Predicting vehicle trajectory of non-lane based driving behaviour with Temporal Fusion Transformer

Long, Kejun; Fei, Yi; Xing, Lu; Pei, Xin; Yao, Danya; Zheng, Ou; Abdel-Aty, Mohamed

doi:10.1080/21680566.2024.2326018

Transportmetrica B: Transport Dynamics

2024

DOI: 10.1080/21680566.2024.2326018

|View full text |Cite

Predicting vehicle trajectory of non-lane based driving behaviour with Temporal Fusion Transformer

Kejun Long,

Yi Fei,

Lu Xing

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A local and global feature fusion network for Super-Resolution reconstruction of turbulent flows

Gong,

Xu,

Zhao

et al. 2024

Physics of Fluids

View full text Add to dashboard Cite

The resolution of flow fields represents a significant factor influencing the accuracy of turbulent flow analysis. Nevertheless, the acquisition of high-resolution turbulence data remains a challenge due to the limitations imposed by computing resources. The interpolation method, while capable of achieving high-resolution turbulence at low cost, faces challenges in capturing details of turbulent flows. In this study, a local and global feature fusion network (LGFN) is designed for the reconstruction of high-resolution turbulent flows with high quality. First, dual parallel branches made of dense blocks are introduced into the LGFN to extract local features of turbulent flows. Moreover, the features after the first dense block of each branch are summarized into the self-attention block to obtain global features. The extracted local and global features are aggregated through learnable weight parameters to achieve feature fusion. Finally, the fused turbulence features are scaled to the same dimensional size as the high-resolution turbulence through the implementation of multilayer pixel shuffle layers and convolution layers. The effectiveness of the proposed network was evaluated using datasets of forced isotropic turbulence and channel turbulence. The results demonstrate that the reconstructed velocity fields of the LGFN exhibit the highest degree of similarity to the direct numerical simulation results, in comparison with bicubic interpolation, static convolutional neural network, and super-resolution dense connection network results. In addition, compared to alternative methods, the proposed network effectively captures the characteristics of isotropic or anisotropic turbulence even at larger scales.

show abstract

A local and global feature fusion network for Super-Resolution reconstruction of turbulent flows

Gong,

Xu,

Zhao

et al. 2024

Physics of Fluids

View full text Add to dashboard Cite

show abstract

An improved hybrid model for vehicle trajectory prediction considering spatiotemporal interactions

Zhang,

Xu,

Zhou

et al. 2024

Transportmetrica B: Transport Dynamics

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Predicting vehicle trajectory of non-lane based driving behaviour with Temporal Fusion Transformer

Cited by 2 publications

References 47 publications

A local and global feature fusion network for Super-Resolution reconstruction of turbulent flows

A local and global feature fusion network for Super-Resolution reconstruction of turbulent flows

An improved hybrid model for vehicle trajectory prediction considering spatiotemporal interactions

Contact Info

Product

Resources

About