Connected and Autonomous Vehicles: Implications for Policy and Practice in City and Transportation Planning
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Innovation in Ecosystem Business Models: an Application to Maas and Autonomous Vehicles in Urban Mobility System
Innovation in ecosystem business models : An application to MaaS and Autonomous vehicles in urban mobility system Rodrigo Gandia To cite this version: Rodrigo Gandia. Innovation in ecosystem business models : An application to MaaS and Autonomous vehicles in urban mobility system. Economics and Finance. Université Paris-Saclay; University of Lavras, UFLA (Brésil), 2020. English. NNT : 2020UPASC018. tel-02895349 HAL Id: tel-02895349 https://tel.archives-ouvertes.fr/tel-02895349 Submitted on 9 Jul 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Innovation in Ecosystem Business Models: An Application to MaaS and Autonomous Vehicles in Urban Mobility System Thèse de doctorat de l'université Paris-Saclay École doctorale n° 573 Interfaces : approches interdisciplinaires, fon- dements, applications et innovation (Interfaces) Spécialité de doctorat : Ingénierie des systèmes complexes Unité de recherche : Université Paris-Saclay, CentraleSupélec, Laboratoire Genie Industriel, 91190, Gif-sur-Yvette, France. Référent : CentraleSupélec Thèse présentée et -
Autonomous Vehicle, Sensing and Communication Survey 1 Introduction
Autonomous Vehicle, Sensing and Communication Survey Edited and submitted by Shraga Shoval Prepared by Shlomi Hacohen and Oded Medina Department of Mechanical Engineering, Ariel University, Ariel, Israel. August 2020 Abstract Autonomous vehicles introduce dramatic changes to the way in which we travel. This technology has the potential to impact the transportation across a wide array of categories including safety, congestion, and travel behavior. In this report we review the main issues involved in autonomous driving as discussed in the literature and shed light on topics that we believe require further development. 1 Introduction The challenges in autonomous driving require a combination of many capabilities, among them: localization, motion planning, vehicle’s systems control (steering, acceleration/deceleration, signaling, etc.), road perception, prediction of other road-users behavior, awareness of dangers, etc. These capabilities have a various level of importance for various levels of driving automation. Even though the first autonomous vehicle (AV) was experimented in 1926 [104], a real-modern autonomous vehicle was first presented in 1986 by a team from Carnegie Mellon University [58]. Since 2010, many major automotive manufacturers, such as GM, Mercedes Benz, Ford, Toyota, and many more are developing AVs [90]. A demonstration of AV equipped with numerous sensors and communication systems was presented by Toyota in 2013 [10]. In 2016, Google’s AV has passed over one million kilometers. These events present a glimpse to the landmarks in the development of AV that, due to the complexity of the task, progresses slowly with considerable amount of attention to safety and reliability. AVs need a large amount of data for reliable decision making. -
Smart Transportation in China and the United States Yuming Ge, Xiaoman Liu, Libo Tang, and Darrell M
DECEMBER 2017 Smart transportation in China and the United States Yuming Ge, Xiaoman Liu, Libo Tang, and Darrell M. West INTRODUCTION It is no surprise that countries are suffering traffic problems as their rural and suburban populations move to cities and population density increases around urban areas. As cities grow in density, we have seen vehicular congestion, poor urban planning, and insufficient highway designs. The unfortunate downsides of these trends are the loss of lives due to accidents, time and money spent commuting, and lost productivity and economic growth for the system as a whole. All these developments are problematic because transportation accounts for six to 12 percent of Gross Domestic Product in many developed countries.1 The cost of traffic congestion alone is estimated to be greater than $200 billion in four Western countries: France, Germany, the United Kingdom, and the United States.2 But the good news is that recent advancements in computing, network speed, and communications sensors make it possible to improve transportation infrastructure, traffic management, and vehicular operations. Through better infrastructure, ubiquitous connectivity, 5G (fifth generation) networks, Yuming Ge, Xiaoman Liu, and dynamic traffic signals, remote sensors, vehicle-sharing services, and autonomous vehicles, it is Libo Tang are researchers at the possible to increase automotive safety, efficiency, and operations. Connected vehicles are moving China Academy beyond crash notification and lane changing guidance to offer a variety of services related to main- of Information and Communication tenance, operations, and entertainment. 5G networks will enable a shift in cellular technology from Technology (CAICT). supporting fixed services to communication and data exchange that is machine-based. -
Using Fuzzy Logic in Automated Vehicle Control
Intelligent Transportation Systems Using Fuzzy Logic in Automated Vehicle Control José E. Naranjo, Carlos González, Ricardo García, and Teresa de Pedro, Instituto de Automática Industrial Miguel A. Sotelo, Universidad de Alcalá de Henares ntil recently, in-vehicle computing has been largely relegated to auxiliary U tasks such as regulating cabin temperature, opening doors, and monitoring Automated versions fuel, oil, and battery-charge levels. Now, however, computers are increasingly assum- of a mass-produced ing driving-related tasks in some commercial models. Among those tasks are vehicle use fuzzy logic • maintaining a reference velocity or keeping a safe nomous robots and fuzzy control systems and the distance from other vehicles, Universidad de Alcalá de Henares’s knowledge of techniques to both • improving night vision with infrared cameras, and artificial vision. (The “Intelligent-Vehicle Systems” • building maps and providing alternative routes. sidebar discusses other such projects.) We’re devel- address common oping a testbed infrastructure for vehicle driving that Still, many traffic situations remain complex and includes control-system experimentation, strategies, challenges and difficult to manage, particularly in urban settings. and sensors. All of our facilities and instruments are The driving task belongs to a class of problems that available for collaboration with other research groups incorporate human depend on underlying systems for logical reasoning in this field. So far, we’ve automated and instru- and dealing with uncertainty. So, to move vehicle mented two Citroën Berlingo mass-produced vans procedural knowledge computers beyond monitoring and into tasks related to carry out our objectives. to environment perception or driving, we must inte- into the vehicle control grate aspects of human intelligence and behaviors Automated-vehicle equipment so that vehicles can manage driving actuators in a Figure 1 shows two mass-produced electric Cit- algorithms. -
The Study on Innovation, Development and Implementation
ISSN (Online) 2581-9429 IJAR SCT ISSN (Print) 2581-XXXX International Journal of Advanced Research in Science, Communication and Technology (IJARSCT) Volume 11, Issue 1, November 2020 Impact Factor: 4.819 The Study on Innovation, Development and Implementation of the Self-driving Car Aniruddha Chaki Student, Department of Electrical Engineering Siliguri Institute of Technology, Siliguri, India Abstract: The development of the self-driving car is one of the greatest inventions of the century. With the technological advancement, the implementation of the autonomous vehicle has become the major attention among the researchers. This paper discusses the brief history of self-driving cars, its research and development, key technologies behind the self-driving, main aspects of implementing self-driving cars. The paper also describes some challenges with possible solutions to the fully autonomous vehicle in mass implementation. Keywords: Advanced Driver Assistance System (ADAS), autonomous, navigation, positioning, self- driving technology, radar, lidar, path planning, obstacle avoidance, Full Self driving (FSD), accidents, protests, advantages, challenges. I. INTRODUCTION In this 21st-century progress in technology has made man as omnipotent, omniscient and omnipresent as a god. It is being tried to find out and accomplish any work with higher efficiency, lesser cost, and least possible effort. This makes researchers explore the domain of Automation, Machine learning, and Artificial Intelligence to handle the hardest of the jobs which were once tedious and cumbersome for humans [1]. We are driving towards the future where humans will only be involved in high mental ability skills and all the other works will be automated [2]. In this paper, the previous researches in the field of self-driving have been studied. -
Morgan Stanley NDR
AMBARELLA.COM AMBARELLA.COM December 6th & 9th, 2019 COPYRIGHT COPYRIGHT AMBARELLA 2019 Morgan Stanley NDR Frankfurt, Germany and Milan, Italy Dr. Alberto Broggi, GM Ambarella Italy Casey Eichler, CFO Louis Gerhardy, Corporate Development 1 Forward-Looking Statements This presentation contains forward-looking statements that are subject to many risks and uncertainties. All statements made in this presentation other than statements of historical facts are forward-looking statements, including, without limitation, statements regarding Ambarella’s strategy, future operations, financial targets, future revenues, projected costs, prospects, plans and objectives for future operations, future product introductions, future rate of our revenue growth, the size of markets addressed by the company's solutions and the growth rate of those markets, technology trends, our ability to address market and customer demands and to timely develop new or enhanced solutions to meet those demands, our ability to achieve design wins, and our ability to retain and expand our customer and partner relationships. In some cases, you can identify forward-looking statements by terms such as "may," "will," "should," "could," "would," "expects," "plans," "anticipates," "believes," "estimates," "projects," "predicts," "potential," or the negative of those terms, and similar expressions and comparable terminology intended to identify forward-looking statements. We have based forward-looking statements largely on our estimates of our financial results and our current expectations -
Study on Electrical Vehicle and Its Scope in Future
© 2018 JETIR February 2018, Volume 5, Issue 2 www.jetir.org (ISSN-2349-5162) Study on Electrical Vehicle and Its Scope in Future Divya Sharma, Department of Electrical Engineering Galgotias University, Yamuna Expressway Greater Noida, Uttar Pradesh ABSTRACT: The cars are based on programming in advanced time to give the individual driver relaxed driving. In the automobile industry, various opportunities are known, which makes a car automatic. Google, the largest network, has been working on self-driving cars since 2010 and yet an innovative adaptation to present a mechanised vehicle in a radically new model is continuing to be launched. The modelling, calibration and study of changes in city morphology in autonomous vehicles (AVs). Transport system are utilized to travel among the tangential houses as well as the intermediate work and necessitate transportation space. The main benefits of an autonomous vehicle areit can be operated day parking area for other uses, mitigatemetropolitanground. We also reduce the cost per kilometre of driving. Researchers are interested in the area of automotive automation, where most applications are found in different places. The technology in this research paper will help to understand the quick, present and future technologies used or used in the automotive field to render automotive automation. KEYWORDS: Autonomous Vehicle, Linriccan Wonder, Robotic Van, Self-Driving Car, Tesla corporation. INTRODUCTION Users all over the world are delighted with the introduction of an automated vehicle for the general -
2016 WP Transformation of the Workplace Through Robotics, AI And
The Transformation of the Workplace Through Robotics, Artificial Intelligence, and Automation: Employment and Labor Law Issues, Solutions, and the Legislative and Regulatory Response January 2016 AUTHORS Garry Mathiason Michael Lotito Robert Wolff Natalie Pierce Kerry Notestine Greg Brown John Cerilli Eugene Ryu Joon Hwang Phil Gordon Ilyse Schuman Miranda Mossavar Paul Kennedy Paul Weiner Sarah Ross Theodora Lee William Hays Weissman Jeff Seidle IMPORTANT NOTICE This publication is not a do-it-yourself guide to resolving employment disputes or handling employment litigation. Nonetheless, employers involved in ongoing disputes and litigation will find the information extremely useful in understanding the issues raised and their legal context. This report is not a substitute for experienced legal counsel and does not provide legal advice or attempt to address the numerous factual issues that inevitably arise in any employment-related dispute. Copyright ©2016 Littler Mendelson, P.C. All material contained within this publication is protected by copyright law and may not be reproduced without the express written consent of Littler Mendelson. Table of Contents SECTION / TOPIC PAGE I. INTRODUCTION 1 A. Legal Challenges Arising from Robotics, Artificial Intelligence 1 and Automation B. A Note on the Important Concepts Addressed in This Report 2 II. ROBOTICS, AUTOMATION, AND THE NEW WORKPLACE LANDSCAPE (CATEGORY ONE) 3 A. Job Dislocation and Job Creation 4 1. WARN Act 4 2. Severence 4 3. Retraining 5 B. Labor Unions and Collective Bargaining 5 1. Protected Concerted Activity 5 2. Collective Bargaining 6 C. Tax Implications 7 D. Anti-Discrimination 8 1. Age Discrimination in Employment Act of 1967 (ADEA) 8 2. -
Forecasting Americans' Long-Term Adoption of Connected And
FORECASTING AMERICANS’ LONG-TERM ADOPTION OF CONNECTED AND AUTONOMOUS VEHICLE TECHNOLOGIES 1 Prateek Bansal 2 Graduate Research Assistant 3 Department of Civil, Architectural and Environmental Engineering 4 The University of Texas at Austin 5 [email protected] 6 Phone: 512-293-1802 7 Kara M. Kockelman 8 (Corresponding Author) 9 E.P. Schoch Professor in Engineering 10 Department of Civil, Architectural and Environmental Engineering 11 The University of Texas at Austin 12 [email protected] 13 14 15 The following paper is a pre-print, the final publication can be found in 16 Transportation Research Part A: Policy and Practice, 95:49-63 (2017). 17 18 19 ABSTRACT 20 Automobile enterprises, researchers, and policymakers are interested in knowing the future of 21 connected and autonomous vehicles (CAVs). To this end, this study proposes a new simulation- 22 based framework to forecast Americans’ long-term (year 2015 to 2045) adoption levels of CAV 23 technologies under eight different scenarios based on: 5% and 10% annual drops in technology 24 prices; 0%, 5%, and 10% annual increments in Americans’ willingness to pay (WTP); and 25 changes in government regulations. This simulation was calibrated with the data obtained from a 26 survey of 2,167 Americans, in order to obtain their preferences for CAV technologies and their 27 household’s annual vehicle transaction decisions. 28 29 Results indicate that the average WTP (of respondents with a non-zero WTP) to add connectivity 30 and Level 3 and Level 4 automations are $110, $5,551, and $14,589, respectively. Long-term 31 fleet evolution suggests that the privately held light-duty vehicle fleet will have 24.8% Level 4 32 AV penetration by 2045 under an annual 5% price drop and constant WTP values. -
Recent Developments in the Automation of Cars
International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 5, Issue 1 (Jan-Feb, 2017), PP. 95-100 RECENT DEVELOPMENTS IN THE AUTOMATION OF CARS- A REVIEW 1Mrudul Amritphale, 2Abhishek Mahesh, 3Ashwini Motiwale, Department of Mechanical Engineering, Acropolis Institute of Technology and Research Indore, Madhya Pradesh, India [email protected] Abstract— Vehicular automation can be defined as the use of assistance systems (ADAS) are engineered to eliminate the technology in a vehicle in such a way that it can assist the driver physical as well as the psychological after-effects of a in the best possible way by reducing his/her mental stresses. The collision by eliminating the possibilities of causing the present world is accepting the rapid changes in the technology collision. In the 21st century, various automobile companies and trying to apply it in the real world. Hence, the automobile are focusing on applying the newly advanced intelligent industry is keenly interested in improvising with the dynamic features of a vehicle by using the new technology. Modern day systems in their cars, in order to assist the drivers while cars such as Mercedes Benz S-class, BMW 7 series etc. are driving as well as to safeguard the passengers travelling in the equipped with such automated systems. Vehicles at present are cars, systems such as adaptive cruise control, advanced semi-automated: fully automated vehicles are under development automatic collision notification, intelligent parking assistant and testing and will take time to be available commercially. system, automatic parking, precrash system, traffic sign Passengers comfort and wellbeing, increased safety and increased recognition, distance control assist, dead man’s switch, anti- highway capacity are among the most important initiatives lock braking system(ABS) and many more. -
Accelerating the Transition to Zevs in Shared and Autonomous Fleets
Accelerating the transition to ZEVs in shared and autonomous fleets December 4, 2018 Table of contents Executive summary 5 1 Introduction 8 2 Background 10 Electromobility 10 Shared passenger mobility models 11 Shared electric passenger fleets 11 Vehicular automation 12 Barriers to adoption 13 3 Review of implications for low-carbon transportation 15 ZEVs 15 Shared 16 Autonomous 18 ZEV + shared 19 ZEV + autonomous 20 Shared + autonomous 20 ZEV + shared + autonomous 20 4 Electromobility in shared mobility fleets 22 Vehicle type 22 Profiles of users and riders of shared use mobility 22 Profiles of users and riders of shared use ZEVs & opportunities for acceleration 23 Challenges 23 Reasons for adopting BEVs in shared mobility 25 Logistics, operations of BEVs within shared use mobility 26 Trip distance 26 Daily vehicle kilometers traveled and parking time 27 BEV range and charging needs 28 5 Practicality and business case for BEVs in shared use mobility 30 Cost of ownership and BEV value proposition in car sharing 30 Cost of ownership and BEV value proposition in ride hailing 31 6 Shared electromobility deployment conclusions 35 Key success factors 35 Lessons learned 35 Policies that support electromobility in shared use fleets 36 Concluding remarks 38 Appendices A. Growth of shared mobility services 40 B. List of ZEV shared mobility services 41 C. Impacts of ride hailing 44 D. Example of BEV car sharing education tools 45 E. Uber ZEV-related communications 46 F. BEV in ride hailing payback, Montréal and London 48 Accelerating the Transition to ZEVs in Shared and Autonomous Fleets 2 List of figures and tables List of figures Figure 1. -
From Local Academic Spin-Off to International Firm: the Case of Vislab
International Business Research; Vol. 13, No. 6; 2020 ISSN 1913-9004 E-ISSN 1913-9012 Published by Canadian Center of Science and Education From Local Academic Spin-Off to International Firm: The Case of VisLab Maria Cristina Arcuri1, Elisa Bocchialini2 & Gino Gandolfi2 1 Department of Economics and Management, University of Florence, Florence, Italy 2 Department of Economics and Management, University of Parma, Parma, Italy Correspondence: Elisa Bocchialini, Department of Economics and Management, University of Parma, Via J.F. Kennedy, 6 – 43125 Parma, Italy. Received: April 7, 2020 Accepted: May 8, 2020 Online Published: May 22, 2020 doi:10.5539/ibr.v13n6p100 URL: https://doi.org/10.5539/ibr.v13n6p100 Abstract Universities play an important role in developing and transferring technology. In Italy, much innovation takes place where universities are located outside large towns, as in the case of VisLab. VisLab, the Vision and Intelligent Systems Laboratory, founded by Prof. Alberto Broggi of Parma University, is a pioneer in perception systems and autonomous vehicle research. It is also the spin-off of the University of Parma acquired by Silicon Valley company Ambarella Inc., in July 2015 for $30 million. After the deal, VisLab remained in Italy and all the staff, about thirty researchers, were hired by VisLab for the Parma location. This paper examines the university-industry interaction and, in particular, academic spin-off, as a source of economic growth, pointing out the importance of the context. The study describes the main characteristics of the VisLab case, including the possible alternative strategies, the structure of the final M&A deal and the advantages deriving from Parma and surrounding area.