Autonomous Vehicle Ecosystem Analysis & Opportunities
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Autonomous Vehicles
10/27/2017 Looming on the Horizon: Autonomous Vehicles Jim Hedlund Highway Safety North MN Toward Zero Deaths Conference October 26, 2017 St. Paul, MN In the next 25 minutes • AV 101 . What’s an autonomous vehicle (AV)? . What’s on the road now? . What’s coming and when? . What does the public think about AVs? . What are current state laws on AVs? • Traffic safety issues for states . AV testing . AV operations . What should states do . What should national organizations do 2 1 10/27/2017 Report released Feb. 2, 2017 3 Autonomous = self-driving, right? So what’s the challenge? • When all vehicles are autonomous: . The passenger economy: transportation as a service . No crashes (at least none due to driver error – about 94% currently) • And it’s coming soon . 26 states and DC with AV legislation or executive orders (and AVs probably can operate in most states without law changes) 4 2 10/27/2017 NCSL October 2017 www.ghsa.org @GHSAHQ 5 But … • It’s far more complicated than that 6 3 10/27/2017 What’s an AV? • Level 0: no automation, driver in complete control • Level 1: driver assistance . Cruise control or lane position, driver monitors at all times • Level 2: occasional self-driving . Control both speed and lane position in limited situations, like Interstates; driver monitors at all times ************** • Level 3: limited self-driving in some situations, like Interstates . Vehicle in full control, informs when driver must take control • Level 4: full self-driving under certain conditions . Vehicle in full control for entire trip, such as urban ride-sharing • Level 5: full self-driving at all times 7 What’s on the road now? • Level 1 available for many years . -
Waymo Rolls out Autonomous Vans Without Human Drivers 7 November 2017, by Tom Krisher
Waymo rolls out autonomous vans without human drivers 7 November 2017, by Tom Krisher get drowsy, distracted or drunk. Google has long stated its intent to skip driver- assist systems and go directly to fully autonomous driving. The Waymo employee in the back seat won't be able to steer the minivan, but like all passengers, will be able to press a button to bring the van safely to a stop if necessary, Waymo said. Within a "few months," the fully autonomous vans will begin carrying volunteer passengers who are now taking part in a Phoenix-area test that includes use of backup drivers. Waymo CEO John Krafcik, who was to make the In this Sunday, Jan. 8, 2017, file photo, a Chrysler announcement Tuesday at a conference in Pacifica hybrid outfitted with Waymo's suite of sensors Portugal, said the company intends to expand the and radar is shown at the North American International testing to the entire 600-square-mile Phoenix area Auto Show in Detroit. Waymo is testing vehicles on and eventually bring the technology to more cities public roads with only an employee in the back seat. The around the world. It's confident that its system can testing started Oct. 19 with an automated Chrysler Pacifica minivan in the Phoenix suburb of Chandler, Ariz. handle all situations on public roads without human It's a major step toward vehicles driving themselves intervention, he said. without human backups on public roads. (AP Photo/Paul Sancya, File) "To have a vehicle on public roads without a person at the wheel, we've built some unique safety features into this minivan," Krafcik said in remarks prepared for the conference. -
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 Landscape Jan2020
Created By: The Autonomous Vehicles landscape Designed by: Powered By: Marc Amblard Last Update January 2020 Orsay Consulting AV Applications Analytics AV Fleet Goods Delivery (13) Shuttle & Robo-Taxis (21) Driving Aid & Monitoring (18) Occupant understanding (15) Management (8) Designated Gatik 2getthere Aurrigo Auro Robotics AEV Robotics AutoX BARO e.GO Moove EasyMile Braiq Cambridge Carvi GreenRoad i4drive IntelliVision KeepTruckin Aectiva eyeSight Fotonation Genesis Bestmile Fleetonomy Boxbot Dispatch Einride Kiwi Campus VEHICLES EdgeTensor Eyeris Driver Mobile Telematics Technologies Lab LM Industries Lohr May Mobility Navya NEXT Future Optimus Ride Open Motors Ottopia Phantom Auto RideOS Marble Nuro RoboCV Robby Robomart ISFM LightMetrics Nauto NetraDyne Roadsense Safe Drive Seegrid Smartdrive Guardian Optical Innov Plus Jungo Life Detection Pertech Phasya Technology Transportation Technology Systems System Technologies Connectivity Technologies Solutions Ride Cell Vulog Starship TeleRetail Udelv Softcar Waymo Zoox Ziiko Coast Autonomous Voyomotive Zendrive DriveTrust Samsara Seeing Machine Smart Eye Vayyar Wrnch Technologies Robotics AV Software Stack Automotive Stack (70) Localization & Mapping (37) Simulation and Validation (35) Dvpt tools (20) AImotive Algolux Aptiv Argo AI Ascent Apex.AI Aurora Autonomous AutoX Deeproute AI Autonomous Applanix Artisense Accerion atlatec Carmera Civil Maps Combain Ception Almotive ANSYS Applied AV Simulation Alpha Drive Algolux Autonomou Apex.AI Robotics Innovation Fusion Intelligent Driving -
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. -
Self-Driving Cars: a Survey Claudine Badue A,∗, Rânik Guidolini A, Raphael Vivacqua Carneiro A, Pedro Azevedo A, Vinicius B
Expert Systems With Applications 165 (2021) 113816 Contents lists available at ScienceDirect Expert Systems With Applications journal homepage: www.elsevier.com/locate/eswa Review Self-driving cars: A survey Claudine Badue a,<, Rânik Guidolini a, Raphael Vivacqua Carneiro a, Pedro Azevedo a, Vinicius B. Cardoso a, Avelino Forechi b, Luan Jesus a, Rodrigo Berriel a, Thiago M. Paixão c, Filipe Mutz c, Lucas de Paula Veronese a, Thiago Oliveira-Santos a, Alberto F. De Souza a a Departamento de Informática, Universidade Federal do Espírito Santo, Av. Fernando Ferrari 514, 29075-910, Goiabeiras, Vitória, Espírito Santo, Brazil b Coordenadoria de Engenharia Mecânica, Instituto Federal do Espírito Santo, Av. Morobá 248, 29192–733, Morobá, Aracruz, Espírito Santo, Brazil c Coordenadoria de Informática, Instituto Federal do Espírito Santo, ES-010 Km-6.5, 29173-087, Manguinhos, Serra, Espírito Santo, Brazil ARTICLEINFO ABSTRACT Keywords: We survey research on self-driving cars published in the literature focusing on autonomous cars developed Self-driving cars since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE Robot localization level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into Occupancy grid mapping the perception system and the decision-making system. The perception system is generally divided into many Road mapping subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles Moving objects detection Moving objects tracking detection and tracking, road mapping, traffic signalization detection and recognition, among others. The Traffic signalization detection decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as Traffic signalization recognition route planning, path planning, behavior selection, motion planning, and control. -
Las Estrategias De Las Empresas Automovilísticas Con El Coche
Facultad de Ciencias Económicas y Empresariales LAS ESTRATEGIAS DE LAS EMPRESAS AUTOMOVILÍSTICAS CON EL COCHE AUTÓNOMO Y LOS NUEVOS JUGADORES Autor: María Gabriela Anitua Galdón Director: Miguel Á ngel López Gómez MADRID | Abril 2019 2 RESUMEN El presente trabajo analiza las diversas estrategias concernientes al coche autónomo, implementadas por los nuevos jugadores en contraposición con los competidores tradicionales. En primer lugar, se contextualiza la temática en cuestión, a través de un enfoque histórico y conceptual del vehículo automatizado. Posteriormente, se presentan una serie de teorías estratégicas relacionadas con la temática planteada y las implicaciones de éstas. A continuación, a través del estudio de casos, se extraen determinadas conclusiones relativas al futuro del sector automovilístico, así como una serie de posibles líneas de investigación y estrategias ganadoras para los competidores, con el fin de alcanzar la victoria en la carrera planteada, que supondrá el cambio más relevante de los próximos siglos en relación a la industria automovilística. Palabras clave: vehículo autónomo, estrategia, transporte, nuevos jugadores, industria automovilística, innovación disruptiva y automatización. 3 ABSTRACT This paper analyses the various strategies concerning the autonomous vehicle, implemented by new players in contrast to traditional competitors. First of all, the subject matter is contextualized, through a historical and conceptual approach of the automated vehicle. Subsequently, a series of strategic theories related to the proposed theme and its consequences are presented. The case studies draw certain conclusions regarding the future of the automobile sector, as well as a series of possible lines of research and winning strategies for competitors, in order to achieve victory in the proposed race, which will represent the most important change in the coming centuries in relation to the automotive sector. -
Autonomous Vehicle Benchmarking Using Unbiased Metrics
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) October 25-29, 2020, Las Vegas, NV, USA (Virtual) Autonomous Vehicle Benchmarking using Unbiased Metrics David Paz1∗, Po-jung Lai1∗, Nathan Chan2∗, Yuqing Jiang2∗ Henrik I. Christensen2∗ Abstract— With the recent development of autonomous vehi- ment reports2 are not time and distance normalized: did the cle technology, there have been active efforts on the deployment vehicle experience five disengagements during the course of this technology at different scales that include urban and of 10 miles or 10,000 miles? Or did it experience five highway driving. While many of the prototypes showcased have been shown to operate under specific cases, little effort disengagements over the course of 10 minutes or 2,000 has been made to better understand their shortcomings and hours? generalizability to new areas. Distance, uptime and number of Given the lack of spatiotemporal information, in many manual disengagements performed during autonomous driving cases, these unnormalized reports make it impossible to provide a high-level idea on the performance of an autonomous quantify the performance and robustness of the autonomous system but without proper data normalization, testing location information, and the number of vehicles involved in testing, systems and most importantly quantify their overall safety the disengagement reports alone do not fully encompass system with respect to other autonomous systems or human drivers. performance and robustness. Thus, in this study a complete set This study aims to shed light on autonomous system of metrics are applied for benchmarking autonomous vehicle technology performance and safety by leveraging spatiotem- systems in a variety of scenarios that can be extended for poral information and metrics geared towards benchmarking comparison with human drivers and other autonomous vehicle 3 systems. -
New Mobility: Today's Technology and Policy Landscape
WHITE PAPER JULY 2017 NEW MOBILITY: TODAY’S TECHNOLOGY AND POLICY LANDSCAPE Peter Slowik and Fanta Kamakaté www.theicct.org [email protected] BEIJING | BERLIN | BRUSSELS | SAN FRANCISCO | WASHINGTON ACKNOWLEDGMENTS This work was conducted with the generous support of The 11th Hour Project of the Schmidt Family Foundation and the William and Flora Hewlett Foundation. The authors thank the experts, practitioners, and observers identified throughout the report who agreed to answer our many questions and share their understanding of this quickly evolving sector. Drew Kodjak, Nic Lutsey, and Joe Schultz provided critical reviews on an earlier version of the report. Any errors are the authors’ own. International Council on Clean Transportation 1225 I Street NW, Suite 900 Washington, DC 20005 USA [email protected] | www.theicct.org | @TheICCT © 2017 International Council on Clean Transportation NEW MOBILITY: TODAY’S TECHNOLOGY AND POLICY LANDSCAPE TABLE OF CONTENTS I. Introduction .............................................................................................................................1 II. Definitions and background ................................................................................................ 2 New mobility taxonomy ........................................................................................................................ 2 III. New mobility landscape ..................................................................................................... 5 Key players, policies, -
CITY of BELLEVUE Smart Mobility Plan 2018 Prepared for the City of Bellevue by Table of Contents EXECUTIVE SUMMARY
CITY OF BELLEVUE Smart Mobility Plan 2018 Prepared for the City of Bellevue by Table of Contents EXECUTIVE SUMMARY ............................................... 7 INTRODUCTION .......................................................... 11 A Transformation in Transportation ...................................................................... 11 Vision and Goals ........................................................................................................ 13 A Look Back ................................................................................................................ 14 Preview into the Future ............................................................................................ 15 Elements of the Smart Mobility Plan ..................................................................... 16 Priority Projects Summary ...................................................................................... 17 CITY INITIATIVES ....................................................... 19 Shared-Use Mobility .................................................................................................. 20 Autonomous & Connected Vehicles ...................................................................... 25 Electric Vehicles ......................................................................................................... 34 Real-Time Traveler Information .............................................................................. 39 Data Management ................................................................................................... -
2014 Automated Vehicles Symposium Proceedings
2014 Automated Vehicles Symposium Proceedings 2014 Automated Vehicle Symposium – Synopsis of Proceedings DISCLAIMER The opinions, findings, and conclusions expressed in this publication are those of the speakers and contributors to the Automated Vehicle Symposium and not necessarily those of the United States Department of Transportation. The United States Government assumes no liability for its contents or use thereof. If trade names manufacturers’ names or specific products are mentioned, it is because they are considered essential to the object of the publication and should not be construed as an endorsement. The United States Government does not endorse products or manufacturers. 2014 Automated Vehicles Symposium Proceedings—Final December 2014 Publication Number: FHWA-JPO-14-176 Prepared by: Volpe National Transportation Systems Center Cambridge, MA 02142 Prepared for: Intelligent Transportation Systems Joint Program Office Office of the Assistant Secretary for Research and Technology 1200 New Jersey Avenue, S.E. Washington, DC 20590 2 Acknowledgments The 2014 Automated Vehicles Symposium (AVS 2014) was produced through a partnership between the Association of Unmanned Vehicle Systems International (AUVSI) and Transportation Research Board (TRB), and is indebted to its many volunteer contributors and speakers who developed the symposium content, produced sessions, and contributed to the development of these proceedings. AVS 2014 also benefitted from the direct financial contributions of its commercial benefactors, and the institutional support of the many companies and agencies that supported the time, travel, and participation for their staff. The organizing committee gratefully acknowledges the United States Department of Transportation (U.S. DOT) Intelligent Transportation Systems Joint Program Office (ITS JPO) for the level of its support to AVS 2014 and for these proceedings. -
2017/19: Should Australia Adopt Driverless Vehicles? File:///C:/Dpfinal/Schools/Doca2017/2017Autocar/2017Autocar.Html
2017/19: Should Australia adopt driverless vehicles? file:///C:/dpfinal/schools/doca2017/2017autocar/2017autocar.html ? 2017/19: Should Australia adopt driverless vehicles? What they said... 'A large proportion (of car accidents) could be avoided by using self-driving vehicles' Hussein Dia, chair of Civil Engineering at Swinburne University of Technology ' If we see children distracted by the ice cream truck across the street, we know to slow down, as they may dash toward it. Today's computers aren't nearly as skilled at interpreting complex situations like these' Andrew Ng, director of Baidu's Institute of Deep Learning The issue at a glance On November 29, 2017, it was announced that Perth would be one of the first cities in the world to test fully-automated vehicles in 2018. The city will trial driverless hire cars capable of picking up passengers. http://www.abc.net.au/news/2017-11-29/uber-style-driverless-cars-to-be-tested-in- perth-in-global-trial/9207120 The year before, in August 2016, Perth began trialling Australia's first driverless shuttle bus along the foreshore in South Perth. http://www.abc.net.au/news/2016-08-31/driverless-bus-trialed-in- south-perth/7802976 The technology is proving attractive to other Australian states and territories who are already making changes to road laws to ready themselves for trials of automated vehicles. https://www.ntc.gov.au/current-projects/changing-driving-laws-to-support-automated-vehicles/ However, a range of analysts have suggested the enthusiasm for automated vehicles is premature and that the new technology will be more problematic than many realise.