Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3GPP Standards

Khan, Muhammad Jalal; Khan, Manzoor Ahmed; Malik, Sumbal; Kulkarni, Parag; Alkaabi, Najla; Ullah, Obaid; El-Sayed, Hesham; Ahmed, Amir; Turaev, Sherzod

doi:10.3390/s23042261

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…This standardization process involves progressive enhancements as can be seen in Figure 4 . C-V2X also defines a PC5 interface facilitating direct V2X communication via the sidelink, as well as a Uu interface enabling communication between the terminal and base station through the uplink/downlink [ 42 ]. With its extensive network capacity and wide coverage, C-V2X is well-suited for supporting collaborative perception in the internet of vehicles (IoV) environment, enhancing data transmission reliability, reducing transmission delays, and minimizing frequent horizontal switching within the network.…”

Section: Collaborative Perception Sharingmentioning

confidence: 99%

Collaborative Perception—The Missing Piece in Realizing Fully Autonomous Driving

Malik,

Khan,

Khan

et al. 2023

Sensors

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Environment perception plays a crucial role in enabling collaborative driving automation, which is considered to be the ground-breaking solution to tackling the safety, mobility, and sustainability challenges of contemporary transportation systems. Despite the fact that computer vision for object perception is undergoing an extraordinary evolution, single-vehicle systems’ constrained receptive fields and inherent physical occlusion make it difficult for state-of-the-art perception techniques to cope with complex real-world traffic settings. Collaborative perception (CP) based on various geographically separated perception nodes was developed to break the perception bottleneck for driving automation. CP leverages vehicle-to-vehicle and vehicle-to-infrastructure communication to enable vehicles and infrastructure to combine and share information to comprehend the surrounding environment beyond the line of sight and field of view to enhance perception accuracy, lower latency, and remove perception blind spots. In this article, we highlight the need for an evolved version of the collaborative perception that should address the challenges hindering the realization of level 5 AD use cases by comprehensively studying the transition from classical perception to collaborative perception. In particular, we discuss and review perception creation at two different levels: vehicle and infrastructure. Furthermore, we also study the communication technologies and three different collaborative perception message-sharing models, their comparison analyzing the trade-off between the accuracy of the transmitted data and the communication bandwidth used for data transmission, and the challenges therein. Finally, we discuss a range of crucial challenges and future directions of collaborative perception that need to be addressed before a higher level of autonomy hits the roads.

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Section: Collaborative Perception Sharingmentioning

confidence: 99%

Collaborative Perception—The Missing Piece in Realizing Fully Autonomous Driving

Malik,

Khan,

Khan

et al. 2023

Sensors

Self Cite

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“…It is used for V2V, V2I, and V2P communications, the exchange of information being carried out with the help of sidelink (SL) transmission. Within PC5, C-V2X applications operate in the 5.9 GHz ITS band for short-range applications, on a distance of less than 1 km [115].…”

Section: (3)mentioning

confidence: 99%

Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication

Lazar,

Pauca,

Maxim

et al. 2023

Sensors

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A suitable control architecture for connected vehicle platoons may be seen as a promising solution for today’s traffic problems, by improving road safety and traffic flow, reducing emissions and fuel consumption, and increasing driver comfort. This paper provides a comprehensive overview concerning the defining levels of a general control architecture for connected vehicle platoons, intending to illustrate the options available in terms of sensor technologies, in-vehicle networks, vehicular communication, and control solutions. Moreover, starting from the proposed control architecture, a solution that implements a Cooperative Adaptive Cruise Control (CACC) functionality for a vehicle platoon is designed. Also, two control algorithms based on the distributed model-based predictive control (DMPC) strategy and the feedback gain matrix method for the control level of the CACC functionality are proposed. The designed architecture was tested in a simulation scenario, and the obtained results show the control performances achieved using the proposed solutions suitable for the longitudinal dynamics of vehicle platoons.

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“…Existing autonomous driving technologies primarily focus on enhancing the intelligence of individual vehicles [6]. While deep learning technologies have contributed to the designing of more efficient resource allocation algorithms for autonomous driving systems with multiple vehicles and roadside base stations, the advent of wireless communication technologies such as 5G and Cellular Vehicle-to-Everything (C-V2X) has ushered in novel prospects for autonomous driving satisfying the need for reliable and fast down-link sensory data transmission from roadside base stations with wide-range coverage [7]. These technologies facilitate the reliable real-time exchange of perception information among the mix of vehicles and roadside base stations, thereby augmenting the distant perceptual capabilities of each vehicle and enhancing the safety of autonomous-driving vehicles [6,8].…”

Section: Introductionmentioning

confidence: 99%

Multi-Cell Cooperative Resource Allocation and Performance Evaluation for Roadside-Assisted Automated Driving

Yang,

Zhu,

et al. 2024

WEVJ

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The proliferation of wireless technologies, particularly the advent of 5G networks, has ushered in transformative possibilities for enhancing vehicular communication systems, particularly in the context of autonomous driving. Leveraging sensory data and mapping information downloaded from base stations using I2V links, autonomous vehicles in these networks present the promise of enabling distant perceptual abilities essential to completing various tasks in a dynamic environment. However, the efficient down-link transmission of vehicular network data via base stations, often relying on spectrum sharing, presents a multifaceted challenge. This paper addresses the intricacies of spectrum allocation in vehicular networks, aiming to resolve the thorny issues of cross-station interference and coupling while adapting to the dynamic and evolving characteristics of the vehicular environment. A novel approach is suggested involving the utilization of a multi-agent option-critic reinforcement learning algorithm. This algorithm serves a dual purpose: firstly, it learns the most efficient way to allocate spectrum resources optimally. Secondly, it adapts to the ever-changing dynamics of the environment by learning various policy options tailored to different situations. Moreover, it identifies the conditions under which a switch between these policy options is warranted as the situation evolves. The proposed algorithm is structured in two layers, with the upper layer consisting of policy options that are shared across all agents, and the lower layer comprising intra-option policies executed in a distributed manner. Through experimentation, we showcase the superior spectrum efficiency and communication quality achieved by our approach. Specifically, our approach outperforms the baseline methods in terms of training average reward convergence stability and the transmission success rate. Control-variable experiments also reflect the better adaptability of the proposed method as the environmental conditions change, underscoring the significant potential of the proposed method in aiding successful down-link transmissions in vehicular networks.

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Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3GPP Standards

Cited by 11 publications

References 20 publications

Collaborative Perception—The Missing Piece in Realizing Fully Autonomous Driving

Collaborative Perception—The Missing Piece in Realizing Fully Autonomous Driving

Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication

Multi-Cell Cooperative Resource Allocation and Performance Evaluation for Roadside-Assisted Automated Driving

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