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 language | English |
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Title of host publication | 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) |
Publisher | IEEE |
Pages | 4360-4366 |
ISBN (Electronic) | 9798350399462 |
ISBN (Print) | 9798350399479 |
DOIs | |
Publication status | Published - Sept 2023 |
Event | 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023) - Bilbao, Bizkaia, Spain Duration: 24 Sept 2023 → 28 Sept 2023 https://2023.ieee-itsc.org/ |
Publication series
Name | IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC |
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ISSN (Print) | 2153-0009 |
ISSN (Electronic) | 2153-0017 |
Conference
Conference | 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023) |
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Abbreviated title | IEEE ITSC 2023 |
Country/Territory | Spain |
City | Bilbao, Bizkaia |
Period | 24/09/23 → 28/09/23 |
Internet address |