Risk and Trust in artificial intelligence technologies: A case study of Autonomous Vehicles

2020 ◽  
Author(s):  
Ighoyota Ben. Ajenaghughrure ◽  
Sonia Claudia da Costa Sousa ◽  
David Lamas
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles

scholarly journals Employing waterborne autonomous vehicles for museum visits: a case study in Amsterdam

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Helena Hang Rong ◽  
Wei Tu ◽  
Fábio Duarte ◽  
Carlo Ratti
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Central Area ◽  
Urban Tourism ◽  
Innovative Approach ◽  
Multi Objective ◽  
Travel Experience ◽  
Points Of Interest ◽  
Objective Model

AbstractAmsterdam is a culturally rich city attracting millions of tourists. Popular activities in Amsterdam consist of museum visits and boat tours. By strategically combining them, this paper presents an innovative approach using waterborne autonomous vehicles (WAVs) to improve the museum visitation in Amsterdam. Multi-source urban data including I Amsterdam card data and Instagram hashtags are used to reveal museum characteristics such as offline and online popularity of museums and visitation patterns. A multi-objective model is proposed to optimize WAV routes by considering museum characteristics and travel experiences. An experiment in the Amsterdam Central area was conducted to evaluate the viability of employing WAVs. By comparing WAVs with land transportation, the results demonstrate that WAVs can enhance travel experience to cultural destinations. The presented innovative WAVs can be extended to a larger variety of points of interest in cities. These findings provide useful insights on embracing artificial intelligence in urban tourism.

scholarly journals Application of Artificial Intelligence in Tesla- A Case Study

2021 ◽  
pp. 205-218
Author(s):  
Divya Kumari ◽  
Subrahmanya Bhat
Keyword(s):  
Artificial Intelligence ◽  
Automobile Industry ◽  
Financial Services ◽  
Autonomous Vehicles ◽  
Oil And Gas ◽  
Route Planning ◽  
Case Study Report ◽  
Deep Layers ◽  
Car Park

Background/Purpose: Artificial intelligence algorithms are like humans, performing a task repeatedly, each time changing it slightly to maximize the result. A neural network is made up of several deep layers that allow for learning. Financial services, ICT, life science, oil and gas, retail, automotive, industrial healthcare, and chemicals and manufacturing sectors are among the industries that employ these algorithms. The electric motor is a new concept, and the automobile industry is now undergoing intensive research to determine whether it is practicable and financially viable. There are already some first movers, such as Tesla, who have successfully established their model and are moving forward. Tesla is forcing the auto industry to adapt quickly. Tesla introduced Autopilot driver capability for its Model S vehicle. Tesla Autopilot is a suite of sophisticated driver-assist technologies that include traffic adjustment, congested roads navigation system, autopilot car-parks, computer-controlled road rules, semi-autonomous route planning on major roadways, and the ability to summon the vehicle out of a designated car-park. This article provides a comprehensive analysis of Tesla Company and Innovations of Autopilot Vehicles. Objective: This case study report addresses the growth of Tesla Company in the field of Autonomous Vehicles. Design/Methodology/Approach: The knowledge for this case study of Tesla was gathered from various academic articles, online articles, and the SWOT framework. Findings/Result: Based on the research, this paper discusses the technological histories, Autopilot driving features, safety concerns, financial plans, market challenges, different models, and how Tesla Inc. is accelerating the world's movement in multiple initiatives such as the contribution of the global economic system, study in the Artificial Intelligence and Machine Learning area. Originality/Value: This paper study provides a brief overview of Tesla Inc. given the various data collected, and information about Tesla Autopilot vehicles using Artificial Intelligence based Innovations in Entrepreneurial Oriented Cars. Paper type: A Research Case study paper - focuses on Application of Artificial Intelligence in Tesla Autopilot Vehicles and growth & Journey of the Tesla Inc. Company.

scholarly journals A Framework for Component Selection Considering Dark Sides of Artificial Intelligence: A Case Study on Autonomous Vehicle

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 384
Author(s):  
Mohammad Reza Jabbarpour ◽  
Ali Mohammad Saghiri ◽  
Mehdi Sookhak
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Intelligent Systems ◽  
Selection Process ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Component Selection ◽  
Wide Range ◽  
Selection Framework

Nowadays, intelligent systems play an important role in a wide range of applications, including financial ones, smart cities, healthcare, and transportation. Most of the intelligent systems are composed of prefabricated components. Inappropriate composition of components may lead to unsafe, power-consuming, and vulnerable intelligent systems. Although artificial intelligence-based systems can provide various advantages for humanity, they have several dark sides that can affect our lives. Some terms, such as security, trust, privacy, safety, and fairness, relate to the dark sides of artificial intelligence, which may be inherent to the intelligent systems. Existing solutions either focus on solving a specific problem or consider the some other challenge without addressing the fundamental issues of artificial intelligence. In other words, there is no general framework to conduct a component selection process while considering the dark sides in the literature. Hence, in this paper, we proposed a new framework for the component selection of intelligent systems while considering the dark sides of artificial intelligence. This framework consists of four phases, namely, component analyzing, extracting criteria and weighting, formulating the problem as multiple knapsacks, and finding components. To the best of our knowledge, this is the first component selection framework to deal with the dark sides of artificial intelligence. We also developed a case study for the component selection issue in autonomous vehicles to demonstrate the application of the proposed framework. Six components along with four criteria (i.e., energy consumption, security, privacy, and complexity) were analyzed and weighted by experts via analytic hierarchy process (AHP) method. The results clearly show that the appropriate composition of components was selected through the proposed framework for the desired functions.

Autonomous Vehicles in the Mobility System

2020 ◽  
pp. 500-514
Author(s):  
Thilo von Pape
Keyword(s):  
Artificial Intelligence ◽  
Mobile Phones ◽  
Mobile Communication ◽  
Autonomous Vehicles ◽  
Communication Research ◽  
Collective Control ◽  
System P ◽  
Mobility System ◽  
And Control ◽  
Transportation Technologies

This chapter discusses how autonomous vehicles (AVs) may interact with our evolving mobility system and what they mean for mobile communication research. It juxtaposes a conceptualization of AVs as manifestations of automation and artificial intelligence with an analysis of our mobility system as a historically grown hybrid of communication and transportation technologies. Since the emergence of railroad and telegraph, this system has evolved on two layers: an underlying infrastructure to power and coordinate the movements of objects, people, and ideas in industrially scaled speeds, volumes, and complexity and an interface to seamlessly access this infrastructure and control it. AVs are poised to further enhance the seamlessness which mobile phones and cars already lent to mobility. But in assuming increasingly sophisticated control tasks, AVs also disrupt an established shift toward individual control, demanding new interfaces to enable higher levels of individual and collective control over the mobility infrastructure.

scholarly journals A Programming Approach to Collective Autonomy

2021 ◽  
Vol 10 (2) ◽  
pp. 27
Author(s):  
Roberto Casadei ◽  
Gianluca Aguzzi ◽  
Mirko Viroli
Keyword(s):  
Autonomous Vehicles ◽  
Autonomic Computing ◽  
Autonomous Agents ◽  
Programming Approach ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Simulated Case ◽  
Complex Settings

Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.

scholarly journals UNDERSTANDING SPECTRUM BIAS IN ALGORITHMS DERIVED BY ARTIFICIAL INTELLIGENCE A CASE STUDY IN DETECTING AORTIC STENOSIS USING ELECTROCARDIOGRAMS

2021 ◽  
Vol 77 (18) ◽  
pp. 3241
Author(s):  
Andrew S. Tseng ◽  
Michal Shelly-Cohen ◽  
Zachi Itzhak Attia ◽  
Peter Noseworthy ◽  
Paul Friedman ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Aortic Stenosis
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