Interactive Behavior Modeling for Vulnerable Road Users With Risk-Taking Styles in Urban Scenarios: A Heterogeneous Graph Learning Approach-Reference-Cited by-同舟云学术

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Interactive Behavior Modeling for Vulnerable Road Users With Risk-Taking Styles in Urban Scenarios: A Heterogeneous Graph Learning Approach

Published:2024-08 Issue:8 Volume:25 Page:8538-8555
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Li Zirui¹^ORCID,Gong Jianwei¹^ORCID,Zhang Zheyu¹^ORCID,Lu Chao¹^ORCID,Knoop Victor L.²^ORCID,Wang Meng³^ORCID

Affiliation:

1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China

2. Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, The Netherlands

3. Chair of Traffic Process Automation, “Friedrich List” Faculty of Transport and Traffic Sciences, TU Dresden, Dresden, Germany

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10621861/10537107.pdf?arnumber=10537107

Reference42 articles.

1. Predictive Cruise Control Using Radial Basis Function Network-Based Vehicle Motion Prediction and Chance Constrained Model Predictive Control

2. Trajectron++: Dynamically-Feasible Trajectory Forecasting with Heterogeneous Data

3. A survey on motion prediction and risk assessment for intelligent vehicles

4. Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks

5. Social LSTM: Human Trajectory Prediction in Crowded Spaces

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