Cooperative Behavior Planning for Automated Driving using Graph Neural Networks

Klimke, Marvin; Völz, Benjamin; Buchholz, Michael

doi:10.48550/arxiv.2202.11376

Cited by 2 publications

(3 citation statements)

References 21 publications

(29 reference statements)

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“…Finally, Klimke et al [149] utilized GNNs for developing a cooperative motion planning of multiple vehicles at urban intersections. By using the graph representation of the vehicles, they were able to deal with the dynamic number of vehicles in the scene.…”

Section: Cai Et Al Proposed Dignetmentioning

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: Cai Et Al Proposed Dignetmentioning

confidence: 99%

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Rahmani

Baghbani

Bouguila

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…In [39], a highway lane-changing scenario was modeled as a directed graph, and graph representation was implemented based on the relative position between vehicles. Furthermore, in [124], an intersection scenario was constructed, and the connection between vehicles was modeled based on their turning intentions. In [36], an attention mechanism was introduced to capture the mutual interplay between vehicles to achieve better cooperative control.…”

Section: Comprehensive State Representationmentioning

confidence: 99%

“…Graph representation is implemented based on the relative position between vehicles. [124] GCN+TD3 Intersection Simulation in Highway-env Flow rate in the intersection is significantly improved.…”

Section: Graph Modelingmentioning

confidence: 99%

Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends

Liu,

Li,

Tang

et al. 2023

Sensors

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The proper functioning of connected and autonomous vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaborative decision-making technology for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) methods have become an efficient way in solving decision-making problems. However, with the development of computing technology, graph reinforcement learning (GRL) methods have gradually demonstrated the large potential to further improve the decision-making performance of CAVs, especially in the area of accurately representing the mutual effects of vehicles and modeling dynamic traffic environments. To facilitate the development of GRL-based methods for autonomous driving, this paper proposes a review of GRL-based methods for the decision-making technologies of CAVs. Firstly, a generic GRL framework is proposed in the beginning to gain an overall understanding of the decision-making technology. Then, the GRL-based decision-making technologies are reviewed from the perspective of the construction methods of mixed autonomy traffic, methods for graph representation of the driving environment, and related works about graph neural networks (GNN) and DRL in the field of decision-making for autonomous driving. Moreover, validation methods are summarized to provide an efficient way to verify the performance of decision-making methods. Finally, challenges and future research directions of GRL-based decision-making methods are summarized.

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Cooperative Behavior Planning for Automated Driving using Graph Neural Networks

Cited by 2 publications

References 21 publications

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Graph Neural Networks for Intelligent Transportation Systems: A Survey

Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends

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