Autonomous Vehicles
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The Definitive Guide to Data-Driven Attribution Explore Marketing Performance Measurement Best Practices
WHITE PAPER | Google Attribution 360 The definitive guide to data-driven attribution Explore marketing performance measurement best practices Google Inc. [email protected] g.co/360suite The definitive guide to data-driven attribution 1 WHITE PAPER | Google Attribution 360 Opportunities for Growth Data-driven attribution helps marketers know more and guess less. If your organization is still focused on crediting only the last touch point for your marketing success, you may be leaving up to 20%-40% of potential return on investment (ROI) on the table.1 This marketing performance measurement best practice offers an unprecedented level of visibility into the customer journey. It helps marketers make fact-based decisions, 5xTop-performing marketing organizations are five times more gain efficiencies, and realize greater returns on marketing investments. likely to use advanced attribution. This introduction to data-driven attribution will explain how and if data-driven attribution tools can help you move your marketing forward. We’ll cover the questions attribution 54% can help answer, how to find the right tool, and tips and tricks on getting started with By contrast, 54% of marketer still a data-driven attribution program. credit the last-click, only. What is attribution? 20–30% Summary: Assumptions about how marketing activities impact customers lead to a Decrease in effective display and retargeting CPA. misinformed marketing strategy. Data-driven attribution reveals the real path-to-purchase, allowing you to fine-tune strategies based on real customer behavior. 25–50% Attribution is the practice of tracking and valuing all marketing touch points that lead to Decrease in level of effort to pull, aggregate, and distribute a desired outcome. -
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. -
Learning Maps for Indoor Mobile Robot Navigation
Learning Maps for Indoor Mobile Robot Navigation Sebastian Thrun Arno BÈucken April 1996 CMU-CS-96-121 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 The ®rst author is partly and the second author exclusively af®liated with the Computer Science Department III of the University of Bonn, Germany, where part of this research was carried out. This research is sponsored in part by the National Science Foundation under award IRI-9313367, and by the Wright Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Advanced Research Projects Agency (ARPA) under grant number F33615-93-1-1330. The views and conclusionscontained in this documentare those of the author and should not be interpreted as necessarily representing of®cial policies or endorsements, either expressed or implied, of NSF, Wright Laboratory or the United States Government. Keywords: autonomous robots, exploration, mobile robots, neural networks, occupancy grids, path planning, planning, robot mapping, topological maps Abstract Autonomous robots must be able to learn and maintain models of their environ- ments. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits ef®cient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more ef®ciently, yet accurate and consistent topological maps are considerably dif®cult to learn in large-scale envi- ronments. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using arti®cial neural networks and Bayesian integration. -
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 . -
Paving the Way for Self-Driving Vehicles”
June 13, 2017 The Honorable John Thune, Chairman The Honorable Bill Nelson, Ranking Member U.S. Senate Committee on Commerce, Science & Transportation 512 Dirksen Senate Office Building Washington, DC 20510 RE: Hearing on “Paving the Way for Self-Driving Vehicles” Dear Chairman Thune and Ranking Member Nelson: We write to your regarding the upcoming hearing “Paving the Way for Self-Driving Vehicles,”1 on the privacy and safety risks of connected and autonomous vehicles. For more than a decade, the Electronic Privacy Information Center (“EPIC”) has warned federal agencies and Congress about the growing risks to privacy resulting from the increasing collection and use of personal data concerning the operation of motor vehicles.2 EPIC was established in 1994 to focus public attention on emerging privacy and civil liberties issues. EPIC engages in a wide range of public policy and litigation activities. EPIC testified before the House of Representatives in 2015 on “the Internet of Cars.”3 Recently, EPIC 1 Paving the Way for Self-Driving Vehicles, 115th Cong. (2017), S. Comm. on Commerce, Science, and Transportation, https://www.commerce.senate.gov/public/index.cfm/pressreleases?ID=B7164253-4A43- 4B70-8A73-68BFFE9EAD1A (June 14, 2017). 2 See generally EPIC, “Automobile Event Data Recorders (Black Boxes) and Privacy,” https://epic.org/privacy/edrs/. See also EPIC, Comments, Docket No. NHTSA-2002-13546 (Feb. 28, 2003), available at https://epic.org/privacy/drivers/edr_comments.pdf (“There need to be clear guidelines for how the data can be accessed and processed by third parties following the use limitation and openness or transparency principles.”); EPIC, Comments on Federal Motor Vehicle Safety Standards; V2V Communications, Docket No. -
Autonomous Vehicles on the Road from the Perspective of a Manufacturer's Liability for Damages
Autonomous Vehicles on the Road from the Perspective of a Manufacturer's Liability for Damages IUC International Maritime and Transport Law Course International Maritime and Transport Law – Transport Law de Lege Ferenda Dubrovnik, 9 September 2020 Contents 1. Introduction - definitions, perspective 2. Challenges - current set of rules; AV-AV, AV-CV, AV-rest 3. Opportunities - vision for future 4. Summary Conclusion Is there a need for more regulation in order to help the production of AVs and mitigate damages? 1. AVs to be treated as conventional vehicles, i.e. as any other product, movable 2. Treat them as elevators/lifts or as autopilot technology 3. New legal framework Current legal framework • National regulations • EU strategies/investments • Independent bodies/entities and their recommendations • Good practices What is an AV? • a vehicle enabled with technology that has the capability of operating or driving the vehicle without the active control or monitoring of a natural person - Maurice Schellekens, Self-driving cars and the chilling effect of liability law • 3 elements: 1. Means (AI or similar technology) 2. Purpose of the means 3. Way of operating the means (active control or monitoring of a human person) • AV as any other movable Waymo’s self driving car, 2020 Source: https://www.google.com/search?q=Waymo&rlz=1C1GCEA_enHR912HR912&sxsrf=ALeKk00G8P_y2Ik0neIaDNxJlqcjKMQBlw:1599407756496&source=lnms&tbm=isch&sa=X&ved=2ahUKEwjR67SZ8tTrAhXSTcAKHZATCDQ Q_AUoAXoECBgQAw&biw=1366&bih=657#imgrc=z71dtWbxBXnwgM Tesla model 3, 2020 Source: -
Model Based Systems Engineering Approach to Autonomous Driving
DEGREE PROJECT IN ELECTRICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 Model Based Systems Engineering Approach to Autonomous Driving Application of SysML for trajectory planning of autonomous vehicle SARANGI VEERMANI LEKAMANI KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Author Sarangi Veeramani Lekamani [email protected] School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Place for Project Sodertalje, Sweden AVL MTC AB Examiner Ingo Sander School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Supervisor George Ungureanu School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Industrial Supervisor Hakan Sahin AVL MTC AB Abstract Model Based Systems Engineering (MBSE) approach aims at implementing various processes of Systems Engineering (SE) through diagrams that provide different perspectives of the same underlying system. This approach provides a basis that helps develop a complex system in a systematic manner. Thus, this thesis aims at deriving a system model through this approach for the purpose of autonomous driving, specifically focusing on developing the subsystem responsible for generating a feasible trajectory for a miniature vehicle, called AutoCar, to enable it to move towards a goal. The report provides a background on MBSE and System Modeling Language (SysML) which is used for modelling the system. With this background, an MBSE framework for AutoCar is derived and the overall system design is explained. This report further explains the concepts involved in autonomous trajectory planning followed by an introduction to Robot Operating System (ROS) and its application for trajectory planning of the system. The report concludes with a detailed analysis on the benefits of using this approach for developing a system. -
New Action Thriller by Alex Schuler Takes on the Controversial And
FOR IMMEDIATE RELEASE Publication Date: 8/17/2021 Alyssia Gonzalez Trade Paper ISBN: 9781933769844 ($18.95) Book Publicist eBook ISBN: 9781933769851 ($9.99) [email protected] New Action Thriller by Alex Schuler Takes on the Controversial and Dramatic History of Self-Driving Cars Level 4 Press presents a fictionalized retelling of how the autonomous car came to be. JAMUL, CA (FEB 2020) — Author Alex Schuler captures intense determination and a clash of egos in his new action thriller, Faster. It follows a fictional engineer and computer scientist who helped pioneer the self-driving car industry while navigating their own tumultuous relationship. Their drive to take on Detroit and become leaders in this technological revolution leaves the reader in awe of what humanity can accomplish, and just how much sacrifice it takes to change the world. Autonomous car breakthroughs spiked during the DARPA challenges between 2004 and 2013, giving names to eccentric computer scientists and engineers drawn to innovation. These remarkable individuals often find themselves embroiled in controversy as they forge new paths. Robotics wonderkid Sebastian Thrun is regularly in the news for his “internet freedom” crusades. Elon Musk is legendary for breaking norms, such as the Tesla autopilot feature rushed to market in 2015. Autonomous vehicle guru Anthony Levandowski took on Google, then found himself sentenced to jail, only to be pardoned by former president Donald Trump. These pioneers dedicated their lives to technological progress. But what’s behind their drive—self-interest or a desire to improve society? Or some of both? Alex Schuler fictionalizes the autonomous car industry’s past, exploring the motivations of these innovators and asking what we can expect for the future. -
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. -
Choosing the Right Digital Device
Choosing the Right Digital Device Image: Core Education This guide will provide you with information that will help you as you decide which devices to choose for your school. It is important to consider not just the devices but how your students and teachers use them and which device is best for which sorts of activities for effective teaching and learning. It will also be useful for schools providing advice to parents about which type of device to purchase for a BYOD environment. This guide is intended for School Leaders, e-learning leaders and technical support staff. It is not necessary to have a background in technology but it is important to have a good pedagogical understanding of why you want to use digital devices and what your learning outcomes are. Overall, to make a choice, you should: 1 - Be clear about purpose 2 - Weigh up options and make decisions 3 - Implement, support and evaluate Choosing the right digital device 1 of 15 Contents Are you clear about the purpose? What features do you need? Health considerations How will you implement, support and evaluate ? Useful Links Appendix - Shared Use of Devices Once you have read this guide you are welcome to contact the Connected Learning Advisory to get more personal assistance. We aim to provide consistent, unbiased advice and are free of charge to all state and state-integrated New Zealand schools and kura. Our advisors can help with all aspects outlined in this guide as well as provide peer review of the decisions you reach before you take your next steps. -
Maximizing Conversion Value with Marketing Analytics and Machine Learning a Braintrust Insights Case Study
Maximizing Conversion Value with Marketing Analytics and Machine Learning A BrainTrust Insights Case Study Executive Summary The promise of digital marketing and the democratization of publishing tools was to put all brands on equal footing. The brands with the most skill at digital marketing and content creation would win, regardless of size. As advertising crept into the Internet, larger companies seized the advantage with big budgets and resources. However, the introduction of machine learning technology is leveling the playing field once more. Bigger brands, plagued with inertia and aging infrastructure, are not able to adopt new technologies as quickly, creating an opening for more nimble companies to use machine learning to seize market share. With machine learning, attribution analysis is more precise and nuanced; brands will understand the true impact of digital marketing channels, even with dozens or hundreds of steps leading to a conversion. Once a brand understands what truly drives business impact, it can extend its advantage using machine learning and predictive analytics to forecast likely business outcomes. Predictive forecasts give us a starting point, a roadmap for planning that’s truly data-driven. Instead of guessing when things are likely to happen in general, predictive analytics give us specificity, granularity to allocate budget and resources with precision. Nimble brands spend only when they need to, only when they are likely to generate outsized returns. Finally, predictive analytics and machine learning help us to understand what will be on the minds of our customers and when, allowing us to create strategies, tactics, and plans of execution that astonish and delight customers. -
Supplementary Submission Intellectual Property And
MAY 2017 HOUSE OF REPRESENTATIVES STANDING COMMITTEE ON INDUSTRY, INNOVATION, SCIENCE AND RESOURCES SUPPLEMENTARY SUBMISSION INTELLECTUAL PROPERTY AND SELF-DRIVING CARS: WAYMO VS UBER DR MATTHEW RIMMER PROFESSOR OF INTELLECTUAL PROPERTY AND INNOVATION LAW FACULTY OF LAW QUEENSLAND UNIVERSITY OF TECHNOLOGY Queensland University of Technology 2 George Street GPO Box 2434 Brisbane Queensland 4001 Australia Work Telephone Number: SUPPLEMENTARY SUBMISSION INTELLECTUAL PROPERTY AND SELF-DRIVING CARS: WAYMO VS UBER Matthew Rimmer While the Australian Parliament has been inquiring into social issues relating to land-based driverless vehicles in Australia since February 2017, intellectual property litigation has erupted in the courts between Waymo (Google’s Self-Driving Car Project) and Uber in the United States. The case has attracted much public attention. Alex Davies has reflected: Until today, the race to build a self-driving car seemed to hinge on who had the best technology. Now it’s become a case of full-blown corporate intrigue. Alphabet’s self-driving startup, Waymo, is suing Uber, accusing the ridesharing giant of stealing critical autonomous driving technology. If the suit goes to trial, Apple’s legal battle with Samsung could wind up looking tame by comparison.1 The intellectual property dispute could have significant implications for competition in respect of self-driving cars and autonomous vehicles. The New York Times has noted that ‘companies in Silicon Valley and Detroit are betting big on self-driving car technology’ and ‘the