The Google Car: Driving Toward A Better Future?

Poczter, Sharon; Jankovic, Luka M.

doi:10.19030/jbcs.v10i1.8324

Cited by 81 publications

(49 citation statements)

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“…Autonomous cars are becoming more frequent in nowadays scientific and industrial context, since Google launched their driverless car project in 2011 [4]. From then onwards, many other approaches proposed different architectures and solutions, all of them moving towards the development of the autonomous vehicle.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

Hussein

Marín-Plaza

García

et al. 2018

Journal of Advanced Transportation

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Self-driving cars are attracting significant attention during the last few years, which makes the technology advances jump fast and reach a point of having a number of automated vehicles on the roads. Therefore, the necessity of cooperative driving for these automated vehicles is exponentially increasing. One of the main issues in the cooperative driving world is the Multirobot Task Allocation (MRTA) problem. This paper addresses the MRTA problem, specifically for the problem of vehicles and requests allocation. The objective is to introduce a hybrid optimization-based approach to solve the problem of multiple intelligent vehicles requests allocation as an instance of MRTA problem, to find not only a feasible solution, but also an optimized one as per the objective function. Several test scenarios were implemented in order to evaluate the efficiency of the proposed approach. These scenarios are based on well-known benchmarks; thus a comparative study is conducted between the obtained results and the suboptimal results. The analysis of the experimental results shows that the proposed approach was successful in handling various scenarios, especially with the increasing number of vehicles and requests, which displays the proposed approach efficiency and performance.

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Section: Related Workmentioning

confidence: 99%

“…Researchers in the Intelligent Transportation Systems (ITS) field are investing more time on the self-driving cars research and development [4][5][6]. However, during the last decade, Multirobot Systems (MRS) fell under the research attention of the ITS community.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

Hussein

Marín-Plaza

García

et al. 2018

Journal of Advanced Transportation

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“…Among the detected objects are persons on foot, bicyclists and other another vehicles, but also flying shopping sack of plastic or rogue birds. The car is equipped with a software which analyzes all received data and process them for a safety navigation on the road [4], [5]. This example of machine learning application represents the quintessence of machine learning.…”

Section: Machine Learningmentioning

confidence: 99%

Machine Learning Techniques Used in Big Data

Nita¹

2016

SBNA

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“…These involve fierce competition between CSA that have opposing beliefs about civil liberty issues (Chapman and Ciment 2014). In addition, new large-scale socio-technical innovations-such as driverless electric vehicles-involve cross-sector competition, which can be rooted in opposing beliefs about the benefits and dangers from technology (Poczter and Jankovic 2014;Smit 2006;Wisniewski 2016). More broadly, some CSA work to win people over to large-scale changes that could be deeply unattractive to people, such as sharing with strangers and the remote monitoring of behaviour (Malhotra and Alstyne 2014).…”

Section: Introductionmentioning

confidence: 99%

Determinants in Competition between Cross-Sector Alliances

Fox

Kauttio

Mubarak

et al. 2017

Administrative Sciences

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Private, public, and not-for-profit organizations come together in cross-sector alliance projects and programmes (CSA) to bring about large-scale changes. CSA can often face determined competition from other alliances that oppose large-scale change or propose alterative large-scale changes. Competition can be related to people's deeply held beliefs arising from their ideologies, cultures, and/or other sources of entrenched preconceptions. In previous CSA research, there has been little consideration of competition between CSA involving people's deeply held beliefs. Accordingly, in this paper, a conceptual framework for better understanding CSA competition is introduced. This encompasses the influence of people's beliefs and related underlying determinants. This is necessary because there are many large-scale challenges that involve private, public, and not-for-profit organizations working together in projects and programmes against competition.

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The Google Car: Driving Toward A Better Future?

Abstract: Google's dramatic ascent and subsequent domination in the past fifteen years of the technology and information industries has financially enabled

Cited by 81 publications

References 1 publication

Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

Machine Learning Techniques Used in Big Data

Determinants in Competition between Cross-Sector Alliances

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