Safety of Autonomous Vehicles
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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. -
Future Outlook in Commercial Vehicles
ADAS In Commercial Pulse Q2’20 Vehicles What’s inside ? 1. Activities of 4 key commercial vehicle manufacturers in ADAS and higher autonomy • Tesla, Daimler Trucks, Traton and Volvo Trucks Image: Daimler Freightliner Inspiration Truck Activities of 4 key emerging players in autonomous CVs • Embark, TuSimple, Nikola Motors, Einride 2. Regulations impacting autonomous CVs in the U.S & EU 3. Future outlook THEMES AND Themes covered in this scope Key Takeaways . Players are focusing on Level-4 technologies KEY TAKEAWAYS Activities of 4 key commercial vehicle that take over on the highways, aiming at manufacturers in ADAS & higher autonomy improving safety and gaining greater fuel o Tesla efficiency. e.g. from platooning. IN ADAS in CVs o Daimler Trucks . Collaborative business models and o Traton investments could accelerate L4-L5 capabilities o Volvo Trucks in the near future and find use cases in last-mile delivery, construction and mining areas Today, we are seeing vehicle automation quickly becoming available throughout the Activities of 4 key emerging players in commercial vehicle market with significant autonomous commercial vehicles . Players like Embark plans to skip Level 3 and go interest and investment by the players. o Embark straight to Level 4, while TuSimple has ambitious o TuSimple plans to scale up Level 4 freight network by 2021 o Nikola Motors . Hydrogen powered autonomous trucks could gain ADAS functionalities such as adaptive cruise o Einride traction and fuel the freight chain control (ACC), automatic emergency braking (AEB) and lane keeping assist (LKA) are . Currently, no federal regulation on autonomous accelerating for commercial vehicles. Truck Regulation impacting autonomous commercial vehicles in the U.S and EU trucking technology exists in the U.S. -
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]. -
State of the Art of Adaptive Cruise Control and Stop and Go Systems
1st AUTOCOM Workshop on Preventive and Active Safety Systems for Road Vehicles State of the Art of Adaptive Cruise Control and Stop and Go Systems Emre Kural, Tahsin Hacıbekir, and Bilin Aksun Güvenç Department of Mechanical Engineering, østanbul Technical University, Gümüúsuyu, Taksim, østanbul, TR-34437, Turkey Abstract—This paper presents the state of the art of Number of Traffic Accidents in European Country Adaptive Cruise Control (ACC) and Stop and Go systems as x1000 well as Intelligent Transportation Systems enhanced with inter vehicle communication. The sensors used in these 400 systems and the level of their current technology are introduced. Simulators related to ACC and Stop and Go 300 (S&G) systems are also surveyed and the MEKAR 200 simulator is presented. Finally, future trends of ACC and Stop and Go systems and their advantages are emphasized. 100 0 I. INTRODUCTION y in e ly K y n a c a U e a n It rk a u New trends in automotive technology result in new rm Sp r e F T systems to produce safer and more comfortable vehicles. G Many driver assistant systems are introduced by automotive manufacturers in order to prevent a possible Figure 1. Traffic Accident Statistics in Europe accident from happening either by intervening in the And as a result, the driver’s routine interventions are control of the vehicle temporarily or by warning the lessened and due to a more controlled driving, fuel driver audibly and visually. consumption will be reduced. On the other hand, the The key reason as to why researchers focus on the automation of highways will also allow an increase in the subject of producing safer cars is related to the statistics number of vehicles traveling on roads without causing that expose the serious consequences of accidents. -
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. -
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. -
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 ................................................................................................................................................... -
ADVANCED DRIVER ASSISTANCE TECHNOLOGY NAMES AAA’S Recommendation for Common Naming of Advanced Safety Systems
JANUARY 2019 ADVANCED DRIVER ASSISTANCE TECHNOLOGY NAMES AAA’s recommendation for common naming of advanced safety systems NewsRoom.AAA.com Advanced Driver Assistance Technology Names (this page intentionally left blank) © 2019 American Automobile Association, Inc. 2 Advanced Driver Assistance Technology Names Abstract Advanced Driver Assistance Systems have become increasingly prevalent on new vehicles. In fact, at least one ADAS feature is available on 92.7% of new vehicles available in the U.S. as of May 2018.1 Not only are these advanced driver assistance systems within financial reach of many new car consumers (about $1,950 for the average ADAS bundle2), they also have the potential to avoid or mitigate the severity of a crash. However, the terminology used to describe them varies widely and often seems to prioritize marketing over clarity. The lack of standardized names for automotive systems adds confusion for motorists when researching and using advanced safety systems. The intent of this paper is to create a dialog with the automotive industry, safety organizations and legislators about the need for common naming for advanced driver assistance systems. Within this report, AAA is proposing a set of standardized technology names for use in describing advanced safety systems. AAA acknowledges that this is a dynamic environment, and that further input from stakeholders and consumer research will further refine this recommendation. To date, automakers have devised their own branded technology names which, for example, has resulted in twenty unique names for adaptive cruise control and nineteen different names for lane keeping assistance (section 3.2) alone. A selection of these names is shown in Figure 1. -
Aptiv Application to Test Automated Driving Systems (ADS)
Charles D. Baker. Governor Karyn E Polito. lJeutenant Governor massDOT Stephanie Pollack. MassDOT Secretary & CEO Massachusetts Department of Transportation Application to Test Automated Driving Systems on Public Ways in Massachusetts CONTACT INFORMA T/ON: Name of Organization: Aptiv Services US LLC and its affiliate nuTonomy, Inc. Primary Contact Person: Abe Ghabra Title: Managing Director, Las Vegas Technical Center Street Address of Company's Headquarters Office: 100 Northern Ave. Suite 200. City, Town of Headquarters Office: Boston State: MA Zip Code: 02210 Country: USA Website: https:ll www.aptlv.com/autonomous-mobllfty CERT/FICA TION: The Applicant certifies that all information contained within this application is true, accurate and complete to the best ofIts knowledge. /i~L /t:h-- Abe Ghabra 01 January 2020 Srgnature ofApplicant's Representative Printed Name Date of Signing Managing Director, Las Vegas Technical Center Position and Title Ten Park Plaza, Suite 4160, Boston, MA 0211 6 Tel. 857-368-4636, TTY: 857-368-0655 www.rnass.gov/massdot Application to Test Automated Driving Systems on Public Ways in Massachusetts Detailed Information Detail # 1: Experience with Automated Driving Systems (ADS) Detail # 2: Operational Design Domain Detail # 3: Summary of Training and Operations Protocol Detail # 4: First Responders Interaction Plan Detail # 5: Applicant’s Voluntary Safety Self-Assessment Detail # 6: Motor Vehicles in Testing Program Detail # 7: Drivers in Testing Program Detail # 8: Insurance Requirements Detail # 9: Additional Questions Note: Applicants should not disclose any confidential information or other material considered to be trade secrets, as the applications are considered to be public records. The Massachusetts Public Records Law applies to records created by or in the custody of a state or local agency, board or other government entity.