Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

Yu Liu, Yuexin Zhang, Kunming Li, Yongliang Qiao, Stewart Worrall, You-Fu Li, He Kong

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

1 Citation (Scopus)

Abstract

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering cross-dataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme. © 2023 IEEE.
Original languageEnglish
Title of host publication2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
PublisherIEEE
Pages4360-4366
ISBN (Electronic)9798350399462
ISBN (Print)9798350399479
DOIs
Publication statusPublished - Sept 2023
Event26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023) - Bilbao, Bizkaia, Spain
Duration: 24 Sept 202328 Sept 2023
https://2023.ieee-itsc.org/

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
ISSN (Print)2153-0009
ISSN (Electronic)2153-0017

Conference

Conference26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)
Abbreviated titleIEEE ITSC 2023
Country/TerritorySpain
CityBilbao, Bizkaia
Period24/09/2328/09/23
Internet address

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