行人未来的位置信息，该研究具有重要的应用价值，可以降低自动驾驶车辆在社会交互下的碰撞风险。然而，传统的模型驱动的行人轨迹预测方法难以在复杂高动态的场景下对行人进行轨迹预测。相比之下，数据驱动的行人轨迹预测方法依靠大规模数据集平台，可以更好地捕捉和建模更复杂的行人交互关系，进而取得较精准的行人轨迹预测效果，成为自动驾驶、机器人导航和视频监控等领域的研究热点。为了宏观把握行人轨迹预测方法的研究现状及关键问题，以行人轨迹预测技术和方法分类为切入点，首先，详述行人轨迹预测已有方法的研究进展并归纳了目前存在的关键问题与挑战；其次，根据行人轨迹预测模型的建模差异，将现有方法分为模型驱动和数据驱动的行人轨迹预测方法，同时总结了不同方法的优缺点及适用场景；然后，对行人轨迹预测任务中使用的主流数据集进行了归纳总结，并对比了不同算法的性能指标；最后，针对行人轨迹预测的未来发展方向进行了展望。

Abstract

Pedestrian trajectory prediction aims to use observed human historical trajectories and surrounding environmental information to predict the future position of the target pedestrian

which has important application value in reducing collision risks for autonomous vehicles in social interactions.However

traditional model-driven pedestrian trajectory prediction methods are difficult to predict pedestrian trajectories in complex and highly dynamic scenes.In contrast

datadriven pedestrian trajectory prediction methods rely on large-scale datasets and can better capture and model more complex pedestrian interaction relationships

thereby achieving more accurate pedestrian trajectory prediction results

and have become a research hotspot in fields such as autonomous driving

robot navigation and video surveillance.In order to macroscopically grasp the research status and key issues of pedestrian trajectory prediction methods

We started with the classification of pedestrian trajectory prediction technology and methods.First

the research progress of existing pedestrian trajectory prediction methods were elaborated and the current key issues and challenges were summarized.Second

according to the modeling differences of pedestrian trajectory prediction models

existing methods were divided into model-driven and data-driven pedestrian trajectory prediction methods

and the advantages

disadvantages and applicable scenarios of different methods were summarized.Then

the mainstream datasets used in pedestrian trajectory prediction tasks were summarized and the performance indicators of different algoriths were compared.Finally

the future development direction of pedestrian trajectory prediction was prospected.

关键词

Keywords

references

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