5G-enabled V2X communications for vulnerable road users safety applications: a review

Zoghlami, Chaima; Kacimi, Rahim; Dhaou, Riadh

doi:10.1007/s11276-022-03191-7

Cited by 22 publications

(11 citation statements)

References 118 publications

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“…The same limitation can be found in the current literature of V2X safety-critical applications [15]- [18]: the proposals lay out approaches to support such applications but leave it unaddressed what the influence will be after the C-V2X got deprived of 60% of its bandwidth.…”

Section: Introductionmentioning

confidence: 74%

Is 30 MHz Enough for C-V2X?

Sunuwar¹,

Kim²,

Reyes³

2023

Preprint

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Connected vehicles are no longer a futuristic dream coming out of a science fiction, but they are swiftly taking a bigger part of one's everyday life. One of the key technologies actualizing the connected vehicles is vehicle-to-everything communications (V2X). Nonetheless, the United States (U.S.) federal government decided to reallocate the spectrum band that used to be dedicated to V2X uses (namely, the "5.9 GHz band") and to leave only 40% of the original chunk (i.e., 30 MHz of bandwidth) for V2X. It ignited concern of whether the 30-MHz spectrum suffices key V2X safety messages and the respective applications. This paper aims at addressing this issue with the particular focus on the New Radio (NR)-V2X Mode 1. We lay out an extensive study on the safety message types and their latency requirements. Then, we present our simulation results examining whether they can be supported in the 30-MHz spectrum setup.

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Section: Introductionmentioning

confidence: 74%

Is 30 MHz Enough for C-V2X?

Sunuwar¹,

Kim²,

Reyes³

2023

Preprint

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“…The interaction issues between AVs and pedestrians have attracted a lot of attention in HCI research. Past research has explored various design approaches to facilitate AV-pedestrian interaction, such as audio signals (engine sounds, bell rings, and soft alarm sounds) [72], Light Signals [16,17,30], Augmented Reality (AR) projection on the road [63], Mobile Applications and Connected Devices [36,69], Internet Vehicle-to-Everything (V2X) Technology [75], Interfaces on Street Infrastructure [45] and external Human-Machine interface (eHMI) [26,42]. Empirical research on the aforementioned methods indicates that these approaches have their respective advantages in terms of clarity, familiarity, and politeness, yet they still present challenges.…”

Section: Related Work 21 Autonomous Vehicles-pedestrian Interaction D...mentioning

confidence: 99%

“…Regarding clarity, the effectiveness of Audio Signals is limited in conveying risk levels [72]; Light Signals are also inefficient in ambiguous situations and busy scenarios [16,17,30]; Street Infrastructure Interfaces may cause visual confusion and fail to indicate multiple pedestrians' intents, affecting clarity [45]. Regarding familiarity, AR [63] offers novel interaction methods but its complexity can increase cognitive load, reducing familiarity; the effectiveness of Audio Signals in conveying risk levels varies across environments, limiting clarity; Mobile Apps and Connected Devices provide intuitive, personalized interactions for understanding AVs' intentions but their effectiveness is contingent on widespread adoption and user familiarity [36,69]; V2X Technology enhances safety through effective communication but faces challenges in network latency and reliability, reflecting limited user familiarity [75].…”

Section: Related Work 21 Autonomous Vehicles-pedestrian Interaction D...mentioning

confidence: 99%

"It Must Be Gesturing Towards Me": Gesture-Based Interaction between Autonomous Vehicles and Pedestrians

Chang,

Chen,

Dong

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Interacting with pedestrians understandably and efficiently is one of the toughest challenges faced by autonomous vehicles (AVs) due to the limitations of current algorithms and external humanmachine interfaces (eHMIs). In this paper, we design eHMIs based on gestures inspired by the most popular method of interaction between pedestrians and human drivers. Eight common gestures were selected to convey AVs' yielding or non-yielding intentions at uncontrolled crosswalks from previous literature. Through a VR experiment (N1 = 31) and a following online survey (N2 = 394), we discovered significant differences in the usability of gesture-based eHMIs compared to current eHMIs. Good gesture-based eHMIs increase the efficiency of pedestrian-AV interaction while ensuring safety. Poor gestures, however, cause misinterpretation. The underlying reasons were explored: ambiguity regarding the recipient of the signal and whether the gestures are precise, polite, and familiar to pedestrians. Based on this empirical evidence, we discuss potential opportunities and provide valuable insights into developing comprehensible gesture-based eHMIs in the future to support better interaction between AVs and other road users. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI.

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“…This entails the connectivity demands from millions of sensors for context-aware driving [2]. This type of intelligent transportation falls into the category of V2X communication [3]. The system resides on multiple sub-communication networks like Vehicle to Vehicle (V2V), Vehicle to Network (V2N), Vehicle to Infrastructure (V2I), and Vehicle to Pedestrians (V2P) [4].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Network Slicing in V2X Networks – A Comprehensive Review

Abood

Wang

et al. 2023

JAIT

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ABSTRACT- The rise of the internet of things (IoT) and autonomous systems has made connecting vehicles more critical. Connected autonomous vehicles can create diverse communication networks that can improve the environment and offer contemporary applications. With the advent of 5G networks, vehicle-to-everything (V2X) networks are expected to be highly intelligent, reside on superfast, reliable, and low-latency connections. Network slicing, Machine Learning (ML), and Deep Learning (DL) are related to network automation and optimization in V2X communication. Machine Learning and Deep Learning (ML/DL) with network slicing aims to optimize the performance, reliability of the V2X network, personalized services, reduced costs, and scalability and enhance the overall driving experience. These advantages can ultimately lead to a safer and more efficient transportation system. However, existing Long-Term Evolution (LTE) systems and enabling 5G technologies cannot meet such dynamic requirements without adding higher complexity levels. Machine learning algorithms mitigate complexity levels, which can be highly instrumental in such vehicular communication systems. This study aims to review V2X slicing based on a proposed taxonomy that describes the enablers of slicing, a different configuration of slicing, the requirements of slicing, and the ML algorithm used to control and manage to slice. This study also reviews various research works established in network slicing through ML algorithms to enable V2X communication use cases, focusing on V2X network slicing and considering efficient control and management. The enabler technologies are considered in light of the network requirements, particular configurations, and the underlying methods and algorithms, with a review of some critical challenges and possible solutions available. The paper concludes with a future roadmap by discussing some open research issues and future directions.

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5G-enabled V2X communications for vulnerable road users safety applications: a review

Cited by 22 publications

References 118 publications

Is 30 MHz Enough for C-V2X?

Is 30 MHz Enough for C-V2X?

"It Must Be Gesturing Towards Me": Gesture-Based Interaction between Autonomous Vehicles and Pedestrians

Intelligent Network Slicing in V2X Networks – A Comprehensive Review

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