Government Access to Vehicle-Generated Data May 2020
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How to Make Use of Data in a Car: Connected Cars, Payment Tech, Analytics, and Other Opportunities
HOW TO MAKE USE OF DATA IN A CAR: CONNECTED CARS, PAYMENT TECH, ANALYTICS, AND OTHER OPPORTUNITIES Andrew Ray David Monteiro May 13, 2020 Tess Blair @MLGlobalTech © 2018 Morgan, Lewis & Bockius LLP Morgan Lewis Automotive Hour Webinar Series Series of automotive industry focused webinars led by members of the Morgan Lewis global automotive team. The 10-part 2020 program is designed to provide a comprehensive overview on a variety of topics related to clients in the automotive industry. Upcoming sessions: JUNE 10 | Employee Benefits in the Automotive and Mobility Context JULY 15 | Working with, or Operating, a Tech Startup in the Automotive and Mobility Sectors AUGUST 5 | Electric Vehicles and Their Energy Impact SEPTEMBER 23 | Autonomous Vehicles Regulation and State Developments NOVEMBER 11 | Environmental Developments and Challenges in the Automotive Space DECEMBER 9 | Capitalizing on Emerging Technology in the Automotive and Mobility Space 2 Table of Contents Section 01 – Introductions Section 02 – Market Overview Section 03 – Data Acquisition and Use Section 04 – Regulatory and Enforcement Risks 3 SECTION 01 INTRODUCTIONS Today’s Presenters Andrew Ray David Monteiro Tess Blair Washington, DC Dallas Philadelphia Tel +1.202.373.6585 Tel +1.214.466.4133 Tel +1.215.963.5161 [email protected] [email protected] [email protected] 5 SECTION 02 MARKET OVERVIEW 7 Market Overview • 135 million Americans spend 51 minutes on average commuting to work five days a week. • Connected commerce experience represents a $230 billion market. • Since 2010, investors have poured $20.8 billion into connectivity and infotainment technologies. Source: “2019 Digital Drive Report,” P97 / PYMNTS.com; “Start me up: Where mobility investments are going,” McKinsey & Company. -
Tesla Autopilot : Semi Autonomous Driving, an Uptick for Future Autonomy
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 09 | Sep-2016 www.irjet.net p-ISSN: 2395-0072 Tesla Autopilot : Semi Autonomous Driving, an Uptick for Future Autonomy Shantanu Ingle1, Madhuri Phute2 1Department of E&TC, Pune Institute of Computer Technology, Pune, Maharashtra, India 2 Department of E&TC, Pune Institute of Computer Technology, Pune, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Manual driving has certainly changed the way response to traffic conditions. Once the driver arrives at the we travel and explore different parts of the globe, with total destination, Model S or Model X scans for a parking space human control and with human intuitions and previous and parallel parks on the driver's command[2]. In the new driving experiences. But manual control doesn’t ensure safety Autopilot, the instrument cluster’s new driver focused and zero fatalities in every possible driving condition. design shows the real-time information used by the car to Autonomous driving is the answer to that problem. However, intelligently determine the vehicle’s behavior in that artificial intelligence hasn't yet touched every aspect of moment relative to its surroundings[3]. Along with the technology. In this paper we present a brief overview about autopilot feature, the driving behavior of each Tesla car, how Tesla Motor’s semi autonomous driving technology called while navigating through different road conditions, is shared Tesla Autopilot is changing the way we navigate and transport to its central server. Based on machine learning and Tesla by incorporating state of the art current artificial intelligence design engineer recommendations, a new feature update is and hardware technology, and enhancing the next generation developed and launched to every other Tesla car on planet. -
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 . -
Connected Car
Connected Car [email protected] 1 Confidential – © 2019 Oracle Internal/Restricted/Highly Restricted I've always been asked, „What is my favorite car?” and I've always said „The next one”. Carroll Shelby Source: Wikipedia 2 Confidential – © 2019 Oracle Internal/Restricted/Highly Restricted Connected Car or Autonomous Car Connected vehicles can exchange information wirelessly with other vehicles and infrastructure, but also with the vehicle manufacture or third-party service providers. Automated vehicles, on the other hand, are vehicles in which at least some aspects of safety- critical control functions occur without direct driver input. 3 Confidential – © 2019 Oracle Internal/Restricted/Highly Restricted The Race is On to Capture In-Vehicle Commerce By 2020, there will be 250 Million connected vehicles on the road globally Gartner & Connected Vehicle Trade Association 82% of new cars will be connected to Internet in 2021 Business Insider Connected car commerce will zoom to $265 billion by 2023 Juniper Research Automakers align with tech firms Voice technology will prevail Source: Business Insider 4 Confidential – © 2019 Oracle Internal/Restricted/Highly Restricted Car Data Facts • What are the risks of allowing direct access to car data? • How do vehicle makers and third party providers protect my personal data and privacy? • Why share car data? • What is the safest and most secure way to share car data? • Will vehicle data be available to all service providers and under the same conditions? • What kind of data can my car share? 5 Confidential – © 2019 Oracle Internal/Restricted/Highly Restricted Car Data • Diverse data types • Speed, Engine RPM, Throttle, Load, Pressure, Gear, Braking, Torque, Steer, Wheels rotations and many more (eg. -
3.17.2020 Request for Feedback on AV Legislation Staff Draft Sections
TO: Majority and Minority Staff of Senate Commerce, Science, and Transportation Committee and House Energy and Commerce Committee FROM: Advocates for Highway and Auto Safety, Consumer Federation of America, KidsAndCars.org, and Trauma Foundation DATE: March 17, 2020 RE: Request for Input on Draft Autonomous Vehicle Legislative Language Introduction Thank you for the opportunity to provide feedback to the final seven sections of potential autonomous vehicle (AV) legislation. Please note that in the attached redlined draft legislative language, there may be additional recommendations not necessarily included in this memo. Similarly, in this memo we detail concerns which may not be directly attached to the redlined legislative language as well as provide justifications for changes we did redline. As you have announced that you have completed the distribution of draft legislative sections, we would like to reiterate that stakeholder feedback should be shared publicly to provide transparency and the opportunity for collaboration as well as the identification of shared policy priorities in order to forge a path forward on the Nation’s first AV law. Because this law will have long-lasting and wide-reaching impacts on safety for all road users, infrastructure, mobility, accessibility, the environment and land use, public transit, first responders and law enforcement, it must be done with robust discussion, debate and deliberation. We would like to begin by reiterating our safety priorities and policy positions that are also included in more detail in our previous memos. They remain relevant and vital in developing any legislation on AVs. In sum, they are: • Our Nation’s first law on AVs must ensure the safe development and deployment of all AVs, including partially automated vehicles whether for sale or use as a pay-for mobility option. -
In the Court of Chancery of the State of Delaware
EFiled: Sep 06 2016 07:12PM EDT Transaction ID 59520160 Case No. 12723- IN THE COURT OF CHANCERY OF THE STATE OF DELAWARE ELLEN PRASINOS, derivatively on behalf ) TESLA MOTORS, INC., ) ) Plaintiff, ) ) vs. ) ) C.A. No. ELON MUSK, BRAD W. BUSS, ROBYN ) M. DENHOLM, IRA EHRENPREIS, ) ANTONIO J. GRACIAS, STEPHEN T. ) JURVETSON, KIMBAL MUSK, ) LYNDON RIVE, PETER RIVE, JOHN H. ) N. FISHER, JEFFREY B. STRAUBEL, D ) SUBSIDIARY, INC., AND SOLARCITY ) CORPORATION, ) ) Defendants, ) ) -and- ) ) TESLA MOTORS, INC., a Delaware ) Corporation, ) ) Nominal Defendant ) VERIFIED STOCKHOLDER DERIVATIVE COMPLAINT Plaintiff Ellen Prasinos (“Plaintiff”), by and through her undersigned counsel, assert this action derivatively on behalf of Tesla Motors, Inc. (“Telsa” or the “Company”) against defendants Elon Musk, Brad W. Buss, Robyn M. Denholm, Ira Ehrenpreis, Antonio J. Gracias, Stephen T. Jurvetson and Kimbal Musk (collectively, the “Board” or the “Director Defendants”), along with Lyndon Rive, Peter Rive, John H. N. Fisher, J.B. Straubel, D Subsidiary, Inc., and 1 ME1 23271632v.1 SolarCity Corporation (“SolarCity”). Plaintiff alleges, upon information and belief based upon, inter alia , the investigation made by her attorneys, except as to those allegations that pertain to the Plaintiff herself, which are alleged upon knowledge, as follows: SUMMARY OF THE ACTION 1. Elon Musk (“Musk”), a smart and charismatic businessman, wants to save the world by “making life multi-planetary”, and “expedit[ing] the move from a mine-and-burn hydrocarbon economy towards a solar electric economy.” While Musk’s goal to save the world may be admirable, he uses unethical and illegal tactics to achieve that goal, especially when his personal financial interests and legacy are at stake. -
On Processing Personal Data in the Context of Connected Vehicles and Mobility Related Applications
Guidelines 1/2020 on processing personal data in the context of connected vehicles and mobility related applications Version 1.0 Adopted on 28 January 2020 Adopted - version for public consultation 1 Table of contents 1 INTRODUCTION ................................................................................................................................ 3 1.1 Related works ........................................................................................................................... 4 1.2 Applicable law .......................................................................................................................... 5 1.3 Scope ........................................................................................................................................ 6 1.4 Definitions ................................................................................................................................ 9 1.5 Privacy and data protection risks ........................................................................................... 10 2 GENERAL RECOMMENDATIONS..................................................................................................... 12 2.1 Categories of data .................................................................................................................. 12 2.2 Purposes ................................................................................................................................. 14 2.3 Relevance and data minimisation ......................................................................................... -
Connected Car Is Talking
Your connected car is talking. Who’s listening? Moving the data-driven user experience forward with value, security and privacy @YourCar: Feeling extra #chatty today. kpmg.com @YourCar: “Monday. 8:23 a.m. 37 degrees. Pulling out of the driveway with Passenger Alex and heading to the office at 123 Main Street.” © 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. The KPMG name and logo are registered trademarks or trademarks of KPMG International. NDPPS 604896 Contents About the authors 1 A message from Gary Silberg 3 Securing the high value of data 5 Big data speaks volumes 8 The risky road ahead 14 A closer look under the hood 19 Cybersecurity in a connected car 22 Reaching your data destination 24 About KPMG 28 © 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. The KPMG name and logo are registered trademarks or trademarks of KPMG International. NDPPS 604896 About the authors Gary Silberg is KPMG LLP’s (KPMG) national sector lead partner for the automotive industry. With more than 25 years of business experience, including more than 15 years in the automotive industry, he is a leading voice in the media on global trends in the automotive industry. He advises numerous domestic and multinational companies in areas of strategy, mergers, acquisitions, divestitures, and joint ventures. -
Assessing the Sustainability Implications of Autonomous Vehicles: Recommendations for Research Community Practice
sustainability Review Assessing the Sustainability Implications of Autonomous Vehicles: Recommendations for Research Community Practice Eric Williams 1,*, Vivekananda Das 1 and Andrew Fisher 2 1 Golisano Institute for Sustainability, Rochester Institute of Technology, Rochester, NY 14623, USA; [email protected] 2 Environmental Resources Management, Fairport, NY 14450, USA; [email protected] * Correspondence: [email protected] Received: 3 February 2020; Accepted: 25 February 2020; Published: 3 March 2020 Abstract: Autonomous vehicles (AV) are poised to induce disruptive changes, with significant implications for the economy, the environment, and society. This article reviews prior research on AVs and society, and articulates future needs. Research to assess future societal change induced by AVs has grown dramatically in recent years. The critical challenge in assessing the societal implications of AVs is forecasting how consumers and businesses will use them. Researchers are predicting the future use of AVs by consumers through stated preference surveys, finding analogs in current behaviors, utility optimization models, and/or staging empirical “AV-equivalent” experiments. While progress is being made, it is important to recognize that potential behavioral change induced by AVs is massive in scope and that forecasts are difficult to validate. For example, AVs could result in many consumers abandoning private vehicles for ride-share services, vastly increased travel by minors, the elderly and other groups unable to drive, and/or increased recreation and commute miles driven due to increased utility of in-vehicle time. We argue that significantly increased efforts are needed from the AVs and society research community to ensure 1) the important behavioral changes are analyzed and 2) models are explicitly evaluated to characterize and reduce uncertainty. -
A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods
applied sciences Review A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods Jianjun Ni 1,2,* , Yinan Chen 1, Yan Chen 1, Jinxiu Zhu 1,2 , Deena Ali 1 and Weidong Cao 1,2 1 College of IOT Engineering, Hohai University, Changzhou 213022, China; [email protected] (Y.C.); [email protected] (Y.C.); [email protected] (J.Z.); [email protected] (D.A.); [email protected] (W.C.) 2 Jiangsu Universities and Colleges Key Laboratory of Special Robot Technology, Hohai University, Changzhou 213022, China * Correspondence: [email protected]; Tel.: +86-519-8519-1711 Received: 23 March 2020; Accepted: 11 April 2020; Published: 16 April 2020 Abstract: Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning. -
Driving Security Into Connected Cars: Threat Model and Recommendations
Driving Security Into Connected Cars: Threat Model and Recommendations Numaan Huq, Craig Gibson, Rainer Vosseler TREND MICRO LEGAL DISCLAIMER The information provided herein is for general information Contents and educational purposes only. It is not intended and should not be construed to constitute legal advice. The information contained herein may not be applicable to all situations and may not reflect the most current situation. Nothing contained herein should be relied on or acted 4 upon without the benefit of legal advice based on the particular facts and circumstances presented and nothing herein should be construed otherwise. Trend Micro The Concept of Connected Cars reserves the right to modify the contents of this document at any time without prior notice. Translations of any material into other languages are intended solely as a convenience. Translation accuracy is not guaranteed nor implied. If any questions arise related to the accuracy of a translation, please refer to 10 the original language official version of the document. Any discrepancies or differences created in the translation are Research on Remote Vehicle Attacks not binding and have no legal effect for compliance or enforcement purposes. Although Trend Micro uses reasonable efforts to include accurate and up-to-date information herein, Trend Micro makes no warranties or representations of any kind as to its accuracy, currency, or completeness. You agree 20 that access to and use of and reliance on this document and the content thereof is at your own risk. Trend Micro Threat Model for Connected Cars disclaims all warranties of any kind, express or implied. Neither Trend Micro nor any party involved in creating, producing, or delivering this document shall be liable for any consequence, loss, or damage, including direct, indirect, special, consequential, loss of business profits, or special damages, whatsoever arising out of access to, 26 use of, or inability to use, or in connection with the use of this document, or any errors or omissions in the content thereof. -
A Strategic Audit of Tesla, Inc. Cody Mccain University of Nebraska - Lincoln
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Honors Theses, University of Nebraska-Lincoln Honors Program Spring 4-30-2019 A Strategic Audit of Tesla, Inc. Cody McCain University of Nebraska - Lincoln Follow this and additional works at: https://digitalcommons.unl.edu/honorstheses Part of the Accounting Commons, and the Agribusiness Commons McCain, Cody, "A Strategic Audit of Tesla, Inc." (2019). Honors Theses, University of Nebraska-Lincoln. 132. https://digitalcommons.unl.edu/honorstheses/132 This Thesis is brought to you for free and open access by the Honors Program at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Honors Theses, University of Nebraska-Lincoln by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. A Strategic Audit of Tesla, Inc. An Undergraduate Honors Thesis Submitted in Partial Fulfillment of University Honors Program Requirements University of Nebraska – Lincoln by Cody McCain, BS Accounting and Agribusiness College of Business April 29, 2019 Faculty Mentors: Samuel Nelson, PhD, Center of Entrepreneurship 1 Abstract After Tesla completed its first every back to back profitable quarters at the end of 2018, sales started to decline in the first quarter of 2019 and many question if the company would every be profitable. Through a strategic audit of Tesla and the electric vehicle industry several key factors have been identified to help improve Tesla’s profitability. Analysis tools used to analyze the company and the industry include Porter’s Five Forces, SWOT Analysis, and PEST Analysis. At the conclusion of the audit there are three recommendations given to help improve Tesla’s strategy.