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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. -
FROM LONGITUDE to ALTITUDE: INDUCEMENT PRIZE CONTESTS AS INSTRUMENTS of PUBLIC POLICY in SCIENCE and TECHNOLOGY Clayton Stallbau
FROM LONGITUDE TO ALTITUDE: INDUCEMENT PRIZE CONTESTS AS INSTRUMENTS OF PUBLIC POLICY IN SCIENCE AND TECHNOLOGY Clayton Stallbaumer* I. INTRODUCTION On a foggy October night in 1707, a fleet of Royal Navy warships ran aground on the Isles of Scilly, some twenty miles southwest of England.1 Believing themselves to be safely west of any navigational hazards, two thousand sailors discovered too late that they had fatally misjudged their position.2 Although the search for an accurate method to determine longitude had bedeviled Europe for several centuries, the “sudden loss of so many lives, so many ships, and so much honor all at once” elevated the discovery of a solution to a top national priority in England.3 In 1714, Parliament issued the Longitude Act,4 promising as much as £20,0005 for a “Practicable and Useful” method of determining longitude at sea; fifty-nine years later, clockmaker John Harrison claimed the prize.6 Nearly three centuries after the fleet’s tragic demise, another accident, also fatal but far removed from longitude’s terrestrial reach, struck a nation deeply. Just four days after its crew observed a moment’s silence in memory of the Challenger astronauts, the space shuttle Columbia disintegrated upon reentry, killing all on board.7 Although * J.D., University of Illinois College of Law, 2006; M.B.A., University of Illinois College of Business, 2006; B.A., Economics, B.A., History, Lake Forest College, 2001. 1. DAVA SOBEL, LONGITUDE: THE TRUE STORY OF A LONE GENIUS WHO SOLVED THE GREATEST SCIENTIFIC PROBLEM OF HIS TIME 12 (1995). -
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]. -
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. -
The Challenge Prize— History and Opportunity
1 THE CHALLENGE PRIZE— HISTORY AND OPPORTUNITY By Larry E. Tise, PhD Historian President, International Congress of Distinguished Awards February 2006 © Larry E. Tise 2006 The Challenge Prize: What Is It? The “Challenge Prize” is a special type of award or prize that has been employed throughout human history to encourage new creative, innovative, and often heroic achievements. The offer of a challenge reward has been used historically to draw forth previously unknown individuals to lead armies to conquests, to motivate individuals or teams in sport to championships, to find hidden secrets in nature, to discover mysteries in science, to innovate new tools to facilitate research, and to produce products that might prove adaptable in altering or “improving” life or the human condition. Kings, generals, and the captains of industry have understood the usefulness of throwing out a challenge that might just captivate the imagination or spirit of a single human being to produce a product or result that can advance national leadership, military conquest, or a hefty profit. Whether issued in the form of promised reward heralded by royal trumpeters and posted on every tree in the realm, issued before throngs of citizenry or soldiery, or posted on the internet as a request for proposals (RFP), the notion of a earning or winning a prize for producing a result that surpasses all others or solves a particular problem, humans— particularly in competitive and entrepreneurial societies—are fully familiar with the notion of the challenge prize and recognize—viscerally if not consciously—the allurement and romance of meeting a challenge, being proclaimed the victor, and securing the promised reward. -
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. -
Design of JET Humanoid Robot with Compliant Modular Actuators for Industrial and Service Applications
applied sciences Article Design of JET Humanoid Robot with Compliant Modular Actuators for Industrial and Service Applications Jaehoon Sim 1 , Seungyeon Kim 1 , Suhan Park 1 , Sanghyun Kim 2 , Mingon Kim 1 and Jaeheung Park 1,3,∗ 1 Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Korea; [email protected] (J.S.); [email protected] (S.K.); [email protected] (S.P.); [email protected] (M.K.) 2 Korea Institute of Machinery and Materials (KIMM), Daejeon 34103, Korea; [email protected] 3 Advanced Institute of Convergence Technology (AICT), Suwon 16229, Korea * Correspondence: [email protected]; Tel.: +82-31-888-9146 Abstract: This paper presents the development of the JET humanoid robot, which is based on the existing THORMANG platform developed in 2015. Application in the industrial and service fields was targeted, and three design concepts were determined for the humanoid robot. First, low stiffness of the actuator modules was utilized for compliance with external environments. Second, to maximize the robot whole-body motion capability, the overall height was increased. However, the weight was reduced to satisfy power requirements. The workspace was also increased to enable various postures, by increasing the range of motion of each joint and extending the links. Compared to the original THORMANG platform, the lower limb length increased by approximately 20%, and the hip range of motion increased by 39.3%. Third, the maintenance process was simplified through modularization of the electronics and frame design for improved accessibility. Several experiments, including stair Citation: Sim, J.; Kim, S.; Park, S.; climbing and egress from a car, were performed to verify that the JET humanoid robot performance Kim, S.; Kim, M.; Park, J. -
UC Berkeley Previously Published Works
UC Berkeley UC Berkeley Previously Published Works Title Building the Second Mind, 1961-1980: From the Ascendancy of ARPA-IPTO to the Advent of Commercial Expert Systems Permalink https://escholarship.org/uc/item/7ck3q4f0 ISBN 978-0-989453-4-6 Author Skinner, Rebecca Elizabeth Publication Date 2013-12-31 eScholarship.org Powered by the California Digital Library University of California Building the Second Mind, 1961-1980: From the Ascendancy of ARPA to the Advent of Commercial Expert Systems copyright 2013 Rebecca E. Skinner ISBN 978 09894543-4-6 Forward Part I. Introduction Preface Chapter 1. Introduction: The Status Quo of AI in 1961 Part II. Twin Bolts of Lightning Chapter 2. The Integrated Circuit Chapter 3. The Advanced Research Projects Agency and the Foundation of the IPTO Chapter 4. Hardware, Systems and Applications in the 1960s Part II. The Belle Epoque of the 1960s Chapter 5. MIT: Work in AI in the Early and Mid-1960s Chapter 6. CMU: From the General Problem Solver to the Physical Symbol System and Production Systems Chapter 7. Stanford University and SRI Part III. The Challenges of 1970 Chapter 8. The Mansfield Amendment, “The Heilmeier Era”, and the Crisis in Research Funding Chapter 9. The AI Culture Wars: the War Inside AI and Academia Chapter 10. The AI Culture Wars: Popular Culture Part IV. Big Ideas and Hardware Improvements in the 1970s invert these and put the hardware chapter first Chapter 11. AI at MIT in the 1970s: The Semantic Fallout of NLR and Vision Chapter 12. Hardware, Software, and Applications in the 1970s Chapter 13. -
Strategic Latency: Red, White, and Blue Managing the National and International Security Consequences of Disruptive Technologies Zachary S
Strategic Latency: Red, White, and Blue Managing the National and International Security Consequences of Disruptive Technologies Zachary S. Davis and Michael Nacht, editors Center for Global Security Research Lawrence Livermore National Laboratory February 2018 Disclaimer: This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes. LLNL-BOOK-746803 Strategic Latency: Red, White, and Blue: Managing the National and International Security Consequences of Disruptive Technologies Zachary S. Davis and Michael Nacht, editors Center for Global Security Research Lawrence Livermore National Laboratory February -
Grand Innovation Prizes: a Theoretical, Normative, and Empirical Evaluation
Research Policy 41 (2012) 1779–1792 Contents lists available at SciVerse ScienceDirect Research Policy jou rnal homepage: www.elsevier.com/locate/respol Grand Innovation Prizes: A theoretical, normative, and empirical evaluation a,∗ b a,1 c Fiona Murray , Scott Stern , Georgina Campbell , Alan MacCormack a MIT Sloan School of Management, 100 Main Street – e62-470, Cambridge, MA 02142, United States b MIT Sloan School of Management and NBER, 100 Main Street – e62-474, Cambridge, MA 02142, United States c Harvard Business School, Soldiers Field Road, Boston, MA 02163, United States a r t i c l e i n f o a b s t r a c t Article history: This paper provides a systematic examination of the use of a Grand Innovation Prize (GIP) in action – the Received 16 May 2011 Progressive Automotive Insurance X PRIZE –a $10 million prize for a highly efficient vehicle. Following a Received in revised form 7 June 2012 mechanism design approach we define three key dimensions for GIP evaluation: objectives, design, and Accepted 17 June 2012 performance, where prize design includes ex ante specifications, ex ante incentives, qualification rules, Available online 31 October 2012 and award governance. Within this framework we compare observations of GIPs from three domains – empirical reality, theory, and policy –tobetter understand their function as an incentive mechanism Keywords: for encouraging new solutions to large-scale social challenges. Combining data from direct observation, Innovation personal interviews, and surveys, together with analysis of extant theory and policy documents on GIPs, X PRIZE Incentive our results highlight three points of divergence: first, over the complexity of defining prize specifications; Energy secondly, over the nature and role of incentives, particularly patents; thirdly, the overlooked challenges Competition associated with prize governance.