行人轨迹预测方法关键问题研究：现状及展望

doi:10.11959/j.issn.2096-6652.202315

Abstract

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.

Key words: pedestrian trajectory prediction, data driven, social interaction, autonomous driving

CLC Number:

TP29

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.

Figures/Tables 10

References 84

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方法	输入信息	网络结构	优缺点
Helbing等人^[12]	行人自身状态和周围环境信息	基于社会力模型结构	优点：模型简单有效、适用性强　缺点：参数敏感、缺少对行人不确定性建模
Karamouzas等人^[34]	行人位置、速度、加速度等信息以及场景信息	基于运动学模型和社会力模型的混合模型	优点：长期预测　缺点：训练成本较高、推理速度较慢
Trautman等人^[32]	历史轨迹信息	基于社会力模型和高斯模型结合	优点：泛化性强　缺点：实时性较低、模型复杂度较高
Zhou等人^[35]	行人历史轨迹数据	基于混合高斯模型的无监督模型结构	优点：高准确性、实时性　缺点：依赖大量数据、可解释性较差
Kooij等人^[17]	历史轨迹数据和环境上下文信息	基于贝叶斯滤波器和运动学模型	优点：全局建模、多模态输入、精度高　缺点：计算量大、需要大量训练数据
Yan等人^[13]	历史轨迹数据信息	基于运动学模型和社会力模型结合	优点：实时性高、灵活性好、可解释性强　缺点：网络结构复杂、训练难度大
Best等人^[37]	行人历史轨迹信息、场景上下文信息	基于运动学模型和贝叶斯模型结合	优点：可预测性强、可解释性好　缺点：计算复杂、依赖先验知识
Xie等人^[38]	行人位置、姿态、方向信息以及场景上下文信息	基于循环神经网络和卷积神经网络以及运动学模型结合	优点：建模精准、多模态融合　缺点：模型复杂、需要大量的训练数据和计算资源
Rudenko等人^[31]	地图、行人速度、位置以及环境信息	基于社会力模型和循环神经网络模型结合	优点：多模型组合、长期预测、扩展性好　缺点：计算复杂

方法	输入信息	网络结构	数据集
S-LSTM^[22]	行人历史轨迹信息	基于LSTM网络和社交池机制结合	ETH/UCY
MX-LSTM^[41]	行人历史轨迹和姿态信息	基于LSTM网络、注意机制和社交池化机制	ETH/UCY、KITTI
Group LSTM^[42]	历史轨迹信息	基于LSTM模型于社交池化机制	ETH/UCY
Social-Grid LSTM^[43]	社会交互信息、时序上下文信息	基于社交池化与LSTM结合	ETH/UCY
SS-LSTM^[39]	历史轨迹、上下文场景信息	基于LSTM编解码结构	ETH/UCY
Scene-LSTM^[40]	场景上下文、行人轨迹点信息	基于LSTM网络和CNN网络结合	ETH/UCY
Shi等人^[24]	历史轨迹信息	基于社交池化机制	GCDC/MOT17
StarNet^[44]	行人历史轨迹信息	基于LSTM和社交池化机制	ETH/UCY
SNS-LSTM^[22]	场景上下文、社会交互信息	基于LSTM网络和池化机制	ETH/UCY

方法	输入信息	网络结构	数据集
Social-GAN^[21]	行人历史轨迹信息	基于LSTM的编解码器结构	ETH/UCY
Sophie^[46]	历史轨迹和上下文场景信息	基于GAN网络和注意机制结合	ETH/UCY和SDD
Social-BiGAT^[50]	行人动态特征、场景上下文信息	基于GCN和GAN结合	ETH/UCY
Social Way^[47]	历史轨迹信息	基于Info-GAN和注意机制结合	ETH/UCY
AEE-GAN^[49]	场景上下文、历史轨迹	基于InfoGAN网络和LSTM网络架构结合	ETH/UCY和SDD
STI-GAN^[48]	历史轨迹信息	基于GAN和图注意机制网络结合	ETH/UCY
Atten-GAN^[53]	历史轨迹信息、场景图信息	基于GAN和双向循环神经网络结合	ETH/UCY

方法	输入信息	网络结构	数据集
STGAT^[55]	历史轨迹信息、场景图像信息	基于GCN网络、LSTM网络和GAT网络结合	ETH/UCY
RSBG^[58]	历史轨迹信息	基于GCN网络和LSTM网络、CNN网络结合	ETH/UCY
Social-STGCNN^[54]	历史轨迹信息	基于GCN网络和CNN网络结合	ETH/UCY
SGCN^[56]	历史轨迹信息、速度、加速度信息	基于稀疏有向图建模时空关系	ETH/UCY
DMRGCN^[59]	历史轨迹信息、行人速度信息	基于GCN和注意机制结合	ETH/UCY
SSAGCN^[61]	历史轨迹信息	基于GCN、TCN和注意机制结合	ETH/UCY、SDD
AST-GNN^[62]	历史轨迹信息	基于GCN和注意机制结合	ETH/UCY
Pedestrian Graph +^[60]	行人位姿、场景上下文信息、车辆速度信息	基于GCN网络和卷积神经网络结合	JAAD/PIE

方法	输入信息	网络结构	数据集
Saleh等人^[72]	历史轨迹、上下文信息	基于Transformer的编解码结构	ETH/UCY
STAR^[71]	历史轨迹信息	基于图卷积（GCN）和Transformer结构结合	ETH/UCY
Giuliari等人^[20]	历史轨迹信息	基于Transformer的编解码结构	ETH/UCY、SDD
Yin等人^[73]	历史轨迹、上下文信息	基于Transformer的编解码结构	JAAD/PIE
Yao等人^[70]	历史轨迹信息	基于端到端的Transformer编解码结构	ETH/UCY
Li等人^[74]	历史轨迹信息、RGB图像信息、场景语义信息	基于图卷积网络和Transformer网络结合	ETH/UCY、VIRAT/ActEV
Su等人^[75]	历史轨迹信息、行人速度和加速度信息	基于交叉模态的Transformer网络架构	JAAD/PIE

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

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数据集	Agent	场景数量	传感器	时长	位置
UCY^[29]	行人	3	相机	29.5 min	校园、城市街道
ETH^[45]	行人	2	相机	25 min	大学入口、人行道、酒店入口
PETS 2009^[79]	行人	10	相机	20 h	公共场所、室外场景
Caltech Pedestrian^[78]	行人	7	车载相机	10 h	城市道路
SDD^[76]	车辆、行人	20	相机	—	校园
JAAD^[82]	行人	346	车载相机	240 h	城镇地区、城乡地区
ActEV/VIR^[77]	行人、车辆、船只等	12	高清相机	280 h	路口场景、街道
Crowd Human^[80]	行人	15 000	相机	—	城市路口、商场街道
InD^[81]	车辆、行人、自行车	4	相机	10 h	城市路口
PIE^[83]	行人、车辆	—	车载相机	6h	城镇地区、城乡地区
STCrowd^[84]	行人	9	激光雷达、相机	—	交通路口

[1]	Zhe HUANG, Yongcai WANG, Deying LI. A survey of 3D object detection algorithms [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(1): 7-31.
[2]	Jinna LI, Weiran CHENG. An overview of optimal consensus for data driven multi-agent system based on reinforcement learning [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(4): 327-340.