System Modeling and Performance Evaluation of Predictive QoS for Future Tele-Operated Driving

Schippers, Hendrik; Schüler, Cedrik; Sliwa, Benjamin; Wietfeld, Christian

doi:10.1109/syscon53536.2022.9773810

Cited by 5 publications

(11 citation statements)

References 13 publications

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“…Higher data rates typically lead to lower latency, as more data can be transmitted and processed quickly and vice versa [32,91]. Coverage can be less in rural areas than in urban, which can lead to more latency, indicating that latency depends on location as well [32]. Some studies have shown that latency has a more significant effect on teleoperation than actuator delay [18].…”

Section: Interdependency Between Network Parameters and Latencymentioning

confidence: 99%

“…Network quality in a system (e.g., teleoperation) can be defined by its QoS and quality of experience (QoE) [ 31 , 32 ], also known as objective and subjective factors, respectively. The objective factors include the network performance parameters.…”

Section: Impact Of Network Latency On Teleoperationmentioning

confidence: 99%

“…The objective factors include the network performance parameters. Meanwhile, the subjective factors include user performance parameters such as user interface, video quality, driving precision, comfort level, manoeuvre security, and interruptions [ 32 ].…”

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: Interdependency Between Network Parameters and Latencymentioning

confidence: 99%

Section: Impact Of Network Latency On Teleoperationmentioning

confidence: 99%

See 1 more Smart Citation

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

Kamtam,

Lu,

Bouali

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Experiments with testbeds represent a viable solution to emulate the behavior of real systems with a high level of realism. For example, in [39] the authors presented a testbed built using an RC car equipped with a 5G modem, as well as high-resolution cameras and LiDAR sensors. While this approach can achieve a high level of realism, it presents several limitations in terms of scalability, preventing evaluations with a large number of users.…”

Section: Testbeds and Simulation Platformsmentioning

confidence: 99%

Towards AI-Native Vehicular Communications

Rizzo,

Liotou,

Maret

et al. 2023

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)

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The role of fast yet reliable wireless communications in various application domains is getting ever more important. At the same time, as use cases are becoming more and more complex, application requirements are getting ever more stringent. One example is intelligent transportation, where the efficiency and reliability of wireless data delivery is essential for effective service support. As a consequence, in this context the adoption of AI techniques is widely considered crucial for enabling vehicular communications to adapt to dynamic changes of the environment. In this position paper, we discuss some representative applications of advanced AI tools in vehicular communications. In particular, we elaborate on the potential of distributed learning based on federated learning, of proactive service provisioning, and of graph neural network for enabling AI-native vehicular communications.

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“…The 5G network must be designed to provide these Quality-of-Service (QoS) requirements, which especially apply when the AV is crossing country borders. In addition, predictive QoS [8] mechanisms are needed in order to anticipate a potential degradation in the QoS, and then reduce speed, stop the AV, or even drive the AV to another route where the 5G network can support the service.…”

Section: A Use Case 1: Remote Drivingmentioning

confidence: 99%

5GMED Architecture for Advanced Automotive and Railway Communication Services in Cross-Border Scenarios

Nasreddine

Carmona-Cejudo

Vilalta

et al. 2022

2022 IEEE Future Networks World Forum (FNWF)

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Cooperative, Connected and Automated Mobility (CCAM) and Future Railway Mobile Communications System (FRMCS) use cases present strict requirements in terms of service end-to-end latencies, data-rates, reliability and service availability, posing challenges in the design of 5G network architectures for cross-border scenarios, where current roaming mechanisms introduce large latencies and service disruption times. The 5GMED project proposes a novel 5G network architecture for the Mediterranean corridor cross-border between Spain and France, where the challenges imposed by CCAM and FRMCS in crossborder scenarios are addressed. In this paper, we describe crossborder challenges and present the 5GMED network architecture that will be deployed and evaluated in large-scale trials.

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System Modeling and Performance Evaluation of Predictive QoS for Future Tele-Operated Driving

Cited by 5 publications

References 13 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

Towards AI-Native Vehicular Communications

5GMED Architecture for Advanced Automotive and Railway Communication Services in Cross-Border Scenarios

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