Grey Wolf Optimization in VANET to manage Platooning of Future Autonomous Electrical Vehicles

Rango, Floriano De; Tropea, Mauro; Raimondo, Pierfrancesco; Santamaria, Amilcare Francesco

doi:10.1109/ccnc46108.2020.9045632

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Here, (non-)linear programming (e.g., in [78], [66], [51]) is frequently applied to optimise an objective function. In addition, several approaches perform optimisation with bio-inspired algorithms such as genetic algorithms (e.g., in [74]), ant colony optimisation (e.g., in [86]), or grey wolf optimisation (e.g., in [87], [88]).…”

Section: Algorithmmentioning

confidence: 99%

An Overview on Approaches for Coordination of Platoons

Lesch

Breitbach

Segata

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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In the recent past, platooning evolved into an attractive cooperative driving technology, broadly discussed in research and practice. Vehicles in platoons use cooperative adaptive cruise control to drive at close distances to each other. Platooning (i) increases the capacity of the street by a factor of 2; (ii) reduces the fuel consumption and emissions by up to 20%; and (iii) has social implications as it increases driver comfort and safety. As platooning research progresses, platooning coordination becomes a major research focus. The coordination of platoons, including the assignment of vehicles to platoons, the management of inter-and intra-platoon interactions, and the coordination of interactions with other vehicles is an important step towards an effective usage of platooning in practice. Based on a literature review of 1,600 papers, this survey provides an overview of state of the art in platooning coordination research for both cars and trucks. In this paper, we present a novel taxonomy for platooning coordination and classify existing approaches. We use the results of the literature review to discuss challenges and outline avenues for future work such as multi-objectiveness and individualisation.

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Section: Algorithmmentioning

confidence: 99%

An Overview on Approaches for Coordination of Platoons

Lesch

Breitbach

Segata

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…Delta is the subordinate wolf's term for the From this concept, an appropriate form of cluster is extracted to optimize the mentioned problem. Finally, the alpha, beta, and gamma direct the hunting while their position ought to be by the omega wolves by three best-arrangement considerations [36]. (2) Honey Bee Algorithm (HBA).…”

Section: Performance Of Swarm Intelligence-based Bioinspiredmentioning

confidence: 99%

A Study to Enhance the Route Optimization Algorithm for the Internet of Vehicle

Dhanare

Nagwanshi

Varma³

2022

Wireless Communications and Mobile Computing

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In the Internet of Things (IoT), the advancement of the new modern era of Internet-off driving, known as the Internet of Vehicles (IoV), is aimed at the Intelligent Transportation System (ITS) to improve road safety and traffic. Therefore, IoV is better for traffic management. But perhaps, the main challenges in this type of network are that timely decision to make adequate decisions by a driver under certain conditions of rapid change in topology, high vehicular mobility, and frequent link failures is hard to improve road safety. Therefore, an optimized and congestion-free route is required to collect real-time data from vehicles. Thus, some of the latest bioinspired optimization routing algorithms in the IoV environment are presented in this study. Hence, monitoring speed limits, pollution checks, and emergency responses to traffic accidents should also be considered while performing vehicle routing to avoid traffic problems. In the previous decades, many route-directing protocols for the IoV environment have been proposed that can handle the requirements of reliability and security. But such routing of protocols suffers from high complexity and scalability limitations in big-scale networks, routing overlays, etc. Therefore, their strengths, weaknesses, and critical characteristics are compared using various criteria for such algorithms. Then, a suggestion is made to propose a prescribed combined model of a multimodular bioinspired approach to IoV routing. Finally, the main future directions of research in this sector are highlighted.

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“…ALMANNAA et al (2019) field studies showed that the proposed Eco-CACC could achieve a 31% fuel consumption reduction. DE RANGO et al (2020) performed simulations using MATLAB and Omnet++, showed that the platoons save energy and reduce fuel consumption. They used Grey Wolf Optimization to find the best platoon configuration, limiting to a maximum of five vehicles in each platoon.…”

Section: Deep Dive On Cacc/platooningmentioning

confidence: 99%

Assessment of connected and autonomous vehicles impacts on traffic flow through microsimulation.

Paterlini¹

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Grey Wolf Optimization in VANET to manage Platooning of Future Autonomous Electrical Vehicles

Cited by 6 publications

References 7 publications

An Overview on Approaches for Coordination of Platoons

An Overview on Approaches for Coordination of Platoons

A Study to Enhance the Route Optimization Algorithm for the Internet of Vehicle

Assessment of connected and autonomous vehicles impacts on traffic flow through microsimulation.

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