Pedestrian Trajectory Prediction Method Based on Information Fractals

YANG Tian; WANG Gang; LAI Jian; WANG Yang

doi:10.11999/JEIT230726

Volume 46 Issue 2

Feb. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(2): 527-537

YANG Tian, WANG Gang, LAI Jian, WANG Yang. Pedestrian Trajectory Prediction Method Based on Information Fractals[J]. Journal of Electronics & Information Technology, 2024, 46(2): 527-537. doi: 10.11999/JEIT230726

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YANG Tian, WANG Gang, LAI Jian, WANG Yang. Pedestrian Trajectory Prediction Method Based on Information Fractals[J]. Journal of Electronics & Information Technology, 2024, 46(2): 527-537. doi: 10.11999/JEIT230726

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Pedestrian Trajectory Prediction Method Based on Information Fractals

doi: 10.11999/JEIT230726

YANG Tian¹,
WANG Gang¹,
LAI Jian²,
WANG Yang^{2, 3
,
,}

1.
School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
2.
School of Electronics and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, China
3.
Intelligent Ocean Engineering Research Institute, Harbin Institute of Technology, Shenzhen 518055, China

Funds: The National Natural Science Foundation of China (62071146), Marine Economy Development Project of Guangdong Province (GDNRC [2020]014), Science and Technology Project of Shenzhen (JCYJ20200109113424990)

Received Date: 2023-07-19
Rev Recd Date: 2023-10-25

Available Online: 2023-10-27

Publish Date: 2024-02-29

Abstract

Abstract

Pedestrian trajectory prediction has been widely used in several fields, such as autonomous driving and robot navigation. In trajectory prediction, some uncertain information, such as the uncertainty of trajectory information discrimination in the discriminator and complex interactive information, bring challenges to the trajectory prediction task. In the field of uncertain information processing, information fractals can effectively deal with the uncertainty and complexity of uncertain information. Inspired by this, a trajectory prediction method based on the information fractal is proposed to fully deal with the uncertainty of trajectory information discrimination in the discriminator and improve the prediction accuracy. First, the scene and historical trajectory information are extracted by the feature extraction module. Subsequently, the scene-pedestrian interaction and pedestrian-pedestrian interaction information are obtained through the attention module. Finally, reasonable trajectories are generated using generative adversarial networks and information fractals. Experiments on the two public datasets ETH and UCY reveal that the proposed method can effectively deal with the uncertainty of trajectory information and improve the accuracy of trajectory prediction. For example, the trajectories of sudden turns, overtaking, avoidance, and other behaviors can be effectively predicted. Moreover, the Average Displacement Error (ADE) and Final Displacement Error (FDE) are reduced by an average of 11.11% and 23.48%, respectively compared with the benchmark model error.
- Pedestrian trajectory prediction,
- Uncertain information processing,
- Information fractal,
- Generative adversarial networks

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References(28)

References

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