TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles

B, Nikhil Kamath; Fernandes, Roshan; Rodrigues, Anisha P; Mahmud, Mufti; Vijaya, P.; Gadekallu, Thippa Reddy; Kaiser, M. Shamim

doi:10.3390/s23020653

Cited by 7 publications

(2 citation statements)

References 33 publications

(40 reference statements)

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“…In our earlier study [10], which was based on the Cabspotting project, we documented the instantaneous speed and directions, frequency of pickup and dropoff, distribution of hotspots, driver's empty cruise time, and other statistical elements of taxi mobility as well as trip patterns. Author [11] proposed a parsimonious mobility method known as Heterogeneous Random Walk (HRW) based on Cabspotting data that was accumulated over the course of a month and captures some of significant mobility characteristics that have been observed at macroscopic level.…”

Section: A) Navigation Based Vehicular Network Existing Techniquesmentioning

confidence: 99%

Deep learning algorithms and mechanisms in navigation for vehicular crowd managementsystems in real-time for smart transportation

Kaswan

Balu

Ojha

et al. 2023

Preprint

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Accurate predictions of vehicle mobility and density are necessary for a wide range of mobile applications, including VANETs, crowdsourcing, participatory sensing, network provisioning, and shared transportation. The difficulty of forecasting is exacerbated by the scarcity and scale of vehicular mobility data. Crowd management and navigation analysis of vehicular networks that make use of deep learning techniques are the focus of this study. Multihop path based edge computing is used to analyze vehicular network navigation, and a markov spatio reinforcement neural network is used to manage vehicular crowds. The number of vehicles in the network and its navigation analysis are the basis for the experimental analysis. Throughput, data transmission rate, latency, network traffic analysis, and scalability are the parameters analyzed.proposed technique attained data transmission rate of 94%, latency of 62%, throughput of 61%, network traffic analysis of 59%, scalability of 63%.

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Section: A) Navigation Based Vehicular Network Existing Techniquesmentioning

confidence: 99%

Deep learning algorithms and mechanisms in navigation for vehicular crowd managementsystems in real-time for smart transportation

Kaswan

Balu

Ojha

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Therefore, initiatives towards smart traffic management, are essential to improve the city's infrastructure. This includes dynamic traffic signals, finding appropriate parking slots, network-connected traffic signals, and other related components [142]- [144].…”

Section: ) Traffic Managementmentioning

confidence: 99%

A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Ramalingam,

Selvi,

Victor

et al. 2023

IEEE Access

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Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors. BC can offer decentralized and secure data storage, while CV allows machines to learn and understand visual data. The integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed.

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Deep learning algorithms and mechanisms in navigation for vehicular crowd management systems in real time for smart transportation

et al. 2023

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TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles

Cited by 7 publications

References 33 publications

Deep learning algorithms and mechanisms in navigation for vehicular crowd managementsystems in real-time for smart transportation

Deep learning algorithms and mechanisms in navigation for vehicular crowd managementsystems in real-time for smart transportation

A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Deep learning algorithms and mechanisms in navigation for vehicular crowd management systems in real time for smart transportation

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