Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (3): 668-674.DOI: 10.11772/j.issn.1001-9081.2018081645

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Attention mechanism based pedestrian trajectory prediction generation model

SUN Yasheng1, JIANG Qi2, HU Jie1, QI Jin1, PENG Yinghong1   

  1. 1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
    2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2018-08-09 Revised:2018-09-01 Online:2019-03-10 Published:2019-03-11
  • Contact: 胡洁
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (51775332,51675329,51675342), the Program of State Key Laboratory of Mechanical System and Vibration (GZ2016KF001, GKZD020018), the Open Program of State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System (GZ2016KF001).

基于注意力机制的行人轨迹预测生成模型

孙亚圣1, 姜奇2, 胡洁1, 戚进1, 彭颖红1   

  1. 1. 上海交通大学 机械与动力工程学院, 上海 200240;
    2. 上海交通大学 电子信息与电气工程学院, 上海 200240
  • 作者简介:孙亚圣(1995-),男,河北张家口人,硕士研究生,CCF会员,主要研究方向:深度学习、机器视觉;姜奇(1995-),男,山东东营人,硕士研究生,主要研究方向:机器学习、自然语言理解;胡洁(1973-),男,安徽安庆人,教授,博士,主要研究方向:智能设计、知识融合;戚进(1984-),男,浙江金华人,副研究员,博士,主要研究方向:人工智能、知识工程;彭颖红(1963-),男,四川成都人,教授,博士,主要研究方向:创新设计、知识工程。
  • 基金资助:
    国家自然科学基金资助项目(51775332,51675329,51675342);机械系统与振动国家重点实验室课题(GZ2016KF001,GKZD020018);特种车辆及其传动系统智能制造国家重点实验室开放课题(GZ2016KF001)。

Abstract: Aiming at that Long Short Term Memory (LSTM) has only one pedestrian considered in isolation and cannot realize prediction with various possibilities, an attention mechanism based generative model for pedestrian trajectory prediction called AttenGAN was proposed to construct pedestrian interaction model and predict multiple reasonable possibilities. The proposed model was composed of a generator and a discriminator. The generator predicted multiple possible future trajectories according to pedestrian's past trajectory probability while the discriminator determined whether the trajectories were really existed or generated by the discriminator and gave feedback to the generator, making predicted trajectories obtained conform social norm more. The generator consisted of an encoder and a decoder. With other pedestrians information obtained by the attention mechanism as input, the encoder encoded the trajectories of the pedestrian as an implicit state. Combined with Gaussian noise, the implicit state of LSTM in the encoder was used to initialize the implicit state of LSTM in the decoder and the decoder decoded it into future trajectory prediction. The experiments on ETH and UCY datasets show that AttenGAN can provide multiple reasonable trajectory predictions and can predict the trajectory with higher accuracy compared with Linear, LSTM, S-LSTM (Social LSTM) and S-GAN (Social Generative Adversarial Network) models, especially in scenes of dense pedestrian interaction. Visualization of predicted trajectories obtained by the generator indicated the ability of this model to capture the interaction pattern of pedestrians and jointly predict multiple reasonable possibilities.

Key words: trajectory prediction, Long Short Term Memory (LSTM), Generative Adversarial Network (GAN), attention mechanism, pedestrian interaction

摘要: 针对长短期记忆网络(LSTM)在行人轨迹预测问题中孤立考虑单个行人,且无法进行多种可能性预测的问题,提出基于注意力机制的行人轨迹预测生成模型(AttenGAN),来对行人交互模式进行建模和概率性地对多种合理可能性进行预测。AttenGAN包括一个生成器和一个判别器,生成器根据行人过去的轨迹概率性地对未来进行多种可能性预测,判别器用来判断一个轨迹是真实的还是由生成器伪造生成的,进而促进生成器生成符合社会规范的预测轨迹。生成器由一个编码器和一个解码器组成,在每一个时刻,编码器的LSTM综合注意力机制给出的其他行人的状态,将当前行人个体的信息编码为隐含状态。预测时,首先用编码器LSTM的隐含状态和一个高斯噪声连接来对解码器LSTM的隐含状态初始化,解码器LSTM将其解码为对未来的轨迹预测。在ETH和UCY数据集上的实验结果表明,AttenGAN模型不仅能够给出符合社会规范的多种合理的轨迹预测,并且在预测精度上相比传统的线性模型(Linear)、LSTM模型、社会长短期记忆网络模型(S-LSTM)和社会对抗网络(S-GAN)模型有所提高,尤其在行人交互密集的场景下具有较高的精度性能。对生成器多次采样得到的预测轨迹的可视化结果表明,所提模型具有综合行人交互模式,对未来进行联合性、多种可能性预测的能力。

关键词: 轨迹预测, 长短期记忆网络, 生成对抗网络, 注意力机制, 行人交互

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