Driving Security Into Connected Cars: Threat Model and Recommendations
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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. -
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. -
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. -
Automotive Foundational Software Solutions for the Modern Vehicle Overview
www.qnx.com AUTOMOTIVE FOUNDATIONAL SOFTWARE SOLUTIONS FOR THE MODERN VEHICLE OVERVIEW Dear colleagues in the automotive industry, We are in the midst of a pivotal moment in the evolution of the car. Connected and autonomous cars will have a place in history alongside the birth of industrialized production of automobiles, hybrid and electric vehicles, and the globalization of the market. The industry has stretched the boundaries of technology to create ideas and innovations previously only imaginable in sci-fi movies. However, building such cars is not without its challenges. AUTOMOTIVE SOFTWARE IS COMPLEX A modern vehicle has over 100 million lines of code and autonomous vehicles will contain the most complex software ever deployed by automakers. In addition to the size of software, the software supply chain made up of multiple tiers of software suppliers is unlikely to have common established coding and security standards. This adds a layer of uncertainty in the development of a vehicle. With increased reliance on software to control critical driving functions, software needs to adhere to two primary tenets, Safety and Security. SAFETY Modern vehicles require safety certification to ISO 26262 for systems such as ADAS and digital instrument clusters. Some of these critical systems require software that is pre-certified up to ISO 26262 ASIL D, the highest safety integrity level. SECURITY BlackBerry believes that there can be no safety without security. Hackers accessing a car through a non-critical ECU system can tamper or take over a safety-critical system, such as the steering, brakes or engine systems. As the software in a car grows so does the attack surface, which makes it more vulnerable to cyberattacks. -
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). -
Product Catalogue 2017
PRODUCT CATALOGUE 2017 EXCELLENCE IN AUTOMOTIVE ENGINEERING CONTENTS INTRODUCTION 4 VEHICLE TRACKING 5 VEHICLE SECURITY 8 MOTORBIKE SECURITY 12 PARKING SENSORS 14 REAR SEAT ENTERTAINMENT 16 SPEED LIMITER AND CRUISE CONTROL 18 LED LIGHTING 20 VEHICLE CAMERA SYSTEMS 23 BLUETOOTH 24 NOTES 25 VESTATEC PRODUCT CATALOGUE 2017 3 DISTRIBUTED LINES AND SEAMLESS SOLUTIONS TAILORED DIRECTLY TO YOUR NEEDS Established in 1987, Vestatec celebrates its 30th anniversary this year. Today we are Tier 1 suppliers, supplying to the vehicle production line, to seven vehicle manufacturers and we are approved accessory suppliers to 14 brand in the UK. This experience allows us to provide tailored original equipment products to the specialist aftermarket. Our range of services include dedicated installation manuals, installer training, technical helpline and in-house field support team, enabling us to match the vehicle's manufacturer warranty. Whatever you need, we have the expertise and experience to deliver, every time. 4 VESTATEC PRODUCT CATALOGUE 2017 VEHICLE TRACKING From Europe’s number 1 telematics manufacturer, MetaSystem SPA (over 2 million units a year) Meta Trak is a state of the art connected car platform. The perfect combination of mobile App and in-vehicle hardware, delivering live vehicle tracking, driver scoring and multi vehicle access as well as old school stolen vehicle tracking. Meta Trak works seamlessly with any vehicle or machine, delivering peace of mind with always-on-tracking and fast theft alerts. 4 VESTATEC PRODUCT CATALOGUE 2017 VESTATEC PRODUCT CATALOGUE 2017 5 META TRAK VTS A Thatcham Category VTS Insurance Accredited GPS Tracking System. Mobile/Tablet App for IOS and Android, Web Platform with Locate on Demand, Driver Recognition Tags, Driver Card Not Present Alerts, 12 and 24 Volt Compatible, Waterproof, Transferable from Vehicle to Vehicle, Battery Disconnect Alerts, Tow-Away Alerts, Battery Low Alerts via Push Notification/ Email, Monitored by SOC. -
A Low-Cost Deep Neural Network-Based Autonomous Car
DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car Michael G. Bechtely, Elise McEllhineyy, Minje Kim?, Heechul Yuny y University of Kansas, USA. fmbechtel, elisemmc, [email protected] ? Indiana University, USA. [email protected] Abstract—We present DeepPicar, a low-cost deep neural net- task may be directly linked to the safety of the vehicle. This work based autonomous car platform. DeepPicar is a small scale requires a high computing capacity as well as the means to replication of a real self-driving car called DAVE-2 by NVIDIA. guaranteeing the timings. On the other hand, the computing DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces hardware platform must also satisfy cost, size, weight, and car steering angles as output. DeepPicar uses the same net- power constraints, which require a highly efficient computing work architecture—9 layers, 27 million connections and 250K platform. These two conflicting requirements complicate the parameters—and can drive itself in real-time using a web camera platform selection process as observed in [25]. and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we To understand what kind of computing hardware is needed analyze the Pi 3’s computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. for AI workloads, we need a testbed and realistic workloads. We also systematically compare other contemporary embedded While using a real car-based testbed would be most ideal, it computing platforms using the DeepPicar’s CNN-based real-time is not only highly expensive, but also poses serious safety control workload. -
Anti-Texting Law Facts
Anti-Texting Law Facts Pennsylvania’s anti-texting law, effective March 8, 2012, encourages motorists to put their full focus on driving. What the Law Does The law prohibits as a primary offense any driver from using an Interactive Wireless Communication Device (IWCD) to send, read or write a text-based communication while his or her vehicle is in motion. Defines an IWCD as a wireless phone, personal digital assistant, smart phone, portable or mobile computer or similar devices that can be used for texting, instant messaging, emailing or browsing the Internet. Defines a text-based communication as a text message, instant message, email or other written communication composed or received on an IWCD. Institutes a $50 fine for convictions under this section. Makes clear that this law supersedes and preempts any local ordinances restricting the use of interactive wireless devices by drivers. The penalty is a summary offense with a $50 fine, plus court costs and other fees. The violation carries no points as a penalty and will not be recorded on the driver record for non- commercial drivers. It will be recorded on commercial drivers’ records as a non-sanction violation. The texting ban does NOT include the use of a GPS device, a system or device that is physically or electronically integrated into the vehicle, or a communications device that is affixed to a mass transit vehicle, bus or school bus. The law does not authorize the seizure of an IWCD. Background, Nationwide Perspective In 2010, there were 13,846 crashes in Pennsylvania where distracted driving played a role. -
Road Safety Fundamentals: Concepts, Strategies
Road Safety Fundamentals Concepts, Strategies, and Practices that Reduce Fatalities and Injuries on the Road Notice This document is disseminated under the sponsorship of the U.S. Department of Transportation (USDOT) in the interest of information exchange. The U.S. Government assumes no liability for the use of the information contained in this document. The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names appear in this report only because they are considered essential to the objective of the document. Quality Assurance Statement The Federal Highway Administration (FHWA) provides high-quality information to serve Government, industry, and the public in a manner that promotes public understanding. Standards and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement. This document can be downloaded for free in full or by individual unit at: https://rspcb.safety.fhwa.dot.gov/rsf/ 1. Report No. 2. Government Accession No. 3. Recipient’s Catalog No. FHWA-SA-18-003 4. Title and Subtitle 5. Report Date Road Safety Fundamentals: November 2017 Concepts, Strategies, and Practices that Reduce Fatalities and Injuries on the Road 7. Author(s) 6. Performing Organization Code Lead Editor: Daniel Carter, P.E., Senior Research Associate Unit Authors: 8. Performing Organization Report No. Unit 1: Dan Gelinne, Program Coordinator, UNC Highway Safety Research Center Unit 2: Bevan Kirley, Research Associate, UNC Highway Safety Research 9. Performing Organization Name Center and Address Unit 3: Carl Sundstrom, P.E., Research Associate, UNC Highway Safety University of North Carolina, Research Center Highway Safety Research Center Unit 4: Raghavan Srinivasan, Ph.D., Senior Transportation Research Engineer; Daniel Carter 10. -
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. -
Strategies to Improve Traffic Safety in the United States and Comments on Safety Impacts of Potential Rollback of Vehicle Efficiency Standards
Strategies to Improve Traffic Safety in the United States and Comments on Safety Impacts of Potential Rollback of Vehicle Efficiency Standards David R. Ragland, PhD, MPH Safe Transportation Research and Education Center (SafeTREC) University of California, Berkeley October 23, 2018 Professional Experience: In 2000 I founded the UC Berkeley Traffic Safety Center, now called the Safe Transportation Research and Education Center (SafeTREC), which conducts research on transportation practices, evaluates new technologies for road safety, and analyzes transportation policy (https://safetrec.berkeley.edu/). I have been the Principle Investigator on numerous projects funded at SafeTREC (more than $30M since 2000), and have authored or co-authored more than 100 technical reports and peer-reviewed publications in the traffic safety arena (SafeTREC Publications). I have also advised state and federal transportation agencies on issues of transportation safety, including collision analysis, data collection, and safety for vulnerable populations such as pedestrians and bicyclists. I also co-teach two graduate level courses: (i) Injury Prevention and Control (Injury Prevention and Control (SPH) , which examines traffic safety from a public health viewpoint, and (ii) Traffic Safety and Injury Control (Traffic Safety (CE), which investigates traffic safety from an engineering perspective. Through our courses and seminars, and via student involvement in research, we have introduced several hundred students to various aspects of traffic safety. Many are now working in transportation-related professions in universities, transportation agencies, or consulting firms. Work Performed to Reach Conclusions in the Following Comments Original analysis involving statistical modeling of the relationships between emission and fuel efficiency standards and safety is beyond the scope of this report.