基于状态精细化长短期记忆和注意力机制的社交生成对抗网络用于行人轨迹预测  

Social-interaction GAN for pedestrian trajectory prediction based on state-refinement long short-term memory and attention mechanism

作　　者：吴家皋[1,2] 章仕稳 蒋宇栋 刘林峰[1,2] WU Jiagao;ZHANG Shiwen;JIANG Yudong;LIU Linfeng(School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing Jiangsu 210023,China;Jiangsu Key Laboratory of Big Data Security and Intelligent Processing(Nanjing University of Posts and Telecommunications),Nanjing Jiangsu 210023,China)

机构地区：[1]南京邮电大学计算机学院,南京210023 [2]江苏省大数据安全与智能处理重点实验室(南京邮电大学),南京210023

出　　处：《计算机应用》2023年第5期1565-1570,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61872191)

摘　　要：针对当前行人轨迹预测研究仅考虑影响行人交互因素的问题,基于状态精细化长短期记忆(SR-LSTM)和注意力机制提出一种用于行人轨迹预测的社交生成对抗网络(SRA-SIGAN)模型,利用生成对抗网络(GAN)学习获得目标行人的运动规律。首先,使用SR-LSTM作为位置编码器提取运动意图信息;其次,通过设置速度注意力机制合理地为同一场景中的行人分配影响力,以更好地处理行人的交互;最后,由解码器生成预测的未来轨迹。在多个公开数据集上的测试实验结果表明,SRA-SIGAN模型的总体表现良好。特别是在Zara1数据集上,与SR-LSTM模型相比,SRA-SIGAN模型的平均位移误差(ADE)和最终位移误差(FDE)分别减小了20.0%和10.5%;与社交生成对抗网络(SIGAN)模型相比,SRA-SIGAN的ADE和FDE分别下降了31.7%和24.4%。In order to solve the problem of most current research work only considering the factors affecting pedestrian interaction,based on State-Refinement Long Short-Term Memory(SR-LSTM)and attention mechanism,a Social-Interaction Generative Adversarial Network(SIGAN)for pedestrian trajectory prediction was proposed,namely SRA-SIGAN,where GAN was utilized to learn movement patterns of target pedestrians.Firstly,SR-LSTM was used as a location encoder to extract the information of motion intention.Secondly,the influence of pedestrians in the same scene was reasonably assigned by setting the velocity attention mechanism,thereby handling the pedestrian interaction better.Finally,the predicted future trajectory was generated by the decoder.Experimental results on several public datasets show that the performance of SRASIGAN model is good on the whole.Specifically on the Zara1 dataset,compared with SR-LSTM model,the Average Displacement Error(ADE)and Final Displacement Error(FDE)of SRA-SIGAN were reduced by 20.0%and 10.5%,respectively;compared with the SIGAN model,the ADE and FDE of SRA-SIGAN were decreased by 31.7%and 24.4%,respectively.

关 键 词：生成对抗网络 长短期记忆网络 行人轨迹预测 注意力机制 行人交互 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.4[自动化与计算机技术—控制科学与工程]