Analýza Autonomních Vozidel
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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 -
Result Build a Self-Driving Network
SELF-DRIVING NETWORKS “LOOK, MA: NO HANDS” Kireeti Kompella CTO, Engineering Juniper Networks ITU FG Net2030 Geneva, Oct 2019 © 2019 Juniper Networks 1 Juniper Public VISION © 2019 Juniper Networks Juniper Public THE DARPA GRAND CHALLENGE BUILD A FULLY AUTONOMOUS GROUND VEHICLE IMPACT • Programmers, not drivers • No cops, lawyers, witnesses GOAL • Quadruple highway capacity Drive a pre-defined 240km course in the Mojave Desert along freeway I-15 • Glitches, insurance? • Ethical Self-Driving Cars? PRIZE POSSIBILITIES $1 Million RESULT 2004: Fail (best was less than 12km!) 2005: 5/23 completed it 2007: “URBAN CHALLENGE” Drive a 96km urban course following traffic regulations & dealing with other cars 6 cars completed this © 2019 Juniper Networks 3 Juniper Public GRAND RESULT: THE SELF-DRIVING CAR: (2009, 2014) No steering wheel, no pedals— a completely autonomous car Not just an incremental improvement This is a DISRUPTIVE change in automotive technology! © 2019 Juniper Networks 4 Juniper Public THE NETWORKING GRAND CHALLENGE BUILD A SELF-DRIVING NETWORK IMPACT • New skill sets required GOAL • New focus • BGP/IGP policies AI policy Self-Discover—Self-Configure—Self-Monitor—Self-Correct—Auto-Detect • Service config service design Customers—Auto-Provision—Self-Diagnose—Self-Optimize—Self-Report • Reactive proactive • Firewall rules anomaly detection RESULT Free up people to work at a higher-level: new service design and “mash-ups” POSSIBILITIES Agile, even anticipatory service creation Fast, intelligent response to security breaches CHALLENGE -
An Off-Road Autonomous Vehicle for DARPA's Grand Challenge
2005 Journal of Field Robotics Special Issue on the DARPA Grand Challenge MITRE Meteor: An Off-Road Autonomous Vehicle for DARPA’s Grand Challenge Robert Grabowski, Richard Weatherly, Robert Bolling, David Seidel, Michael Shadid, and Ann Jones. The MITRE Corporation 7525 Colshire Drive McLean VA, 22102 [email protected] Abstract The MITRE Meteor team fielded an autonomous vehicle that competed in DARPA’s 2005 Grand Challenge race. This paper describes the team’s approach to building its robotic vehicle, the vehicle and components that let the vehicle see and act, and the computer software that made the vehicle autonomous. It presents how the team prepared for the race and how their vehicle performed. 1 Introduction In 2004, the Defense Advanced Research Projects Agency (DARPA) challenged developers of autonomous ground vehicles to build machines that could complete a 132-mile, off-road course. Figure 1: The MITRE Meteor starting the finals of the 2005 DARPA Grand Challenge. 2005 Journal of Field Robotics Special Issue on the DARPA Grand Challenge 195 teams applied – only 23 qualified to compete. Qualification included demonstrations to DARPA and a ten-day National Qualifying Event (NQE) in California. The race took place on October 8 and 9, 2005 in the Mojave Desert over a course containing gravel roads, dirt paths, switchbacks, open desert, dry lakebeds, mountain passes, and tunnels. The MITRE Corporation decided to compete in the Grand Challenge in September 2004 by sponsoring the Meteor team. They believed that MITRE’s work programs and military sponsors would benefit from an understanding of the technologies that contribute to the DARPA Grand Challenge. -
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Accepted March 22, 2020 Digital Object Identifier 10.1109/ACCESS.2020.2983149 A Survey of Autonomous Driving: Common Practices and Emerging Technologies EKIM YURTSEVER1, (Member, IEEE), JACOB LAMBERT 1, ALEXANDER CARBALLO 1, (Member, IEEE), AND KAZUYA TAKEDA 1, 2, (Senior Member, IEEE) 1Nagoya University, Furo-cho, Nagoya, 464-8603, Japan 2Tier4 Inc. Nagoya, Japan Corresponding author: Ekim Yurtsever (e-mail: [email protected]). ABSTRACT Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high- level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state- of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development. INDEX TERMS Autonomous Vehicles, Control, Robotics, Automation, Intelligent Vehicles, Intelligent Transportation Systems I. INTRODUCTION necessary here. CCORDING to a recent technical report by the Eureka Project PROMETHEUS [11] was carried out in A National Highway Traffic Safety Administration Europe between 1987-1995, and it was one of the earliest (NHTSA), 94% of road accidents are caused by human major automated driving studies. The project led to the errors [1]. Against this backdrop, Automated Driving Sys- development of VITA II by Daimler-Benz, which succeeded tems (ADSs) are being developed with the promise of in automatically driving on highways [12]. -
AN ADVANCED VISION SYSTEM for GROUND VEHICLES Ernst
AN ADVANCED VISION SYSTEM FOR GROUND VEHICLES Ernst Dieter Dickmanns UniBw Munich, LRT, Institut fuer Systemdynamik und Flugmechanik D-85577 Neubiberg, Germany ABSTRACT where is it relative to me and how does it move? (VT2) 3. What is the likely future motion and (for subjects) intention of the object/subject tracked? (VT3). These ‘Expectation-based, Multi-focal, Saccadic’ (EMS) vision vision tasks have to be solved by different methods and on has been developed over the last six years based on the 4- different time scales in order to be efficient. Also the D approach to dynamic vision. It is conceived around a fields of view required for answering the first two ‘Multi-focal, active / reactive Vehicle Eye’ (MarVEye) questions are quite different. Question 3 may be answered with active gaze control for a set of three to four more efficiently by building on the results of many conventional TV-cameras mounted fix relative to each specific processes answering question 2, than by resorting other on a pointing platform. This arrangement allows to image data directly. both a wide simultaneous field of view (> ~ 100°) with a Vision systems for a wider spectrum of tasks in central region of overlap for stereo interpretation and high ground vehicle guidance have been addresses by a few resolution in a central ‘foveal’ field of view from one or groups only. The Robotics Institute of Carnegie Mellon two tele-cameras. Perceptual and behavioral capabilities University (CMU) [1-6] and DaimlerChrysler Research in are now explicitly represented in the system for improved Stuttgart/Ulm [7-12] (together with several university flexibility and growth potential. -
A Hierarchical Control System for Autonomous Driving Towards Urban Challenges
applied sciences Article A Hierarchical Control System for Autonomous Driving towards Urban Challenges Nam Dinh Van , Muhammad Sualeh , Dohyeong Kim and Gon-Woo Kim *,† Intelligent Robotics Laboratory, Department of Control and Robot Engineering, Chungbuk National University, Cheongju-si 28644, Korea; [email protected] (N.D.V.); [email protected] (M.S.); [email protected] (D.K.) * Correspondence: [email protected] † Current Address: Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Korea. Received: 23 April 2020; Accepted: 18 May 2020; Published: 20 May 2020 Abstract: In recent years, the self-driving car technologies have been developed with many successful stories in both academia and industry. The challenge for autonomous vehicles is the requirement of operating accurately and robustly in the urban environment. This paper focuses on how to efficiently solve the hierarchical control system of a self-driving car into practice. This technique is composed of decision making, local path planning and control. An ego vehicle is navigated by global path planning with the aid of a High Definition map. Firstly, we propose the decision making for motion planning by applying a two-stage Finite State Machine to manipulate mission planning and control states. Furthermore, we implement a real-time hybrid A* algorithm with an occupancy grid map to find an efficient route for obstacle avoidance. Secondly, the local path planning is conducted to generate a safe and comfortable trajectory in unstructured scenarios. Herein, we solve an optimization problem with nonlinear constraints to optimize the sum of jerks for a smooth drive. In addition, controllers are designed by using the pure pursuit algorithm and the scheduled feedforward PI controller for lateral and longitudinal direction, respectively. -
An Introduction of Autonomous Vehicles and a Brief Survey
Journal of Critical Reviews ISSN- 2394-5125 Vol 7, Issue 13, 2020 AN INTRODUCTION OF AUTONOMOUS VEHICLES AND A BRIEF SURVEY 1Tirumalapudi Raviteja, 2Rajay Vedaraj I.S 1Research Scholar, School of Mechanical Engineering, Vellore instutite of technology, Tamilnadu, India, Email: [email protected] . 2Professor, School of Mechanical Engineering, Vellore instutite of technology, Tamilnadu, India, Email: [email protected] Received: 09.04.2020 Revised: 10.05.2020 Accepted: 06.06.2020 Abstract: An autonomous car is also called a self-driving car or driverless car or robotic car. Whatever the name but the aim of the technology is the same. Down the memory line, autonomous vehicle technology experiments started in 1920 only and controlled by radio technology. Later on, trails began in 1950. From the past few years, updating automation technology day by day and using all aspects of using in regular human life. The present scenario of human beings is addicted to automation and robotics technology using like agriculture, medical, transportation, automobile and manufacturing industries, IT sector, etc. For the last ten years, the automobile industry came forward to researching autonomous vehicle technology (Waymo Google, Uber, Tesla, Renault, Toyota, Audi, Volvo, Mercedes-Benz, General Motors, Nissan, Bosch, and Continental's autonomous vehicle, etc.). Level-3 Autonomous cars came out in 2020. Everyday autonomous vehicle technology researchers are solving challenges. In the future, without human help, robots will manufacture autonomous cars using IoT technology based on customer requirements and prefer these vehicles are very safe and comfortable in transportation systems like human traveling or cargo. Autonomous vehicles need data and updating continuously, so in this case, IoT and Artificial intelligence help to share the information device to the device. -
AUTONOMOUS VEHICLES the EMERGING LANDSCAPE an Initial Perspective
AUTONOMOUS VEHICLES THE EMERGING LANDSCAPE An Initial Perspective 1 Glossary Abbreviation Definition ACC Adaptive Cruise Control - Adjusts vehicle speed to maintain safe distance from vehicle ahead ADAS Advanced Driver Assistance System - Safety technologies such as lane departure warning AEB Autonomous Emergency Braking – Detects traffic situations and ensures optimal braking AUV Autonomous Underwater Vehicle – Submarine or underwater robot not requiring operator input AV Autonomous Vehicle - vehicle capable of sensing and navigating without human input CAAC Cooperative Adaptive Cruise Control – ACC with information sharing with other vehicles and infrastructure CAV Connected and Autonomous Vehicles – Grouping of both wirelessly connected and autonomous vehicles DARPA US Defense Advanced Research Projects Agency - Responsible for the development of emerging technologies EV Electric Vehicle – Vehicle that used one or more electric motors for propulsion GVA Gross Value Added - The value of goods / services produced in an area or industry of an economy HGV Heavy Goods Vehicle – EU term for any truck with a gross combination mass over 3,500kg (same as US LGV) HMI Human Machine Interface – User interface between a vehicle and the driver / passenger IATA International Air Transport Association - Trade association of the world’s airlines LIDAR Light Detection and Ranging - Laser-based 3D scanning and sensing MaaS Mobility as a Service - Mobility solutions that are consumed as a service rather than purchased as a product ODD Operational Design -
Self-Driving Car Using Artificial Intelligence Prof
International Journal of Future Generation Communication and Networking Vol. 13, No. 3s, (2020), pp. 612–615 Self-Driving Car Using Artificial Intelligence Prof. S.R. Wategaonkar #1, Sujoy Bhattacharya*2, Shubham Bhitre*3 Prem Kumar Singh *4, Khushboo Bedse *5 #Professor and Project Faculty member, Department of Electronic and Telecommunication, Bharati Vidyapeeth’s College of Engineering, Navi Mumbai, Maharashtra, India. *Project Student, Department of Electronic and Telecommunication, Bharati Vidyapeeth’s College of Engineering, Navi Mumbai, Maharashtra, India. [email protected] [email protected] [email protected] [email protected] [email protected] Abstract Self-driving cars have the prospective to transform urban mobility by providing safe, convenient and congestion free transportability. This autonomous vehicle as an application of Artificial Intelligence (AI) have several difficulties like traffic light detection, lane end detection, pedestrian, signs etc. This problem can be overcome by using technologies such as Machine Learning (ML), Deep Learning (DL) and Image Processing. In this paper, author’s propose deep neural network for lane and traffic light detection. The model is trained and assessed using the dataset, which contains the front view image frames and the steering angle data captured when driving on the road. Keeping cars inside the lane is a very important feature of self-driving cars. It learns how to keep the vehicle in lane from human driving data. The paper presents Artificial Intelligence based autonomous navigation and obstacle avoidance of self-driving cars, applied with Deep Learning to a simulated cars in an urban environment. Keywords—Self-Driving car, Autonomous driving, Object Detection, Lane End Detection, Deep Learning, Traffic light Detection. -
Stanley: the Robot That Won the DARPA Grand Challenge
Stanley: The Robot that Won the DARPA Grand Challenge ••••••••••••••••• •••••••••••••• Sebastian Thrun, Mike Montemerlo, Hendrik Dahlkamp, David Stavens, Andrei Aron, James Diebel, Philip Fong, John Gale, Morgan Halpenny, Gabriel Hoffmann, Kenny Lau, Celia Oakley, Mark Palatucci, Vaughan Pratt, and Pascal Stang Stanford Artificial Intelligence Laboratory Stanford University Stanford, California 94305 Sven Strohband, Cedric Dupont, Lars-Erik Jendrossek, Christian Koelen, Charles Markey, Carlo Rummel, Joe van Niekerk, Eric Jensen, and Philippe Alessandrini Volkswagen of America, Inc. Electronics Research Laboratory 4009 Miranda Avenue, Suite 100 Palo Alto, California 94304 Gary Bradski, Bob Davies, Scott Ettinger, Adrian Kaehler, and Ara Nefian Intel Research 2200 Mission College Boulevard Santa Clara, California 95052 Pamela Mahoney Mohr Davidow Ventures 3000 Sand Hill Road, Bldg. 3, Suite 290 Menlo Park, California 94025 Received 13 April 2006; accepted 27 June 2006 Journal of Field Robotics 23(9), 661–692 (2006) © 2006 Wiley Periodicals, Inc. Published online in Wiley InterScience (www.interscience.wiley.com). • DOI: 10.1002/rob.20147 662 • Journal of Field Robotics—2006 This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without manual intervention. The robot’s software system relied predominately on state-of-the-art artificial intelligence technologies, such as machine learning and probabilistic reasoning. This paper describes the major components of this architecture, and discusses the results of the Grand Chal- lenge race. © 2006 Wiley Periodicals, Inc. 1. INTRODUCTION sult of an intense development effort led by Stanford University, and involving experts from Volkswagen The Grand Challenge was launched by the Defense of America, Mohr Davidow Ventures, Intel Research, ͑ ͒ Advanced Research Projects Agency DARPA in and a number of other entities. -
Självkörande Bussar I Stadstrafik
Självkörande bussar i stadstrafik - förstudie Jan Jacobson, Kari Westgaard Berg, Daniel Bügel, Kristian Flink, Anders Thorsén, Charlotta Tornvall, Mari Lie Venjum RISE Rapport 2018:63 Självkörande bussar i stadstrafik - förstudie Jan Jacobson, Kari Westgaard Berg, Daniel Bügel, Kristian Flink, Anders Thorsén, Charlotta Tornvall, Mari Lie Venjum 1 Abstract Automated buses in urban traffic - prestudy Automated road transport is regarded as a key enabler for sustainable transport. One example is the use of small automated buses as a supplement to already existing public transport services. There are several manufacturers of these kind of buses, and field trials are in progress. The goal of the pre-project is to evaluate the feasibility and criteria for transport with automated buses in two middle-sized Nordic municipalities, Lørenskog in Norway and Borås in Sweden, by analyzing at least two different test-cases in each location. Feasibility, adaptation to existing traffic and conditions for public acceptance are described. The pre-project concludes that automated buses are possible in these two municipalities. Further test and demonstrations should be made. Key words: automated driving, shuttle bus, automated transport RISE Research Institutes of Sweden AB RISE Rapport 2018:63 ISBN: 978-91-88907-06-6 Borås 2018 2 Innehåll Abstract ....................................................................................................... 1 Innehåll ..................................................................................................... -
Introducing Driverless Cars to UK Roads
Introducing Driverless Cars to UK Roads WORK PACKAGE 5.1 Deliverable D1 Understanding the Socioeconomic Adoption Scenarios for Autonomous Vehicles: A Literature Review Ben Clark Graham Parkhurst Miriam Ricci June 2016 Preferred Citation: Clark, B., Parkhurst, G. and Ricci, M. (2016) Understanding the Socioeconomic Adoption Scenarios for Autonomous Vehicles: A Literature Review. Project Report. University of the West of England, Bristol. Available from: http://eprints.uwe.ac.uk/29134 Centre for Transport & Society Department of Geography and Environmental Management University of the West of England Bristol BS16 1QY UK Email enquiries to [email protected] VENTURER: Introducing driverless cars to UK roads Contents 1 INTRODUCTION .............................................................................................................................................. 2 2 A HISTORY OF AUTONOMOUS VEHICLES ................................................................................................ 2 3 THEORETICAL PERSPECTIVES ON THE ADOPTION OF AVS ............................................................... 4 3.1 THE MULTI-LEVEL PERSPECTIVE AND SOCIO-TECHNICAL TRANSITIONS ............................................................ 4 3.2 THE TECHNOLOGY ACCEPTANCE MODEL ........................................................................................................ 8 3.3 SUMMARY ...................................................................................................................................................