CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN
Predicting plausible and collisionless trajectories is critical in various applications, such as robotic navigation and autonomous driving. This is a challenging task due to two major factors. First, it is difficult for deep neural networks to understand how pedestrians move to avoid collisions and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9063432/ |
| _version_ | 1818857087697944576 |
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| author | Shaohua Liu Haibo Liu Huikun Bi Tianlu Mao |
| author_facet | Shaohua Liu Haibo Liu Huikun Bi Tianlu Mao |
| author_sort | Shaohua Liu |
| collection | DOAJ |
| description | Predicting plausible and collisionless trajectories is critical in various applications, such as robotic navigation and autonomous driving. This is a challenging task due to two major factors. First, it is difficult for deep neural networks to understand how pedestrians move to avoid collisions and how they react to each other. Second, given observed trajectories, there are multiple possible and plausible trajectories followed by pedestrians. Although an increasing number of previous works have focused on modeling social interactions and multimodality, the trajectories generated by these methods still lead to many collisions. In this work, we propose CoL-GAN, a new attention-based generative adversarial network using a convolutional neural network as a discriminator, which is able to generate trajectories with fewer collisions. Through experimental comparisons with prior works on publicly available datasets, we demonstrate that Col-GAN achieves state-of-the-art performance in terms of accuracy and collision avoidance. |
| first_indexed | 2024-12-19T08:34:49Z |
| format | Article |
| id | doaj.art-c4f2a454b17c477a8a6b849c1023c860 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:34:49Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c4f2a454b17c477a8a6b849c1023c8602022-12-21T20:29:04ZengIEEEIEEE Access2169-35362020-01-01810166210167110.1109/ACCESS.2020.29870729063432CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GANShaohua Liu0https://orcid.org/0000-0002-9374-0192Haibo Liu1Huikun Bi2Tianlu Mao3School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaBeijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, ChinaBeijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, ChinaPredicting plausible and collisionless trajectories is critical in various applications, such as robotic navigation and autonomous driving. This is a challenging task due to two major factors. First, it is difficult for deep neural networks to understand how pedestrians move to avoid collisions and how they react to each other. Second, given observed trajectories, there are multiple possible and plausible trajectories followed by pedestrians. Although an increasing number of previous works have focused on modeling social interactions and multimodality, the trajectories generated by these methods still lead to many collisions. In this work, we propose CoL-GAN, a new attention-based generative adversarial network using a convolutional neural network as a discriminator, which is able to generate trajectories with fewer collisions. Through experimental comparisons with prior works on publicly available datasets, we demonstrate that Col-GAN achieves state-of-the-art performance in terms of accuracy and collision avoidance.https://ieeexplore.ieee.org/document/9063432/Trajectory predictiongenerative adversarial networkdeep learning |
| spellingShingle | Shaohua Liu Haibo Liu Huikun Bi Tianlu Mao CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN IEEE Access Trajectory prediction generative adversarial network deep learning |
| title | CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN |
| title_full | CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN |
| title_fullStr | CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN |
| title_full_unstemmed | CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN |
| title_short | CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN |
| title_sort | col gan plausible and collision less trajectory prediction by attention based gan |
| topic | Trajectory prediction generative adversarial network deep learning |
| url | https://ieeexplore.ieee.org/document/9063432/ |
| work_keys_str_mv | AT shaohualiu colganplausibleandcollisionlesstrajectorypredictionbyattentionbasedgan AT haiboliu colganplausibleandcollisionlesstrajectorypredictionbyattentionbasedgan AT huikunbi colganplausibleandcollisionlesstrajectorypredictionbyattentionbasedgan AT tianlumao colganplausibleandcollisionlesstrajectorypredictionbyattentionbasedgan |