DSRC &amp; C-V2X Comparison for Connected and Automated Vehicles in Different Traffic Scenarios

Jin, Yuan; Zhu, Quanxin

doi:10.48550/arxiv.2203.12553

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…To enable these activities, two sorts of technologies are used. The first one pertains to connectivity, and features many developed standards such as DSRC and C-V2X [137]. Secondly, we find AI technology and its branches (see Section II) dedicated to the automation part.…”

Section: B Connected and Autonomous Vehicles 1) Motivationmentioning

confidence: 99%

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions

Hafi,

Brik,

Frangoudis

et al. 2024

IEEE Access

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Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. The revolution of 6G networks is driven by massive data availability, moving from centralized and big data towards small and distributed data. This trend has motivated the adoption of distributed and collaborative ML/DL techniques. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique that recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks.INDEX TERMS 6G networks, wireless communication, federated deep learning, split deep learning, split federated learning.

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Section: B Connected and Autonomous Vehicles 1) Motivationmentioning

confidence: 99%

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions

Hafi,

Brik,

Frangoudis

et al. 2024

IEEE Access

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show abstract

“…9. According to research, C-V2X can offer more coverage (as shown in the picture), improved dependability, better support for quality of service (QoS), 360°non-line-of-sight (NLOS) awareness, and higher capacity than DSRC [46]. Both RATs are capable of supporting basic safety use cases, such as traffic light information and emergency vehicle notifications, road work, and emergency brake warnings, with end-to-end latency needs of about 100 milliseconds under conditions of moderate vehicular congestion.…”

Section: ) C-v2x and Dsrcmentioning

confidence: 99%

State-of-the-Art Review on Recent Advancements on Lateral Control of Autonomous Vehicles

et al. 2022

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The most well-known research on driverless vehicles at the moment is connected autonomous vehicles (CAVs), which reflects the future path for the self driving field. The development of connected autonomous vehicles (CAVs) is not only increasing logistics operations, but it is also opening up new possibilities for the industry's sustainable growth. In this review, we will explore the cloud-controlled wireless network-based model of cyber physical aspect of the autonomous vehicle, which is coupled with unmanned aerial vehicles (UAVs). Additionally, this model is Internet of Things (IoT) managed and AI-based, with a blockchain-based security mechanism. Additionally, we'll focus on lateral control in autonomous driving, particularly the lane change maneuver, taking social behavior into account. Here, we briefly reviewed Vehicle-to-Everything (V2X) communication, which is carried out by on-board sensors and connected wireless medium that enhance the lane departure processes while retaining human driver behavior relying on obstacle avoidance.

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“…The results demonstrate that 802.11bd outperformed its predecessor, 802.11p, in multiple V2X performance metrics and provide valuable observations and experience in operating the NGV standard. However, as a typical cyber-physical system, the existing study [25] has already verified that the performance of V2X will be affected by traffic scenarios. Most prior simulation-based studies have overly simplified the test scenarios to one-to-one or one-tomany linear arrays, thus overlooking the influence of intricate and dynamic large-scale traffic environments on the communication performance of the IEEE 802.11bd standard.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of IEEE 802.11bd and IEEE 802.11p on the Data Dissemination Properties in Dynamic Traffic Scenarios

Xue,

Gong,

2024

Applied Sciences

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With the rapid deployment of intelligent transportation systems in real-life applications, both dedicated short-range communications (DSRC) and cellular Vehicle-to-Everything (C-V2X), utilized to enable V2X communication, are undergoing extensive development to meet the quality of service (QoS) demands of advanced vehicular applications and scenarios. Compared to C-V2X, which lacks fully validated effective reliability, DSRC has undergone extensive field testing worldwide, ensuring its practicality. IEEE 802.11bd, as the next-generation V2X (NGV) standard within DSRC, is expected to greatly exceed the performance of its predecessor, 802.11p. However, existing studies mention that the ambient traffic environment will influence the performance of V2X due to the cyber-physical properties of V2X. To fully assess the advancements of NGV, this study presents a comparative analysis of IEEE 802.11bd and IEEE 802.11p, focusing on dynamic traffic conditions. Specifically, the technical advancements of the IEEE 802.11bd standard are first theoretically examined, emphasizing significant enhancements in aspects like modulation and coding schemes, coding rates, and channel coding. Subsequently, these critical technical enhancements are implemented in Veins, a simulation framework for the Internet of Vehicles (IoV), encompassing large-scale dynamic traffic scenarios. The simulation results indicate that the IEEE 802.11bd standard significantly enhances the data transfer rate compared to IEEE 802.11p, achieving a stable twofold increase. Furthermore, the data transmission latency is reduced by over half compared to IEEE 802.11p, while the data transmission reliability experiences a noteworthy 20% enhancement. Notably, the enhanced data transmission mode of the IEEE 802.11bd standard requires an increased signal-to-noise ratio (SNR). Additionally, this research evaluates the data dissemination properties in the IoV and finds that the traffic volume has a limited impact on the data propagation speed.

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DSRC & C-V2X Comparison for Connected and Automated Vehicles in Different Traffic Scenarios

Cited by 3 publications

References 16 publications

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions

State-of-the-Art Review on Recent Advancements on Lateral Control of Autonomous Vehicles

A Comparative Study of IEEE 802.11bd and IEEE 802.11p on the Data Dissemination Properties in Dynamic Traffic Scenarios

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