Effects of Signal Latency on Human Performance in Teleoperations

Blackett, Claire; Fernandes, Alexandra; Teigen, Espen; Thoresen, Thomas

doi:10.1007/978-3-030-85540-6_50

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…Finally, the overall end-to-end latency (∆ E2E ) or RTT is the sum of delays of all the components from uplink to downlink over the network. The end-to-end latency can be represented in an equation as Human operators have demonstrated the ability to perceive delays as low as 10 to 20 ms [28]. A latency of around 300 ms has a noticeable impact on the operator behaviour.…”

Section: End-to-end Delaymentioning

confidence: 99%

“…The parameters of simulated/emulated networks depend on how the user defines them. For example, in a simulation experiment, driving control had a latency of 170 ms and flight control had 1.5 to 3 s [ 28 ]. Another example, an NS3–IP-based emulator, had a latency of 16–19 ms [ 10 ].…”

Section: Impact Of Network Latency On Teleoperationmentioning

confidence: 99%

“…Human operators have demonstrated the ability to perceive delays as low as 10 to 20 ms [ 28 ]. A latency of around 300 ms has a noticeable impact on the operator behaviour.…”

Section: Impact Of Network Latency On Teleoperationmentioning

confidence: 99%

See 2 more Smart Citations

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: End-to-end Delaymentioning

confidence: 99%

Section: Impact Of Network Latency On Teleoperationmentioning

confidence: 99%

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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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“…b. Let immediate interaction by recognizing gestures with a latency below 3 s [34]. c. Use signalling and systems already available in airport environments.…”

Section: Research Frameworkmentioning

confidence: 99%

Real-Time Visual Recognition of Ramp Hand Signals for UAS Ground Operations

Carro

LópezHernández

Granados

2023

J Intell Robot Syst

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We describe the design and validation of a vision-based system that allows the dynamic identification of ramp signals performed by airport ground staff. This ramp signals’ recognizer increases the autonomy of unmanned vehicles and prevents errors caused by visual misinterpretations or lack of attention from the pilot of manned vehicles. This system is based on supervised machine learning techniques, developed with our own training dataset and two models. The first model is based on a pre-trained Convolutional Pose Machine followed by a classifier, for which we have evaluated two possibilities: A Random Forest and a Multi-Layer Perceptron based classifier. The second model is based on a single Convolutional Neural Network that classifies the gestures directly imported from real images. When experimentally tested, the first model proved to be more accurate and scalable than the second one. Its strength relies on a better capacity to extract information from the images and transform the domain of pixels into spatial vectors, which increases the robustness of the classification layer. The second model instead is more adequate for gestures’ identification in low visibility environments, such as during night operations, conditions in which the first model appeared to be more limited, segmenting the shape of the operator. Our results support the use of supervised learning and computer vision techniques for the correct identification and classification of ramp hand signals performed by airport marshallers.

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