DRL-GAT-SA: Deep reinforcement learning for autonomous driving planning based on graph attention networks and simplex architecture

Peng, Yanfei; Tan, Guozhen; Si, Huaiwei; Li, Jianping

doi:10.1016/j.sysarc.2022.102505

Cited by 21 publications

(5 citation statements)

References 15 publications

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“…This approach combines Graph Attention Networks with DRL. The system, which incorporates Attention Networks and Simplex Architecture, ensures the safety of vehicle driving and efficiently prevents crashes [26] . This paper focuses on the problem of capturing in the absence of boundary conditions.…”

Section: Introductionmentioning

confidence: 99%

A collaborative siege method of multiple unmanned vehicles based on reinforcement learning

Su,

Pu,

Wang

et al. 2024

Intelligence & Robotics

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A method based on multi-agent reinforcement learning is proposed to tackle the challenges to capture escaping Target by Unmanned Ground Vehicles (UGVs). Initially, this study introduces environment and motion models tailored for cooperative UGV capture, along with clearly defined success criteria for direct capture. An attention mechanism integrated into the Soft Actor-Critic (SAC) is leveraged, directing focus towards pivotal state features pertinent to the task while effectively managing less relevant aspects. This allows capturing agents to concentrate on the whereabouts and activities of the target agent, thereby enhancing coordination and collaboration during pursuit. This focus on the target agent aids in refining the capture process and ensures precise estimation of value functions. The reduction in superfluous activities and unproductive scenarios amplifies efficiency and robustness. Furthermore, the attention weights dynamically adapt to environmental shifts. To address constrained incentives arising in scenarios with multiple vehicles capturing targets, the study introduces a revamped reward system. It divides the reward function into individual and cooperative components, thereby optimizing both global and localized incentives. By facilitating cooperative collaboration among capturing UGVs, this approach curtails the action space of the target UGV, leading to successful capture outcomes. The proposed technique demonstrates enhanced capture success compared to previous SAC algorithms. Simulation trials and comparisons with alternative learning methodologies validate the effectiveness of the algorithm and the design approach of the reward function.

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Section: Introductionmentioning

confidence: 99%

A collaborative siege method of multiple unmanned vehicles based on reinforcement learning

Su,

Pu,

Wang

et al. 2024

Intelligence & Robotics

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“…and decode hidden relationships between each timeslot remains burdensom Furthermore, transport and core networks also require the ability to understand traffi (congestion) patterns, resource utilization, and anomaly detection in complex topolo graphs [11][12][13]. Therefore, before focusing on other potential issues in E2E networkin one key research is the selection of optimization algorithms that handle complex grap structured topologies and extract data to support self-organizing capabilities [14,15]. Previous works supported by standardization, academia, and industry experts, a coming to conduct the creation of cutting-edge testbeds and simulation tools for netwo intelligence [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, transport and core networks also require the ability to understand traffic (congestion) patterns, resource utilization, and anomaly detection in complex topology graphs [11][12][13]. Therefore, before focusing on other potential issues in E2E networking, one key research is the selection of optimization algorithms that handle complex graph-structured topologies and extract data to support self-organizing capabilities [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Intelligent End-to-End Networking Solutions: Integrating Graph Neural Networks and Deep Reinforcement Learning Approaches

Tam,

Ros,

Song

et al. 2024

Electronics

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This paper provides a comprehensive survey of the integration of graph neural networks (GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions. We delve into the fundamentals of GNN, its variants, and the state-of-the-art applications in communication networking, which reveal the potential to revolutionize access, transport, and core network management policies. This paper further explores DRL capabilities, its variants, and the trending applications in E2E networking, particularly in enhancing dynamic network (re)configurations and resource management. By fusing GNN with DRL, we spotlight novel approaches, ranging from radio access networks to core management and orchestration, across E2E network layers. Deployment scenarios in smart transportation, smart factory, and smart grids demonstrate the practical implications of our survey topic. Lastly, we point out potential challenges and future research directions, including the critical aspects for modelling explainability, the reduction in overhead consumption, interoperability with existing schemes, and the importance of reproducibility. Our survey aims to serve as a roadmap for future developments in E2E networking, guiding through the current landscape, challenges, and prospective breakthroughs in the algorithm modelling toward network automation using GNN and DRL.

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“…In the Internet of Things (IoT) era, safety-critical systems have also been growingly more distributed and based on embedded modules [13][14][15], such as those used in the control functions of Unmanned Ground Vehicles (UGVs) [16][17][18][19][20][21][22][23][24], Unmanned Aerial Vehicles (UAVs) [25][26][27], and in a new generation of distributed Communication-Based Train Control (CBTC) systems for metro and railway signaling [28,29]. Furthermore, the increased flexibility and processing capability provided by recent Field-Programmable Gate Arrays (FPGAs) and other Programmable Logic Devices (PLDs) allows these elements to play an even more important role in cutting-edge safety-critical systems than today [30,31] while keeping design costs reasonable for achieving the needed efficiency and performance levels [32,33].…”

Section: Introductionmentioning

confidence: 99%

Design and Assurance of Safety-Critical Systems with Artificial Intelligence in FPGAs: The Safety ArtISt Method and a Case Study of an FPGA-Based Autonomous Vehicle Braking Control System

Silva Neto,

Silva,

Camargo

et al. 2023

Electronics

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With the advancements in utilizing Artificial Intelligence (AI) in embedded safety-critical systems based on Field-Programmable Gate Arrays (FPGAs), assuring that these systems meet their safety requirements is of paramount importance for their revenue service. Based on this context, this paper has two main objectives. The first of them is to present the Safety ArtISt method, developed by the authors to guide the lifecycle of AI-based safety-critical systems, and emphasize its FPGA-oriented tasks and recommended practice towards safety assurance. The second one is to illustrate the application of Safety ArtISt with an FPGA-based braking control system for autonomous vehicles relying on explainable AI generated with High-Level Synthesis. The results indicate that Safety ArtISt played four main roles in the safety lifecycle of AI-based systems for FPGAs. Firstly, it provided guidance in identifying the safety-critical role of activities such as sensitivity analyses for numeric representation and FPGA dimensioning to achieve safety. Furthermore, it allowed building qualitative and quantitative safety arguments from analyses and physical experimentation with actual FPGAs. It also allowed the early detection of safety issues—thus reducing project costs—and, ultimately, it uncovered relevant challenges not discussed in detail when designing safety-critical, explainable AI for FPGAs.

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DRL-GAT-SA: Deep reinforcement learning for autonomous driving planning based on graph attention networks and simplex architecture

Cited by 21 publications

References 15 publications

A collaborative siege method of multiple unmanned vehicles based on reinforcement learning

A collaborative siege method of multiple unmanned vehicles based on reinforcement learning

A Survey of Intelligent End-to-End Networking Solutions: Integrating Graph Neural Networks and Deep Reinforcement Learning Approaches

Design and Assurance of Safety-Critical Systems with Artificial Intelligence in FPGAs: The Safety ArtISt Method and a Case Study of an FPGA-Based Autonomous Vehicle Braking Control System

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