A Delay Compensation Framework for Connected Testbeds

Guo, Sicong; Liu, Yuzhang; Zheng, Yingshi; Ersal, Tulga

doi:10.1109/tsmc.2021.3091974

Cited by 11 publications

(9 citation statements)

References 24 publications

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“…Zheng et al [ 42 ] measure the delay between Michigan and California. Guo et al [ 85 ] collect variable delay from real network data using the User Datagram Protocol (UDP). Saparia et al [ 86 ] collect data from 4G LTE networks.…”

Section: Impact Of Network Latency On Teleoperationmentioning

confidence: 99%

Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies

Kamtam,

Lu,

Bouali

et al. 2024

Sensors

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With remarkable advancements in the development of connected and autonomous vehicles (CAVs), the integration of teleoperation has become crucial for improving safety and operational efficiency. However, teleoperation faces substantial challenges, with network latency being a critical factor influencing its performance. This survey paper explores the impact of network latency along with state-of-the-art mitigation/compensation approaches. It examines cascading effects on teleoperation communication links (i.e., uplink and downlink) and how delays in data transmission affect the real-time perception and decision-making of operators. By elucidating the challenges and available mitigation strategies, the paper offers valuable insights for researchers, engineers, and practitioners working towards the seamless integration of teleoperation in the evolving landscape of CAVs.

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Section: Impact Of Network Latency On Teleoperationmentioning

confidence: 99%

Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies

Kamtam,

Lu,

Bouali

et al. 2024

Sensors

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show abstract

“…The virtual integration and coupling of geographically scattered real-time simulators across communication networks are done in a closed loop [76]. A networked closed-loop system is a system that integrates scattered components into a closed loop via a communication channel.…”

Section: ) Closed Loop Nature Of Co-simulation Systemmentioning

confidence: 99%

“…This method improves prediction accuracy when models are high-fidelity. The drawbacks include: When models are inaccurate or unavailable, their applications may be hindered [76], and When dynamic models are unavailable, they may not be robust and applicable [116] Examples of modelbased implementation include the Predictive control scheme implemented in [117] [118], the active resilient control scheme implemented in [119], the Model-based event-triggered predictive control (MB-ETPC) scheme implemented in [120], the Adaptive unscented Kalman filter framework implemented in [121], Variable-horizon model predictive control scheme implemented in [121] 3) Model-Free Delay Compensation Framework…”

Section: ) Delay Compensation Framework For Time-varying Signalsmentioning

confidence: 99%

Overview of Interface Algorithms, Interface Signals, Communication and Delay in Real-Time Co-Simulation of Distributed Power Systems

Buraimoh,

Ozkan,

Timilsina

et al. 2023

IEEE Access

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In recent years, there has been growing research interest in the virtual integration of hardware and software assets across different geographical locations. However, achieving joint real-time simulation in virtually connected laboratories presents several challenges that must be addressed. One critical aspect involves selecting suitable interface algorithms to conserve energy, ensure signal decomposition, and reconstruct data accurately, thereby preventing distortions. Additionally, communication latencies must be taken into account to maintain experiment fidelity. The establishment of fast and reliable communication between real-time simulators in different laboratories through coupling interfaces, is of utmost importance for successful real-time cosimulation. Nevertheless, assuming the constant availability of reliable and delay-free communication is unrealistic, which can lead to performance degradation and system instability. These requirements pose significant obstacles to implementing virtual integration involving various real-time simulators and hardware-in-the-loop setups across diverse laboratories. The real-time co-simulation of power systems, in particular, is highly susceptible to ineffective interface algorithms, signal decomposition, and reconstruction, as well as communication delays, potentially causing loss of synchronism and negatively impacting simulation fidelity. Consequently, these limitations render such setups unsuitable for dynamic and transient studies. In light of these challenges, this paper aims to provide an overview of interface algorithms, signal decomposition and reconstruction techniques, communication protocols, and delay compensation approaches. The goal is to ensure system fidelity, enhance coupling point modeling, and improve the accuracy of real-time co-simulation in virtual environments across long distances while preserving ongoing research confidentiality, safeguarding intellectual property, and facilitating collaborative research.

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“…For this reason, there is a possibility that the robustness of many reported evaluation methods may not be preserved if outliers of communication delay are not considered in common scenarios, such as teleoperation via shared impedance control [ 29 ], delayed bilateral control [ 30 ], evaluation of display methods [ 18 ], and so on. In addition, several studies have proved that compensation strategies are effective in reducing the effect of large communication delays on vehicle mobility [ 6 , 31 , 32 , 33 ]. Again, what seems to be lacking is the considerations regarding communication outlier detection and the implementation of countermeasures.…”

Section: Introductionmentioning

confidence: 99%

Communication Delay Outlier Detection and Compensation for Teleoperation Using Stochastic State Estimation

Kim,

Hwang,

Lim

et al. 2024

Sensors

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There have been numerous studies attempting to overcome the limitations of current autonomous driving technologies. However, there is no doubt that it is challenging to promise integrity of safety regarding urban driving scenarios and dynamic driving environments. Among the reported countermeasures to supplement the uncertain behavior of autonomous vehicles, teleoperation of the vehicle has been introduced to deal with the disengagement of autonomous driving. However, teleoperation can lead the vehicle to unforeseen and hazardous situations from the viewpoint of wireless communication stability. In particular, communication delay outliers that severely deviate from the passive communication delay should be highlighted because they could hamper the cognition of the circumstances monitored by the teleoperator, or the control signal could be contaminated regardless of the teleoperator’s intention. In this study, communication delay outliers were detected and classified based on the stochastic approach (passive delays and outliers were estimated as 98.67% and 1.33%, respectively). Results indicate that communication delay outliers can be automatically detected, independently of the real-time quality of wireless communication stability. Moreover, the proposed framework demonstrates resilience against outliers, thereby mitigating potential performance degradation.

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A Delay Compensation Framework for Connected Testbeds

Cited by 11 publications

References 24 publications

Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies

Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies

Overview of Interface Algorithms, Interface Signals, Communication and Delay in Real-Time Co-Simulation of Distributed Power Systems

Communication Delay Outlier Detection and Compensation for Teleoperation Using Stochastic State Estimation

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