DOCSLIB.ORG

Autonomous Vehicle Technology: a Guide for Policymakers

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Autonomous Vehicle Technology: a Guide for Policymakers Autonomous Vehicle Technology A Guide for Policymakers James M. Anderson, Nidhi Kalra, Karlyn D. Stanley, Paul Sorensen, Constantine Samaras, Oluwatobi A. Oluwatola C O R P O R A T I O N For more information on this publication, visit www.rand.org/t/rr443-2 This revised edition incorporates minor editorial changes. Library of Congress Cataloging-in-Publication Data is available for this publication. ISBN: 978-0-8330-8398-2 Published by the RAND Corporation, Santa Monica, Calif. © Copyright 2016 RAND Corporation R® is a registered trademark. Cover image: Advertisement from 1957 for “America’s Independent Electric Light and Power Companies” (art by H. Miller). Text with original: “ELECTRICITY MAY BE THE DRIVER. One day your car may speed along an electric super-highway, its speed and steering automatically controlled by electronic devices embedded in the road. Highways will be made safe—by electricity! No traffic jams…no collisions…no driver fatigue.” Limited Print and Electronic Distribution Rights This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions.html. The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. RAND’s publications do not necessarily reflect the opinions of its research clients and sponsors. Support RAND Make a tax-deductible charitable contribution at www.rand.org/giving/contribute www.rand.org Preface This report builds on RAND’s long tradition of research on advanced technologies. From our research on world-circling spaceships in 1946 to developing the conceptual foundations of the Internet in the early 1960s, RAND has long provided policymakers with guidance about tomorrow’s world. RAND’s recent research on the policy effects of autonomous vehicles includes Liability and Regulation of Autonomous Vehicle Technologies, by Nidhi Kalra, James M. Anderson, and Martin Wachs (2009), and The U.S. Experience with No-Fault Automobile Insurance: A Retrospective, by James M. Anderson, Paul Heaton, and Stephen J. Carroll (2010). Both publications are available on RAND’s website. This report results from the RAND Corporation’s Investment in People and Ideas program. Support for this program is provided, in part, by the generosity of RAND’s donors and by the fees earned on client-funded research. The RAND Transportation, Space, and Technology Program The research reported here was conducted in the RAND Transporta- tion, Space, and Technology Program, which addresses topics relating to transportation systems, space exploration, information and telecom- munication technologies, nano- and biotechnologies, and other aspects of science and technology policy. Program research is supported by government agencies, foundations, and the private sector. iii iv Autonomous Vehicle Technology: A Guide for Policymakers This program is part of RAND Justice, Infrastructure, and Envi- ronment, a division of the RAND Corporation dedicated to improving policy and decisionmaking in a wide range of policy domains, includ- ing civil and criminal justice, infrastructure protection and homeland security, transportation and energy policy, and environmental and nat- ural resource policy. Questions or comments about this report should be sent to the project leader, James Anderson, [email protected]. For more information about the Transportation, Space, and Technology Pro- gram, see http://www.rand.org/transportation or contact the director at [email protected]. This version of the report, RR-443-2, replaces an earlier version that contained an incomplete account of General Motor’s policy on its use of OnStar customer data in footnote 8 on page 69, none of which affected the findings of the report. Contents Preface ............................................................................. iii Figures and Tables ............................................................... xi Summary .........................................................................xiii Acknowledgments ............................................................. xxv Abbreviations ..................................................................xxvii CHAPTER ONE Introduction ....................................................................... 1 What Are Autonomous and Automated Vehicles? ............................ 2 Why Is Autonomous Vehicle Technology Important Now? .................. 4 What Decisions Do Policymakers Face? ........................................ 6 CHAPTER TWO The Promise and Perils of Autonomous Vehicle Technology ............ 9 A Summary of the Social Costs of Driving ..................................... 9 Effects of Autonomous Vehicle Technology on Safety and Crashes ........12 Effect of Autonomous Vehicle Technologies on Mobility for Those Unable to Drive ..............................................................16 Congestion .........................................................................17 Potential Effects on Traffic Congestion ......................................17 Potential Effects on the Costs of Traffic Congestion ...................... 24 Land Use ...........................................................................25 Energy and Emissions Implications of Autonomous Vehicles .............. 28 Fuel Economy .................................................................. 28 Autonomous Vehicles Might Enable Alternative Fuels .....................33 How Will Travel Demand Affect Energy and Emissions? ................ 36 v vi Autonomous Vehicle Technology: A Guide for Policymakers Costs and Disadvantages ........................................................ 38 Conclusion ........................................................................ 40 CHAPTER THREE Current State Law and Legislative Activity .................................41 Nevada’s Certificate of Compliance............................................ 42 Comparison of State Legislation ............................................... 43 Nevada (NRS 482.A and NAC 482.A) ..................................... 44 Florida (Fla. Stat. Title XXIII, Ch. 319, S 145) .............................45 California (Cal. Veh. Code, Division 16.6)................................. 46 Washington, D.C. (L19-0278) ................................................47 Ongoing Legislation in Other States .......................................... 48 Arizona (HB 2167) ............................................................ 48 Colorado (SB 13-016) ......................................................... 48 Hawaii (HB 1461) ..............................................................49 Massachusetts (HB 3369) ......................................................49 Michigan (SB 0169) ........................................................... 50 New Hampshire (HB 444) ................................................... 50 New Jersey (A2757) ........................................................... 50 New York (S4912) .............................................................. 50 Oklahoma (HB 3007) ..........................................................51 Oregon (HB 2428) .............................................................51 South Carolina (HB 4015) .....................................................51 Texas (HB 2932) ................................................................52 Washington (HB 1649) ........................................................52 Wisconsin (SB 80) ..............................................................52 Stakeholder Interviews............................................................53 Conclusion .........................................................................53 CHAPTER FOUR Brief History and Current State of Autonomous Vehicles ...............55 A Brief History ...................................................................55 Phase 1: Foundational Research .............................................. 56 Phase 2: Grand Challenges ................................................... 56 Phase 3: Commercial Development ..........................................57 State of Autonomous Vehicle Technology ......................................58 Contents vii Making Sense of the World ....................................................58 Integrity, Security, and Verification ...........................................70 Policy Implications ................................................................72 Conclusion .........................................................................74 CHAPTER FIVE The Role of Telematics and Communications .............................75 A Review of the Communications Technologies Used by Autonomous Vehicles ....................................................... 77 Commercial Wireless Services ................................................ 77 Dedicated Short-Range Communications ...................................79 Bluetooth .......................................................................
Load more

Recommended publications
  • 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
    [Show full text]
  • Intel and Mobileye Autonomous Driving Solutions
    PRODUCT BRIEF Autonomous Driving Intel and Mobileye Autonomous Driving Solutions Together, Mobileye and Intel deliver scalable and versatile solutions using purpose-built software and efficient, powerful compute that will make autonomous driving an economic reality. Total Technology and Resources Making the self-driving car a reality requires an unprecedented integration of technology and expertise. Together, Mobileye and Intel provide comprehensive, scalable solutions to enable autonomous driving engineered for safety and affordability. With a combination of industry-leading computer vision, unique algorithms, crowdsourced mapping, efficient driving policy, a mathematical model designed for safety, and high-performing, low-power system designs, we deliver a versatile road map that scales to millions, not thousands, of vehicles. Purpose-built software and hardware Mobileye’s portfolio of innovative software across all pillars of autonomous driving (the “eyes”) complements Intel’s high-performance computing and connectivity expertise (the “brains”). This winning package supports complex and computationally intense vision processing with low power consumption, creating powerful and smart autonomous driving solutions from bumper to cloud. We start with revolutionary software from Mobileye that includes: • Low-cost, 360-degree surround-view computer vision • Crowdsourced Road Experience Management™ (REM™) mapping and localization • Sensor fusion, integrating surround vision and mapping with a multimodal sensor system • Efficient, semantic-based artificial intelligence (AI) for driving policy (decision-making) • A formal safety software layer via our Responsibility Sensitive Safety (RSS) model This advanced software calls for a powerful compute platform. We use a combination of proven Mobileye EyeQ® SoCs for computer vision, localization, sensor fusion, trajectory planning, and our RSS model designed for safety, as well as Intel® processors for translation of the planned trajectory into commands that are issued to the vehicle’s actuation system.
    [Show full text]
  • Stanley: the Robot That Won the DARPA Grand Challenge
    STANLEY Winning the DARPA Grand Challenge with an AI Robot_ Michael Montemerlo, Sebastian Thrun, Hendrik Dahlkamp, David Stavens Stanford AI Lab, Stanford University 353 Serra Mall Stanford, CA 94305-9010 fmmde,thrun,dahlkamp,[email protected] Sven Strohband Volkswagen of America, Inc. Electronics Research Laboratory 4009 Miranda Avenue, Suite 150 Palo Alto, California 94304 [email protected] http://www.cs.stanford.edu/people/dstavens/aaai06/montemerlo_etal_aaai06.pdf 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 http://robots.stanford.edu/papers/thrun.stanley05.pdf DARPA Grand Challenge: Final Part 1 Stanley from Stanford 10.54 https://www.youtube.com/watch?v=M2AcMnfzpNg Sebastian Thrun helped build Google's amazing driverless car, powered by a very personal quest to save lives and reduce traffic accidents. 4 minutes https://www.ted.com/talks/sebastian_thrun_google_s_driverless_car THE GREAT ROBOT RACE – documentary Published on Jan 21, 2016 DARPA Grand Challenge—a raucous race for robotic, driverless vehicles sponsored by the Pentagon, which awards a $2 million purse to the winning team. Armed with artificial intelligence, laser-guided vision, GPS navigation, and 3-D mapping systems, the contenders are some of the world's most advanced robots. Yet even their formidable technology and mechanical prowess may not be enough to overcome the grueling 130-mile course through Nevada's desert terrain. From concept to construction to the final competition, "The Great Robot Race" delivers the absorbing inside story of clever engineers and their unyielding drive to create a champion, capturing the only aerial footage that exists of the Grand Challenge.
    [Show full text]
  • Auto Retailing: Why the Franchise System Works Best
    AUTO RETAILING: WHY THE FRANCHISE SYSTEM WORKS BEST Q Executive Summary or manufacturers and consumers alike, the automotive and communities—were much more highly motivated and franchise system is the best method for distributing and successful retailers than factory employees or contractors. F selling new cars and trucks. For consumers, new-car That’s still true today, as evidenced by some key findings franchises create intra-brand competition that lowers prices; of this study: generate extra accountability for consumers in warranty and • Today, the average dealership requires an investment of safety recall situations; and provide enormous local eco- $11.3 million, including physical facilities, land, inventory nomic benefits, from well-paying jobs to billions in local taxes. and working capital. For manufacturers, the franchise system is simply the • Nationwide, dealers have invested nearly $200 billion in most efficient and effective way to distribute and sell automo- dealership facilities. biles nationwide. Franchised dealers invest millions of dollars Annual operating costs totaled $81.5 billion in 2013, of private capital in their retail outlets to provide top sales and • an average of $4.6 million per dealership. These service experiences, allowing auto manufacturers to concen- costs include personnel, utilities, advertising and trate their capital in their core areas: designing, building and regulatory compliance. marketing vehicles. Throughout the history of the auto industry, manufactur- • The vast majority—95.6 percent—of the 17,663 ers have experimented with selling directly to consumers. In individual franchised retail automotive outlets are locally fact, in the early years of the industry, manufacturers used and privately owned.
    [Show full text]
  • Handheld Computing Terminal Industrializing Mobile Gateway for the Internet of Things
    Handheld Computing Terminal Industrializing Mobile Gateway for the Internet of Things Retail Inventory Logistics Management Internet of Vehicles Electric Utility Inspection www.AMobile-solutions.com About AMobile Mobile Gateway for IIoT Dedicated IIoT, AMobile develops mobile devices and gateways for data collectivity, wireless communications, local computing and processing, and connectivity between the fields and cloud. In addition, AMobile's unified management platform-Node-Watch monitors and manages not only mobile devices but also IoT equipment in real-time to realize industrial IoT applications. Manufacturing • IPC mobile • Mobility computing technology Retail Inspection (IP-6X, Military • Premier member grade, Wide- - early access Blockchain temperature) Healthcare Logistics • Manufacturing resource pool Big Data & AI • Logistics • Global customer support Handheld Mobile Computing Data Collector Smart Meter Mobile Inspection Device with Keyboard Assistant Mobile Computing IoT Terminal Device Ultra-rugged Intelligent Barcode Reader Vehicle Mount Handheld Device Handheld Device Tablet Mobile Gateway AMobile Intelligent established in 2013, a joint venture by Arbor Technology, MediaTek, and Wistron, is Pressure Image an expert in industrial mobile computers and solutions for industrial applications including warehousing, Temperature Velocity Gyroscope logistics, manufacturing, and retail. Leveraging group resources of manufacturing and services from Wistron, IPC know-how and distribution channels from Arbor, mobility technology and
    [Show full text]
  • Chapter 3 Review Questions
    Chapter 3 - Learning to Drive PA Driver’s Manual CHAPTER 3 REVIEW QUESTIONS 1. TEENAGE DRIVERS ARE MORE LIKELY TO BE INVOLVED IN A CRASH WHEN: A. They are driving with their pet as a passenger B. They are driving with adult passengers C. They are driving with teenage passengers D. They are driving without any passengers 2. DRIVERS WHO EAT AND DRINK WHILE DRIVING: A. Have no driving errors B. Have trouble driving slow C. Are better drivers because they are not hungry D. Have trouble controlling their vehicles 3. PREPARING TO SMOKE AND SMOKING WHILE DRIVING: A. Do not affect driving abilities B. Help maintain driver alertness C. Are distracting activities D. Are not distracting activities 4. THE TOP MAJOR CRASH TYPE FOR 16 YEAR OLD DRIVERS IN PENNSYLVANIA IS: A. Single vehicle/run-off-the-road B. Being sideswiped on an interstate C. Driving in reverse on a side street D. Driving on the shoulder of a highway 5. WHEN PASSING A BICYCLIST, YOU SHOULD: A. Blast your horn to alert the bicyclist B. Move as far left as possible C. Remain in the center of the lane D. Put on your four-way flashers 6. WHEN YOU DRIVE THROUGH AN AREA WHERE CHILDREN ARE PLAYING, YOU SHOULD EXPECT THEM: A. To know when it is safe to cross B. To stop at the curb before crossing the street C. To run out in front of you without looking D. Not to cross unless they are with an adult 7. IF YOU ARE DRIVING BEHIND A MOTORCYCLE, YOU MUST: A.
    [Show full text]
  • General Rental Information
    GENERAL RENTAL INFORMATION Our rates include: unlimited mileage (except when indicated differently), reduction for damage and theft, car radio, VAT road tax, preparation of the vehicle, registration fees. Our rates do not include: total elimination of penalty reduction for damage and theft, fuel, refuelling service charge, fines, optional clauses (Mini Kasko, Pai, Super Kasko, Gold Kasko, Road Assistance and Plus), extras, supplements, fees for additional services related to fines, tolls, parking tickets and any penalties or charges imposed by authorities, entities, dealers in relation to the circulation of the vehicle, and anything not expressly included. Please note: Vehicles must be returned during office opening hours. If the customer returns the vehicle during the closure of the local office, he will be held liable for all damage to the vehicle that could be caused during the time between the vehicle has been parked and the opening of our office when our local staff collect it. If the rental period exceeds 30 days, you must complete the procedure and accept the obligations deriving from article 94, paragraph 4 bis of the Italian Road Traffic Code, referring to the update of the Vehicles National Register. JOPARKING SRL is not responsible for anything that may occur in the event of non- compliance with these obligations. The vehicle is delivered in perfect condition and it has to return back in the same conditions. Minimum and maximum age: Minimum age allowed for car rental is 23 years. If the driver's age is between 19-20 years of age, upon payment of the "Young Driver", at a cost of € 24.59 + VAT per day, it is possible to rent cars belonging to the groups A/B/C.
    [Show full text]
  • A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
    sensors Review A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges M. Nadeem Ahangar 1, Qasim Z. Ahmed 1,*, Fahd A. Khan 2 and Maryam Hafeez 1 1 School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK; [email protected] (M.N.A.); [email protected] (M.H.) 2 School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan; [email protected] * Correspondence: [email protected] Abstract: The Department of Transport in the United Kingdom recorded 25,080 motor vehicle fatali- ties in 2019. This situation stresses the need for an intelligent transport system (ITS) that improves road safety and security by avoiding human errors with the use of autonomous vehicles (AVs). There- fore, this survey discusses the current development of two main components of an ITS: (1) gathering of AVs surrounding data using sensors; and (2) enabling vehicular communication technologies. First, the paper discusses various sensors and their role in AVs. Then, various communication tech- nologies for AVs to facilitate vehicle to everything (V2X) communication are discussed. Based on the transmission range, these technologies are grouped into three main categories: long-range, medium- range and short-range. The short-range group presents the development of Bluetooth, ZigBee and ultra-wide band communication for AVs. The medium-range examines the properties of dedicated short-range communications (DSRC). Finally, the long-range group presents the cellular-vehicle to everything (C-V2X) and 5G-new radio (5G-NR). An important characteristic which differentiates each category and its suitable application is latency.
    [Show full text]
  • Hierarchical Off-Road Path Planning and Its Validation Using a Scaled Autonomous Car' Angshuman Goswami Clemson University
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Clemson University: TigerPrints Clemson University TigerPrints All Theses Theses 12-2017 Hierarchical Off-Road Path Planning and Its Validation Using a Scaled Autonomous Car' Angshuman Goswami Clemson University Follow this and additional works at: https://tigerprints.clemson.edu/all_theses Recommended Citation Goswami, Angshuman, "Hierarchical Off-Road Path Planning and Its Validation Using a Scaled Autonomous Car'" (2017). All Theses. 2793. https://tigerprints.clemson.edu/all_theses/2793 This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact [email protected]. HIERARCHICAL OFF-ROAD PATH PLANNING AND ITS VALIDATION USING ASCALED AUTONOMOUS CAR A Thesis Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Master of Science Mechanical Engineering by Angshuman Goswami December 2017 Accepted by: Dr. Ardalan Vahidi, Committee Chair Dr. John R. Wagner Dr. Phanindra Tallapragada Abstract In the last few years. while a lot of research efforthas been spent on autonomous vehicle navigation, primarily focused on on-road vehicles, off-road path planning still presents new challenges. Path planning for an autonomous ground vehicle over a large horizon in an unstructured environment when high-resolution a-priori information is available, is still very much an open problem due to the computations involved. Local- ization and control of an autonomous vehicle and how the control algorithms interact with the path planner is a complex task.
    [Show full text]
  • Passenger Response to Driving Style in an Autonomous Vehicle
    Passenger Response to Driving Style in an Autonomous Vehicle by Nicole Belinda Dillen A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Mathematics in Computer Science Waterloo, Ontario, Canada, 2019 c Nicole Dillen 2019 I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Abstract Despite rapid advancements in automated driving systems (ADS), current HMI research tends to focus more on the safety driver in lower level vehicles. That said, the future of automated driving lies in higher level systems that do not always require a safety driver to be present. However, passengers might not fully trust the capability of the ADS in the absence of a safety driver. Furthermore, while an ADS might have a specific set of parameters for its driving profile, passengers might have different driving preferences, some more defensive than others. Taking these preferences into consideration is, therefore, an important issue which can only be accomplished by understanding what makes a passenger uncomfortable or anxious. In order to tackle this issue, we ran a human study in a real-world autonomous vehicle. Various driving profile parameters were manipulated and tested in a scenario consisting of four different events. Physiological measurements were also collected along with self- report scores, and the combined data was analyzed using Linear Mixed-Effects Models. The magnitude of a response was found to be situation dependent: the presence and proximity of a lead vehicle significantly moderated the effect of other parameters.
    [Show full text]
  • Grounding Human-To-Vehicle Advice for Self-Driving Vehicles
    Grounding Human-to-Vehicle Advice for Self-driving Vehicles Jinkyu Kim1, Teruhisa Misu2, Yi-Ting Chen2, Ashish Tawari2, and John Canny1 1EECS, UC Berkeley, 2Honda Research Institute USA, Inc. 1 2 {jinkyu.kim, canny}@berkeley.edu, {tmisu,ychen,atawari}@honda-ri.com Abstract Visual encoder Recent success suggests that deep neural control net- Vehicle works are likely to be a key component of self-driving ve- controller hicles. These networks are trained on large datasets to imi- End-user Input image tate human actions, but they lack semantic understanding of Human-to-Vehicle Advice Textual e.g., “pedestrians are in crosswalk” image contents. This makes them brittle and potentially un- encoder safe in situations that do not match training data. Here, we propose to address this issue by augmenting training control data with natural language advice from a human. Advice commands includes guidance about what to do and where to attend. Visualizing We present a first step toward advice giving, where we train without advice with advice model’s attention an end-to-end vehicle controller that accepts advice. The controller adapts the way it attends to the scene (visual attention) and the control (steering and speed). Attention Figure 1: Our model takes human-to-vehicle advice as an mechanisms tie controller behavior to salient objects in the input, i.e., “pedestrians are in crosswalk”, and grounds it advice. We evaluate our model on a novel advisable driving into the vehicle controller, which then predicts a sequence dataset with manually annotated human-to-vehicle advice of control commands, i.e., a steering wheel angle and a ve- called Honda Research Institute-Advice Dataset (HAD).
    [Show full text]
  • Public Support Measures for Connected and Automated Driving
    Public support measures for connected and automated driving PUBLIC SUPPORT MEASURES FOR CONNECTED AND AUTOMATED DRIVING Final Report Tender No. GROW-SME-15-C-N102 within the Framework contract No. ENTR/300/PP/2013/FC Written by SPI, VTT and ECORYS May - 2017 Page | i Public support measures for connected and automated driving Europe Direct is a service to help you find answers to your questions about the European Union. Freephone number (*): 00 800 6 7 8 9 10 11 (*) The information given is free, as are most calls (though some operators, phone boxes or hotels may charge you). Reference No. GROW-SME-15-C-N102 Framework contract No. ENTR/300/PP/2013/FC 300/PP/2013/FC The information and views set out in this study are those of the authors and do not necessarily reflect the official opinion of the European Commission. The European Commission does not guarantee the accuracy of the data included in this study. Neither the European Commission nor any person acting on the European Commission’s behalf may be held responsible for the use which may be made of the information contained herein. More information on the European Union is available on the Internet (http://www.europa.eu). ISBN 978-92-9202-254-9 doi: 10.2826/083361 AUTHORS OF THE STUDY Sociedade Portuguesa de Inovação (SPI – Coordinator): Augusto Medina, Audry Maulana, Douglas Thompson, Nishant Shandilya and Samuel Almeida. VTT Technical Research Centre of Finland (VTT): Aki Aapaoja and Matti Kutila. ECORYS: Erik Merkus and Koen Vervoort Page | ii Public support measures for connected and automated driving Contents Acronym Glossary ................................................................................................
    [Show full text]