Hyper-relational interaction modeling in multi-modal trajectory prediction for intelligent connected vehicles in smart cites

Lu, Yuhuan; Wang, Wei; Bai, Rufan; Zhou, Shengwei; Garg, Lalit; Bashir, Ali Kashif; Jiang, Weiwei; Hu, Xiping

doi:10.1016/j.inffus.2024.102682

Information Fusion

2025

DOI: 10.1016/j.inffus.2024.102682

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Hyper-relational interaction modeling in multi-modal trajectory prediction for intelligent connected vehicles in smart cites

Yuhuan Lu,

Wei Wang,

Rufan Bai

et al.

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Cited by 4 publications

References 30 publications

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Enhancing Vehicle Trajectory Quality: A Two‐Step Data Reconstruction Method Using Wavelet Transform and Normal Acceleration Value

Zhang,

Gao,

Zhou

2024

Engineering Reports

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Data reconstruction is essential in enhancing the quality of vehicle trajectory data. Previous studies have identified the location of abnormal data inaccurately, resulting in poor trajectory reconstruction results. This study proposed a two‐step reconstruction method. The first step detected the locations of obviously abnormal speed data using wavelet transform. Then, the abnormal data were repaired by the cubic spline curve interpolation algorithm. The second stage identified the locations of abnormal acceleration data based on the general acceleration value. And the vehicle trajectory data were reconstructed using Lagrange interpolation and Kalman filter algorithms. The approach was utilized on NGSIM trajectory data. The results show that the acceleration values of the proposed method range from −6.69 m/s2 to 4.96 m/s2, with a standard deviation of 0.87. The reconstructed results are more closely matching drivers' physiological capabilities compared to other methods. These findings verify the reliability of the proposed approach and notably improve the quality of the trajectory data. It provides critical foundational data support for traffic planning, design, and management.

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Enhancing Vehicle Trajectory Quality: A Two‐Step Data Reconstruction Method Using Wavelet Transform and Normal Acceleration Value

Zhang,

Gao,

Zhou

2024

Engineering Reports

View full text Add to dashboard Cite

show abstract

Artificial Intelligence-Driven Multimodal Route Planning: Addressing Dynamic Unavailability and Disruptions

Prakash,

Mehta,

Sharma

2024

IEEE Access

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Graph Neural Networks for Routing Optimization: Challenges and Opportunities

Jiang,

Han,

Zhang

et al. 2024

Sustainability

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In this paper, we explore the emerging role of graph neural networks (GNNs) in optimizing routing for next-generation communication networks. Traditional routing protocols, such as OSPF or the Dijkstra algorithm, often fall short in handling the complexity, scalability, and dynamic nature of modern network environments, including unmanned aerial vehicle (UAV), satellite, and 5G networks. By leveraging their ability to model network topologies and learn from complex interdependencies between nodes and links, GNNs offer a promising solution for distributed and scalable routing optimization. This paper provides a comprehensive review of the latest research on GNN-based routing methods, categorizing them into supervised learning for network modeling, supervised learning for routing optimization, and reinforcement learning for dynamic routing tasks. We also present a detailed analysis of existing datasets, tools, and benchmarking practices. Key challenges related to scalability, real-world deployment, explainability, and security are discussed, alongside future research directions that involve federated learning, self-supervised learning, and online learning techniques to further enhance GNN applicability. This study serves as the first comprehensive survey of GNNs for routing optimization, aiming to inspire further research and practical applications in future communication networks.

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Hyper-relational interaction modeling in multi-modal trajectory prediction for intelligent connected vehicles in smart cites

Cited by 4 publications

References 30 publications

Enhancing Vehicle Trajectory Quality: A Two‐Step Data Reconstruction Method Using Wavelet Transform and Normal Acceleration Value

Enhancing Vehicle Trajectory Quality: A Two‐Step Data Reconstruction Method Using Wavelet Transform and Normal Acceleration Value

Artificial Intelligence-Driven Multimodal Route Planning: Addressing Dynamic Unavailability and Disruptions

Graph Neural Networks for Routing Optimization: Challenges and Opportunities

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