Artificial Intelligence for Vehicle-to-Everything: A Survey

Wang, Tong; Hussain, Azhar; Bo, Wang Xi; Maharjan, Sabita

doi:10.1109/access.2019.2891073

Cited by 212 publications

(116 citation statements)

References 104 publications

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“…Even though routing in VANETs has received more attention in the wireless-network community, there are still quite a few challenges that have not yet been carefully investigated. New directions in the development of VANET routing protocols include artificial intelligence [92] or trust in communications [93]. As future work, we are gathering statistics of VANET routing protocols reported in the literature to carry out meta-analysis of VANET performance.…”

Section: Discussionmentioning

confidence: 99%

Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics

et al. 2019

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In the last few years, many routing protocols have been proposed for vehicular ad hoc networks (VANETs) because of their specific characteristics. Protocols that use several metrics have been shown to be the most adequate to VANETs due to their effectiveness in dealing with dynamic environment changes due to vehicle mobility. Metrics such as distance, density, link stability, speed, and position were selected by the authors for the best proposal. Several surveys of routing proposals have been generated to categorize contributions and their application scenarios, but none of them focused on multimetric approaches. In this paper, we present a review of the routing protocols based on more than one metric to select the best route in a VANET. The main objective of this research was to present the contemporary most frequently used metrics in the different proposals and their application scenarios. This review helps in the selection protocols or the creation of metrics when a new protocol is designed.This survey of multimetric VANET routing protocols employed systematic literature-review (SLR) methodology in four well-knownown databases that allowed to analyze current state-of-the-art proposals. In addition, this paper provides a description of these multimetric routing protocols. Our findings indicate that distance and speed are the most popular and versatile metrics. Finally, we define some possible directions for future research related to the use of this class of protocols.

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Section: Discussionmentioning

confidence: 99%

Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics

et al. 2019

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“…However, this technology is still in an exploratory stage and faces several challenges, such as cybersecurity and traffic safety brought about by V2X applications [178,179]. Moreover, the integration of artificial intelligence (AI) and V2X has also drawn the attention of some researchers [180].…”

Section: Emerging Technologies For the Future Development Of Evsmentioning

confidence: 99%

Technology Development of Electric Vehicles: A Review

et al. 2019

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To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution.

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“…The recent advancement and applications of ML in VANETs were discussed in [31]. Detailed discussions of various ML methods used in VANETs were presented in [21], and the applications of various AI techniques in VANETs were discussed in [32].…”

Section: ) To the Best Of Our Knowledge No Comprehensive Survey Thmentioning

confidence: 99%

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

et al. 2020

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Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools, is extensively used to solve critical challenges in various domains. Vehicular ad hoc network (VANET) is expected to be the key role player in reducing road casualties and traffic congestion. To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts. Therefore, VANET faces a spectrum scarcity issue. Cognitive radio (CR) is a promising solution to overcome such an issue. CR-based VANET or CR-VANET must achieve several performance enhancement measures, including ultra-reliable and lowlatency communication. ML methods can be integrated with CR-VANET to make CR-VANET highly intelligent, achieve rapid adaptability to the dynamicity of the environment, and improve the quality of service in an energy-efficient manner. This paper presents an overview of ML, CR, VANET, and CR-VANET, including their architectures, functions, challenges, and open issues. The applications and roles of ML methods in CR-VANET scenarios are reviewed. Insights into the use of ML for autonomous or driverless vehicles are also presented. Current advancements in the amalgamation of these prominent technologies and future research directions are discussed. INDEX TERMS Machine learning, VANET, cognitive radio, autonomous vehicles, smart transportation system. performance of CR-VANET [15]. Security enhancement is one of the major issues in CR-VANET. Here, a vehicle can pretend to be a PU and propagate false information to obtain spectrum access selfishly. ML can be used to detect such actions and enhance security [16], [17]. ML also provides an optimum route to CR-VANET users to avoid traffic jams and road accidents. ML can also play a vital role in the best infotainment experience in CR-VANET. It can be used for appropriate scheduling, selecting the best channel, and prioritizing messages. CR and ML can play a major role in the next-generation driverless car system. The role of CR in the next-generation transportation system has been presented in previous discussions. This survey shows how ML can be applied to reduce road accidents and traffic congestion. CR can be used to accommodate the spectrum required to support massive data communication among automated driverless vehicles and networks. ML can be an integral part of this driverless or automated vehicle system. Similar to a robot, an autonomous vehicle (AV) can learn the surrounding environment and communicate with increased safety, reliability, QoS, and energy efficiency by applying such learning. This paper presents the dynamic usages of ML in CR-VANET elaborately. Several of the benefits of CR in VANETs and ML in VANETs and CR-VANETs are presented in Table 1. B. CONTRIBUTIONS OF THIS SURVEY ARTICLE Many survey articles describe CR, VANET, ML, and CR-VANETs individually or describe a few aspects of their amalgamation. To the best of our knowledge, surveys that cover the usage of ML in CR-VANET...

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Artificial Intelligence for Vehicle-to-Everything: A Survey

Cited by 212 publications

References 104 publications

Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics

Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics

Technology Development of Electric Vehicles: A Review

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks

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