Big autonomous vehicular data classifications: Towards procuring intelligence in ITS

Daniel, Alfred; Subburathinam, Karthik; Paul, Anand; Rajkumar, N.; Rho, Seungmin

doi:10.1016/j.vehcom.2017.03.002

Cited by 27 publications

(13 citation statements)

References 9 publications

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“…Twenty‐first century emerges as a century of new technologies, big data, and omnipresent digital devices. To move self‐driving cars from vision to reality, auto manufacturers depend on enabling electronic technologies for sensing, sensor fusion, communications, high‐performance processing, and other functions . Especially, data that in the transportation context can be called spatial big data is expected to have a crucial impact on the future transportation systems with self‐driving cars and optimal route selection issues included.…”

Section: Spatial Big Data As the Future Of Road Transportmentioning

confidence: 99%

“…As for this moment (year 2017), not much work has been done in terms of stream analytics in transportation field in general. Instead of a few existing literature positions taking into account real‐time stream processing, , most of them focus only on batch analysis of historical data , . It seems rather surprising considering the fact that autonomous vehicles' development is progressing really fast.…”

Section: Batch and Stream Processing In Big Datamentioning

confidence: 99%

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Proposal of big data route selection methods for autonomous vehicles

Reddig

Dikunow

Krzykowska

2018

Internet Technology Letters

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The automotive industry is developing rapidly, mainly in the direction of automation. Automation means constructing self‐driving vehicles that will not need any human assistance. This would not only rise the comfort of travel but most of all it would allow to minimize congestions and reduce car accidents. In this paper, we propose a solution for self‐driving vehicles' development based on big data Kafka and Spark architectures that would enable to gather large quantities of diversified data in real‐time. This, in turn would allow the future cars to make fast, autonomous decisions concerning optimal route selection.

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Section: Spatial Big Data As the Future Of Road Transportmentioning

confidence: 99%

Section: Batch and Stream Processing In Big Datamentioning

confidence: 99%

Proposal of big data route selection methods for autonomous vehicles

Reddig

Dikunow

Krzykowska

2018

Internet Technology Letters

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“…Some of the complications connected with the implementation of ITS systems are discussed in [25] and [26]. The methodology of this paper can be implemented easily just with smartphones yet results are general and can be applied to any connected vehicle that can be the base of an FCD system.The problem of traffic signal synchronization was studied starting in 1967 by Newell and others [25][26][27][28][29][30]. The problem of adaptive synchronization of traffic lights was then explored in 1980 by Sims [31] for the city of Sydney, which suffered from severe congestion problems.…”

mentioning

confidence: 99%

“…All these concepts will be further developed with the introduction of connected vehicles. Some of the complications connected with the implementation of ITS systems are discussed in [25] and [26]. The methodology of this paper can be implemented easily just with smartphones yet results are general and can be applied to any connected vehicle that can be the base of an FCD system.…”

mentioning

confidence: 99%

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A Single Intersection Cooperative-Competitive Paradigm in Real Time Traffic Signal Settings Based on Floating Car Data

et al. 2019

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New technologies such as "connected" and "autonomous" vehicles are going to change the future of traffic signal control and management and possibly will introduce new traffic signal systems that will be based on floating car data (FCD). The use of floating car data to regulate traffic signal systems, in real time, has the potential for an increased sustainability of transportation in terms of energy efficiency, traffic safety and environmental issues. However, research has never explored how not "connected" vehicles would benefit by the implementation of such systems. This paper explores the use of floating car data to regulate traffic signal systems in real-time in a single intersection and in terms of cooperative-competitive paradigm between "connected" vehicles and conventional vehicles. In a dedicated laboratory, developed for testing regulation algorithms, results show that "invisible vehicles" for the system (which are not "connected") in most simulated cases also benefit when real time traffic signal settings based on floating car data are introduced. Moreover, the study estimates the energy and air quality impacts of a single intersection signal regulation by evaluating fuel consumption and pollutant emissions. Specifically, the study demonstrates that significant improvements in air quality are possible with the introduction of FCD regulated traffic signals.Energies 2019, 12, 409 2 of 22 completely eliminated, showing that congestion was reduced and safety increased [5,6]. Cassini [7,8] also presents a fascinating case where traffic signals were installed improperly causing residents to protest, without success, until the moment the lights went off for a technical failure and the traffic jams magically disappeared.While these experiences hint that regulating traffic signal systems properly is an important and actual problem, mobile internet coupled with Global Navigation Satellite System (GNSS) localization systems are creating disruptive innovations in the transportation sector. A real technical revolution has started, driven by the convergence of mobile internet, GNSS and the introduction of "connected" vehicles.Intelligent Transportation Systems (ITS) can take advantage in the use of Floating Car Data (FCD) for managing traffic flow or to extract traffic flow parameters and many large scale deployments of such systems based on Floating Car Data (FCD) are already showing the use of mobile phones [9][10][11][12] and wireless communications [13,14] combined with GNSS technologies.Smartphones (and connected vehicles) can obtain localization and speed information from GNSS systems such as Galileo, GPS and Glonass. GPS sensors embedded in smartphones provide an economical method to obtain vehicular travel times [15] and to evaluate traffic scenarios [16,17]. Smartphones also allow the estimation of traffic safety parameters [18] and route choices [19]. Mobile devices have also been used to asses safety and risks by insurance companies [20] and for traffic safety [21,22] and fuel consumption estimation [23]....

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Big Data for Autonomous Vehicles

Sharma

2021

Studies in Computational Intelligence

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Big autonomous vehicular data classifications: Towards procuring intelligence in ITS

Cited by 27 publications

References 9 publications

Proposal of big data route selection methods for autonomous vehicles

Proposal of big data route selection methods for autonomous vehicles

A Single Intersection Cooperative-Competitive Paradigm in Real Time Traffic Signal Settings Based on Floating Car Data

Big Data for Autonomous Vehicles

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