INTRAFORCE: Intra-Cluster Reinforced Social Transformer for Trajectory Prediction

Emami, Negar; Di Maio, Antonio; Braun, Torsten (15 November 2022). INTRAFORCE: Intra-Cluster Reinforced Social Transformer for Trajectory Prediction. In: 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2022) (pp. 333-338). IEEE Xplore: IEEE 10.1109/WiMob55322.2022.9941547

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Predicting mobile users’ trajectories accurately is essential for improving the performance of wireless networks and autonomous systems. In this paper, we tackle the problem of trajectory prediction in a multi-agent scenario where the social interaction among users is taken into consideration.We propose Intra- Cluster Reinforced Social Transformer (INTRAFORCE), a novel system to design and train Social-Transformer neural networks that learn the spatio-temporal interactions among neighboring mobile users and predict their joint future trajectories. Unlike state-of-the-art social-aware trajectory predictors that either miss the large-distance interactions or are computationally expensive due to the pooling of all users’ interactions, INTRAFORCE clusters users with similar trajectories and learns their interactions. INTRAFORCE performs Neural Architecture Search to optimize each transformer’s architecture to fit each cluster’s user mobility features using Reinforcement Learning. Through experimental validation, we show that INTRAFORCE outperforms several state-of-the-art trajectory predictors on five widely used smallscale pedestrian mobility datasets and one large-scale privacyoriented cellular mobility dataset by achieving lower prediction error, training time, and computational complexity. Keywords: Social-aware Trajectory Prediction, Transformers, Reinforcement Learning, Neural Architecture Search, Clustering.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF)
08 Faculty of Science > Institute of Computer Science (INF) > Communication and Distributed Systems (CDS)

UniBE Contributor:

Emami, Negar, Di Maio, Antonio, Braun, Torsten

Subjects:

000 Computer science, knowledge & systems
500 Science > 510 Mathematics

ISSN:

2160-4894

ISBN:

978-1-6654-6975-3

Publisher:

IEEE

Language:

English

Submitter:

Dimitrios Xenakis

Date Deposited:

14 Sep 2022 15:01

Last Modified:

20 Sep 2023 10:06

Publisher DOI:

10.1109/WiMob55322.2022.9941547

Related URLs:

Uncontrolled Keywords:

Social-aware Trajectory Prediction, Transformers, Reinforcement Learning, Neural Architecture Search, Clustering

BORIS DOI:

10.48350/172848

URI:

https://boris.unibe.ch/id/eprint/172848

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