Safety and Driver Assistance in VANETs: An Experimental Approach for V2V

Evangeline, C. Suganthi; Kumaravelu, Vinoth Babu; Joshi, Atul

doi:10.1109/icces45898.2019.9002562

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…In this case, the disaster management team can create a simple ad-hoc network, like a VANET, for devices to communicate [55]. In addition, VANETs have been implemented to monitor traffic light behavior in the interest of curbing the risk on roads since road accidents are growing in number nowadays [56]. VANETs are characterized by:…”

Section: Vanet/v2x Overviewmentioning

confidence: 99%

“…1) Vehicle-to-vehicle communications: When vehicles interact with each other, this is known as V2V communication. To decrease the number of car accidents, vehicles are designed to communicate with each other using V2V communications to share information about various life-saving road conditions [56]. 2) Vehicle-to-infrastructure communications: When communication occurs between a vehicle and a fixed RSU, this is known as V2I communication [12].…”

Section: A Types Of Communicationsmentioning

confidence: 99%

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A Survey of VANET/V2X Routing From the Perspective of Non-Learning- and Learning-Based Approaches

Chatterjee¹,

Karmakar

Kaddoum

et al. 2022

IEEE Access

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Intelligent transportation systems (ITSs) have become increasingly popular because they support effective coordination in connected vehicles. ITSs present an integrated approach for exchanging relevant information in order to improve the safety, efficiency, and reliability of road transportation systems. A variant of mobile ad-hoc networks (MANETs) called vehicular ad-hoc networks (VANETs) are an integral component of ITSs. VANETs consist of interconnected vehicles with sensing abilities that exchange information related to traffic, positioning, weather, and emergency services. In general, vehicle-to-everything (V2X) refers to communications between any entity and a vehicle, where the entity may be a vehicle, a cloud-based network, a pedestrian, or equipment installed along a road. One of the crucial challenges in V2X is the reliable and timely circulation of information among vehicular nodes to allow drivers to make decisions that increase road safety. In this context, efficient V2X routing protocols play a key role in supporting reliability and safety, and enhancing the overall quality of service (QoS) in VANETs. However, VANETs have distinct characteristics, such as high vehicular node mobility, unsteady connectivity, rapid changes in network topology, and unbounded network size, that can significantly affect routing in the network. Various routing protocols for V2X communication exist in the open technical literature. In this survey, we categorize the routing mechanisms as non-learning-and learning-based approaches, and discuss existing V2X routing protocols and their contributions to and impacts on VANET performance. Here, the learning-based approach implies the use of machine learning algorithms. This survey also summarizes open challenges in designing effective V2X routing protocols and future research directions to consider when developing smart routing mechanisms for next-generation intelligent VANET technologies.INDEX TERMS VANET, V2X, routing protocol, non-learning-based routing, learning-based routing.

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Section: Vanet/v2x Overviewmentioning

confidence: 99%

Section: A Types Of Communicationsmentioning

confidence: 99%

A Survey of VANET/V2X Routing From the Perspective of Non-Learning- and Learning-Based Approaches

Chatterjee¹,

Karmakar

Kaddoum

et al. 2022

IEEE Access

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“…The vehicular network characteristics, such as very dynamic topology and mobility can impose heavy constraints in the FVN training process, such as the need for a long enough link duration with attachment points (i.e., infrastructure and other vehicles) limits user participation in the FL process [7], [8]. The growing complexity and number of parameters of the Machine Learning (ML) models to be transmitted over the network causes the number of participating vehicular users to decrease [9].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Aggregation Approach for Vehicular Federated Learning

Pacheco,

Braun,

Rosário

et al. 2024

IEEE Access

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Federated Learning (FL) has rapidly become a crucial paradigm for training Machine Learning (ML) models when datasets are spread across several devices without compromising the privacy of the data owners. In vehicular networks, FL can be used to train driving models and object detection and classification over sensitive datasets to continuously improve user experience and driving safety. However, the majority of FL implementations cannot efficiently filter malicious vehicular users and low-quality contributions. This article proposes Distributed OT-based Federated Learning (DOTFL), an aggregation mechanism based on the clustering of the received trained Neural Networks Neural Network (NN) at the vehicular devices and on outlier detection. The proposed mechanism can detect malicious contributions by comparing them to previously received contributions and following a clustering approach. Furthermore, the convergence time of the FL process is improved by distributing trained NN weights directly through vehicle-to-vehicle links. Experimental analysis shows an improvement of up to 22% in terms of accuracy compared to state-of-the-art FL approaches. This is achieved by using clustering models and removing outliers, enabling a significantly lower presence of malicious contributions in aggregated models.

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“…Recent studies have analyzed the impact of warning mechanisms on road accident prevention and presented sound evidence as to the importance of these systems in accomplishing the WHO's goal of reducing deaths and injuries in road accidents to 50% by 2030 [2]- [4]. Connected vehicle technologies are seen as a promising solution to prevent and mitigate road accidents by means of emergency message dissemination and information sharing through inter-vehicle communication [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Systematic Literature Review of AI/ML Techniques Applied to VANET Routing

Teixeira

Ferreira

Macedo

2022

Lecture Notes in Networks and Systems

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Vehicular ad hoc networks (VANETs) have emerged as a powerful network model for providing road safety, as well as infotainment applications, for vehicle passengers and drivers alike. These networks are characterized by high node mobility, which in turn introduces communication intermittency and unreliability, deteriorating the network's performance. To provide solutions to the concerns of performance of such networks, articles have proposed intelligent approaches to deal with such issues by means of artificial intelligence and machine learning techniques. This study aimed to review the literature regarding intelligent routing protocols in VANETs by focusing on the techniques used and on how the solutions were evaluated based on performance. A systematic literature review of studies was conducted by adopting the snowballing procedure to collect studies that propose novel approaches to solving the VANET routing protocol by means of intelligent algorithms. The 86 included studies reported that heuristics, fuzzy logic systems and reinforcement learning approaches are the most popular and effective methods to improve network performance in the dynamic VANET environment. The findings have also shown that the literature has yet to find a consensus as to how to evaluate routing protocol performance. An evaluation and comparison framework is required as to enable transparent routing protocol design and selection in future vehicle applications.

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Safety and Driver Assistance in VANETs: An Experimental Approach for V2V

Cited by 9 publications

References 7 publications

A Survey of VANET/V2X Routing From the Perspective of Non-Learning- and Learning-Based Approaches

A Survey of VANET/V2X Routing From the Perspective of Non-Learning- and Learning-Based Approaches

A Distributed Aggregation Approach for Vehicular Federated Learning

Systematic Literature Review of AI/ML Techniques Applied to VANET Routing

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