Multi-Task Y-Shaped Graph Neural Network for Point Cloud Learning in Autonomous Driving

Zou, Xin; Li, Kenli; Li, Yangfan; Wei, Wei; Chen, Cen

doi:10.1109/tits.2022.3150155

Cited by 9 publications

(2 citation statements)

References 26 publications

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“…Zou et al [114] presented a multi-task Y-shaped graph neural network, MTYGNNN, for exploiting 3D point clouds. MTYGNN has two branches for performing the classification and segmentation tasks in point clouds at the same time.…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domainspecific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNNbased frameworks.

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Section: Autonomous Vehiclesmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…The drawbacks of optical cameras are lack of depth information and sensitivity to illumination, while Lidar sensors can provide the accurate depth information and do not depend on lighting. Many deep learning methods are proposed to percept objects from the point cloud data produced by Lidar [5]. However, existing point cloud learning, such as Pointnet [6], ignore the benefits of collaborative learning from multiple agents.…”

Section: Introductionmentioning

confidence: 99%

A Digital-Twin-Empowered Lightweight Model-Sharing Scheme for Multirobot Systems

Xiong

Wang

Leng

et al. 2023

IEEE Internet Things J.

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Multi-robot system for manufacturing is an Industry Internet of Things (IIoT) paradigm with significant operational cost savings and productivity improvement, where Unmanned Aerial Vehicles (UAVs) are employed to control and implement collaborative productions without human intervention. This mission-critical system relies on 3-Dimension (3-D) scene recognition to improve operation accuracy in the production line and autonomous piloting. However, implementing 3-D point cloud learning, such as Pointnet, is challenging due to limited sensing and computing resources equipped with UAVs. Therefore, we propose a Digital Twin (DT) empowered Knowledge Distillation (KD) method to generate several lightweight learning models and select the optimal model to deploy on UAVs. With a digital replica of the UAVs preserved at the edge server, the DT system controls the model sharing network topology and learning model structure to improve recognition accuracy further. Moreover, we employ network calculus to formulate and solve the model sharing configuration problem toward minimal resource consumption, as well as convergence. Simulation experiments are conducted over a popular point cloud dataset to evaluate the proposed scheme. Experiment results show that the proposed model sharing scheme outperforms the individual model in terms of computing resource consumption and recognition accuracy.

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