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 Will the Thai ABC Car Owners, Buy a Smart ABC Car? a Case Study in Bangkok Metropolis

2018 ◽  
Vol 7 (3.30) ◽  
pp. 202
Author(s):  
Keerati Sittichainarong ◽  
Aaron Loh ◽  
Preecha Methavasaraphak ◽  
John Barnes
Keyword(s):  
Automobile Industry ◽  
Autonomous Vehicles ◽  
New Technology ◽  
Influential Factors ◽  
Self Identity ◽  
Factors Affecting ◽  
Intent To Purchase ◽  
Intention To Purchase ◽  
Near Future

Thailand is the biggest manufacturer of trucks and cars outside of Japan and China in Asia. Many had reported that "smart" technology especially that which leads towards driverless or autonomous vehicles will be the most important single development that will affect the automobile industry both domestically and globally.  Hence this research is therefore on the readiness of Thai car owners to adopt the new technology and the intention to purchase a smart car in the near future. Specifically, it is a case study on the influential factors affecting the intent to purchase a smart car by owners of a top Japanese brand in Bangkok. A questionnaire survey was conducted on 385 existing car owners of the Japanese brand under consideration in metropolitan areas of Bangkok and the data returned analyzed by multiple linear regression. The outcome of the research pointed towards ‘Self-identity” and ‘Emotional connection’ as the most influential factors towards the intent to purchase a smart car.  

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 Autonomous Recognition Model

2021 ◽  
Vol 10 (6) ◽  
pp. 67-73
Author(s):  
Akash gupta ◽  
Rahat Ali ◽  
Abhay Pratap Singh ◽  
P.Raja Kumar
Keyword(s):  
Artificial Intelligence ◽  
Automobile Industry ◽  
Autonomous Vehicles ◽  
Future Generation ◽  
Autonomous Driving ◽  
Traffic Signs ◽  
Use Of Technology ◽  
Human Driver ◽  
New Research ◽  
Self Driving Cars

Nowdays we are witnessing the technology transforming everything the way we used to do things and how the automobile industry is transforming itself with the use of technology IOT,Artificial intelligence,Machine learning.Companies shifting its products and its utilities in diferent way and they now want to acquire and introduce level-5 autonomous to future generation and big automobile companies are trying to achieve autonomous vechicles and we have researhed about the model that will help in assisting autonomous vechicles and trying to achieve that.We will develop this model with help of technologies like Artificial intelligence,Machine learning,Deep learning.Autonomous vehcicles will become a reality on our roads in the near future. However, the absence of a human driver requires technical solutions for a range of issues, and these are still being developed and optimised. It is a great contribution for the automotive industry which is going towards innovation and economic growth. If we talking about some past decade the momentum of new research and the world is now at the very advanced stage of technological revolution. “Autonomous-driving” vehicles. The term Self-driving cars, autonomous car, or the driverless cars have different name with common objective. The main focus is to keep the human being out of the vehicle control loop and to relieve them from the task of driving. Everyday automotive technology researchers solve challenges. In the future, without human assistance, robots will produce autonomous vehicles using IoT technology based on customer needs and prefer that these vehicles are more secure and comfortable in mobility systems such as the movement of people or goods. We will build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles .This model we have tested it and resulted in 95% accuracy.

Improving Offshore Platform Production With Artificial Intelligence

2021 ◽  
Author(s):  
Philippe Herve
Keyword(s):  
Artificial Intelligence ◽  
Carbon Emissions ◽  
Oil And Gas ◽  
New Technologies ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Carbon Neutrality ◽  
Oil And Gas Companies ◽  
Oil And Gas Sector

Abstract The oil and gas sector is facing a changing market with new pressures to which it must learn to adapt. One of the biggest changes in expectations is the increased focus being placed on carbon emissions. Many consumers, investors, and lawmakers see reforms to the oil and gas industry as one of the most important avenues toward reducing carbon emissions and curbing climate change, and accordingly, a large number of companies have already made ambitious pledges towards carbon neutrality. New technologies may offer the best avenue for oil and gas companies to reduce their carbon emissions and meet those neutrality goals. Digital technologies—and in particular, artificial intelligence—can aid in decarbonization even with relatively small investments, primarily by enabling large increases in efficiency and reducing unscheduled downtime and the need for flaring. This paper discusses how artificial intelligence-powered predictive maintenance can be applied to reduce carbon emissions, and a case study illustrating a real-world deployment of this technology.

scholarly journals ON THE PATH TO ARTIFICIAL INTELLIGENCE: THE EFFECTS OF A ROBOTICS SOLUTION IN A FINANCIAL SERVICES FIRM

2021 ◽  
Vol 32 (2) ◽  
Author(s):  
Hamieda Parker ◽  
Stephanie Elaine Appel
Keyword(s):  
Artificial Intelligence ◽  
Customer Service ◽  
Financial Services ◽  
Data Entry ◽  
Research Approach ◽  
Work Experiences ◽  
Action Research Approach ◽  
Before And After ◽  
The Impact

As ever-increasing advances in automation and artificial intelligence solutions create more opportunities for businesses to streamline their operations, the key challenges for managers are to identify the appropriate use cases for automation solutions in their organisations and to integrate the solution effectively to meet the objectives of both the firm and its employees. This case study examines the impact of implementing a machine-learning robotic process automation (RPA) solution that is aimed at reducing manual data entry tasks for employees in a financial services firm. The study employed an action research approach to follow a single team in the firm before and after the RPA implementation — a period of six months. The findings showed that RPA improved productivity in the team and created more positive work experiences for employees, as they had more time to dedicate to creative, cognitive, and customer service tasks. The study also found that the roles of employees were being redefined during the integration process, with employees reporting a high potential for broader transformation in the business as a result of the RPA implementation.

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.

Varied Responses to Human-Induced Seismicity in the City of Azle, Texas

2019 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Sarmistha R. Majumdar
Keyword(s):  
State Government ◽  
Induced Seismicity ◽  
Oil And Gas ◽  
The United States ◽  
The State ◽  
Prior History ◽  
Small Community ◽  
Regulatory Policies ◽  
The Status

Fracking has helped to usher in an era of energy abundance in the United States. This advanced drilling procedure has helped the nation to attain the status of the largest producer of crude oil and natural gas in the world, but some of its negative externalities, such as human-induced seismicity, can no longer be ignored. The occurrence of earthquakes in communities located at proximity to disposal wells with no prior history of seismicity has shocked residents and have caused damages to properties. It has evoked individuals’ resentment against the practice of injection of fracking’s wastewater under pressure into underground disposal wells. Though the oil and gas companies have denied the existence of a link between such a practice and earthquakes and the local and state governments have delayed their responses to the unforeseen seismic events, the issue has gained in prominence among researchers, affected community residents, and the media. This case study has offered a glimpse into the varied responses of stakeholders to human-induced seismicity in a small city in the state of Texas. It is evident from this case study that although individuals’ complaints and protests from a small community may not be successful in bringing about statewide changes in regulatory policies on disposal of fracking’s wastewater, they can add to the public pressure on the state government to do something to address the problem in a state that supports fracking.

Shale Resources, Parks Conservation, and Contested Public Lands in North Dakota’s Theodore Roosevelt National Park: Is Fracking Booming?

2020 ◽  
Vol 4 (1) ◽  
pp. 1-13
Author(s):  
Miriam R. Aczel ◽  
Karen E. Makuch
Keyword(s):  
National Park ◽  
Oil And Gas ◽  
The United States ◽  
Federal Agencies ◽  
Cultural Significance ◽  
Theodore Roosevelt ◽  
Natural Beauty ◽  
Theodore Roosevelt National Park ◽  
Bakken Shale

This case study analyzes the potential impacts of weakening the National Park Service’s (NPS) “9B Regulations” enacted in 1978, which established a federal regulatory framework governing hydrocarbon rights and extraction to protect natural resources within the parks. We focus on potential risks to national parklands resulting from Executive Orders 13771—Reducing Regulation and Controlling Regulatory Costs [1]—and 13783—Promoting Energy Independence and Economic Growth [2]—and subsequent recent revisions and further deregulation. To establish context, we briefly overview the history of the United States NPS and other relevant federal agencies’ roles and responsibilities in protecting federal lands that have been set aside due to their value as areas of natural beauty or historical or cultural significance [3]. We present a case study of Theodore Roosevelt National Park (TRNP) situated within the Bakken Shale Formation—a lucrative region of oil and gas deposits—to examine potential impacts if areas of TRNP, particularly areas designated as “wilderness,” are opened to resource extraction, or if the development in other areas of the Bakken near or adjacent to the park’s boundaries expands [4]. We have chosen TRNP because of its biodiversity and rich environmental resources and location in the hydrocarbon-rich Bakken Shale. We discuss where federal agencies’ responsibility for the protection of these lands for future generations and their responsibility for oversight of mineral and petroleum resources development by private contractors have the potential for conflict.

Rehabilitation treatment associating maxillary atresia and missing superior incisors – clinical case study report

2017 ◽  
Vol 10 (38) ◽  
pp. 78-83
Author(s):  
João Batista de Paiva ◽  
Daniele Sigal Linhares ◽  
José Rino ◽  
Lindalva Gutierrez
Keyword(s):  
Clinical Case ◽  
Rehabilitation Treatment ◽  
Clinical Case Study ◽  
Study Report ◽  
Case Study Report
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