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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Decision Neuroscience
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1346374
This article is part of the Research Topic Complex Traffic Scene Perception and Understanding for Autonomous Intelligent Unmanned Systems View all articles

DTDNet: Dynamic Target Driven Network for Pedestrian Trajectory Prediction

Provisionally accepted
Shaohua Liu Shaohua Liu 1Jingkai Sun Jingkai Sun 1,2Pengfei Yao Pengfei Yao 2,3Yinglong Zhu Yinglong Zhu 1,2Tianlu Mao Tianlu Mao 2*Zhaoqi Wang Zhaoqi Wang 2
  • 1 Beijing University of Posts and Telecommunications (BUPT), Beijing, Beijing Municipality, China
  • 2 Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, Beijing Municipality, China
  • 3 University of Chinese Academy of Sciences, Beijing, Beijing, China

The final, formatted version of the article will be published soon.

    Predicting the trajectories of pedestrians is an important and difficult task for many applications, such as robot navigation and autonomous driving. Most of the existing methods believe that an accurate prediction of the pedestrian intention can improve the prediction quality. These works tend to predict a fixed destination coordinate as the agent intention and predict the future trajectory accordingly. However, in the process of moving, the intention of a pedestrian could be a definite location or a general direction and area, and may change dynamically with the changes of surrounding. Thus, regarding the agent intention as a fixed 2-d coordinate is insufficient to improve the future trajectory prediction. To address this problem, we propose Dynamic Target Driven Network for pedestrian trajectory prediction(DTDNet), which employs a multi-precision pedestrian intention analysis module to capture this dynamic. To ensure that this extracted feature contains comprehensive intention information, we design three sub-tasks: predicting coarse-precision endpoint coordinate, predicting fine-precision endpoint coordinate and scoring scene sub-regions. In addition, we propose a original multi-precision trajectory data extraction method to achieve multi-resolution representation of future intention and make it easier to extract local scene information. We compare our model with previous methods on two publicly available datasets (ETH-UCY and Stanford Drone Dataset). The experimental results show that our DTDNet achieves better trajectory prediction performance, and conducts better pedestrian intention feature representation.

    Keywords: Multimodal trajectory prediction, pedestrian intention prediction, multi-precision motion prediction, Multi-task neural network, trajectory endpoint prediction

    Received: 29 Nov 2023; Accepted: 11 Apr 2024.

    Copyright: © 2024 Liu, Sun, Yao, Zhu, Mao and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Tianlu Mao, Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing, 100190, Beijing Municipality, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.