Autonomous & Connected Vehicle Report
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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 . -
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. -
AN ADVANCED VISION SYSTEM for GROUND VEHICLES Ernst
AN ADVANCED VISION SYSTEM FOR GROUND VEHICLES Ernst Dieter Dickmanns UniBw Munich, LRT, Institut fuer Systemdynamik und Flugmechanik D-85577 Neubiberg, Germany ABSTRACT where is it relative to me and how does it move? (VT2) 3. What is the likely future motion and (for subjects) intention of the object/subject tracked? (VT3). These ‘Expectation-based, Multi-focal, Saccadic’ (EMS) vision vision tasks have to be solved by different methods and on has been developed over the last six years based on the 4- different time scales in order to be efficient. Also the D approach to dynamic vision. It is conceived around a fields of view required for answering the first two ‘Multi-focal, active / reactive Vehicle Eye’ (MarVEye) questions are quite different. Question 3 may be answered with active gaze control for a set of three to four more efficiently by building on the results of many conventional TV-cameras mounted fix relative to each specific processes answering question 2, than by resorting other on a pointing platform. This arrangement allows to image data directly. both a wide simultaneous field of view (> ~ 100°) with a Vision systems for a wider spectrum of tasks in central region of overlap for stereo interpretation and high ground vehicle guidance have been addresses by a few resolution in a central ‘foveal’ field of view from one or groups only. The Robotics Institute of Carnegie Mellon two tele-cameras. Perceptual and behavioral capabilities University (CMU) [1-6] and DaimlerChrysler Research in are now explicitly represented in the system for improved Stuttgart/Ulm [7-12] (together with several university flexibility and growth potential. -
An Introduction of Autonomous Vehicles and a Brief Survey
Journal of Critical Reviews ISSN- 2394-5125 Vol 7, Issue 13, 2020 AN INTRODUCTION OF AUTONOMOUS VEHICLES AND A BRIEF SURVEY 1Tirumalapudi Raviteja, 2Rajay Vedaraj I.S 1Research Scholar, School of Mechanical Engineering, Vellore instutite of technology, Tamilnadu, India, Email: [email protected] . 2Professor, School of Mechanical Engineering, Vellore instutite of technology, Tamilnadu, India, Email: [email protected] Received: 09.04.2020 Revised: 10.05.2020 Accepted: 06.06.2020 Abstract: An autonomous car is also called a self-driving car or driverless car or robotic car. Whatever the name but the aim of the technology is the same. Down the memory line, autonomous vehicle technology experiments started in 1920 only and controlled by radio technology. Later on, trails began in 1950. From the past few years, updating automation technology day by day and using all aspects of using in regular human life. The present scenario of human beings is addicted to automation and robotics technology using like agriculture, medical, transportation, automobile and manufacturing industries, IT sector, etc. For the last ten years, the automobile industry came forward to researching autonomous vehicle technology (Waymo Google, Uber, Tesla, Renault, Toyota, Audi, Volvo, Mercedes-Benz, General Motors, Nissan, Bosch, and Continental's autonomous vehicle, etc.). Level-3 Autonomous cars came out in 2020. Everyday autonomous vehicle technology researchers are solving challenges. In the future, without human help, robots will manufacture autonomous cars using IoT technology based on customer requirements and prefer these vehicles are very safe and comfortable in transportation systems like human traveling or cargo. Autonomous vehicles need data and updating continuously, so in this case, IoT and Artificial intelligence help to share the information device to the device. -
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. -
Introducing Driverless Cars to UK Roads
Introducing Driverless Cars to UK Roads WORK PACKAGE 5.1 Deliverable D1 Understanding the Socioeconomic Adoption Scenarios for Autonomous Vehicles: A Literature Review Ben Clark Graham Parkhurst Miriam Ricci June 2016 Preferred Citation: Clark, B., Parkhurst, G. and Ricci, M. (2016) Understanding the Socioeconomic Adoption Scenarios for Autonomous Vehicles: A Literature Review. Project Report. University of the West of England, Bristol. Available from: http://eprints.uwe.ac.uk/29134 Centre for Transport & Society Department of Geography and Environmental Management University of the West of England Bristol BS16 1QY UK Email enquiries to [email protected] VENTURER: Introducing driverless cars to UK roads Contents 1 INTRODUCTION .............................................................................................................................................. 2 2 A HISTORY OF AUTONOMOUS VEHICLES ................................................................................................ 2 3 THEORETICAL PERSPECTIVES ON THE ADOPTION OF AVS ............................................................... 4 3.1 THE MULTI-LEVEL PERSPECTIVE AND SOCIO-TECHNICAL TRANSITIONS ............................................................ 4 3.2 THE TECHNOLOGY ACCEPTANCE MODEL ........................................................................................................ 8 3.3 SUMMARY ................................................................................................................................................... -
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. -
A Study on Tesla Autopilot
INTERNATIONAL JOURNAL OF SCIENTIFIC PROGRESS AND RESEARCH (IJSPR) ISSN: 2349-4689 Issue 171, Volume 71, Number 01, May 2020 A Study on Tesla Autopilot K. Nived Maanyu1, D Goutham Raj2, R Vamsi Krishna3, Dr. Shruthi Bhargava Choubey4 Dept. of ECE, Sreenidhi Institute of Science And Technology, Hyderabad, India Abstract— Tesla Autopilot may well be a refined driver- Enhanced Autopilot has the following partial self- assistance system feature offered by Tesla that has lane driving abilities: automatically change lanes without centering, adaptative controller, self-parking, the power to requiring driver input, the transition from one freeway to mechanically modification lanes, and jointly the flexibleness to another, exit the freeway when your destination is near and summon the automobile to and from a garage or parking spot. more.[21] As an upgrade to the base Autopilot's capabilities, the company's stated intent is to offer full self-driving (FSD) at a Autopilot for HW2 cars came in February 2017. It future time, acknowledging that legal, regulatory, and technical enclosed an adaptative controller, autosteer on divided [1] hurdles must be overcome to achieve this goal. highways, autosteer on 'local roads’ up to a speed of 35 I. INTRODUCTION mph or a specified number of mph over the local speed limit to a maximum of 45 mph.[22] Firmware version 8.1 Autopilot was 1st offered on Oct nine, 2014, for Tesla for HW2 arrived in June 2017 adding a new driving-assist Model S, followed by the Model X upon its release.[3] algorithm, full-speed braking and handling parallel and Autopilot was included within a "Tech Package" option. -
Mechatronics Design of an Unmanned Ground Vehicle for Military Applications 237
Mechatronics Design of an Unmanned Ground Vehicle for Military Applications 237 Mechatronics Design of an Unmanned Ground Vehicle for Military 15X Applications Pekka Appelqvist, Jere Knuuttila and Juhana Ahtiainen Mechatronics Design of an Unmanned Ground Vehicle for Military Applications Pekka Appelqvist, Jere Knuuttila and Juhana Ahtiainen Helsinki University of Technology Finland 1. Introduction In this chapter the development of an Unmanned Ground Vehicle (UGV) for task-oriented military applications is described. The instrumentation and software architecture of the vehicle platform, communication links, remote control station, and human-machine interface are presented along with observations from various field tests. Communication delay and usability tests are addressed as well. The research was conducted as a part of the FinUVS (Finnish Unmanned Vehicle Systems) technology program for the Finnish Defense Forces. The consortium responsible for this project included both industry and research institutions. Since the primary interest of the project was in the tactical utilization scenarios of the UGV and the available resources were relatively limited, the implementation of the vehicle control and navigation systems was tried to be kept as light and cost-effective as possible. The resources were focused on developing systems level solutions. The UGV was developed as a technology demonstrator using mainly affordable COTS components to perform as a platform for the conceptual testing of the UGV system with various types of payloads and missions. Therefore, the research methodology was rather experimental by nature, combining complex systems engineering and mechatronics design, trying to find feasible solutions. The ultimate goal of the research and development work was set to achieve high level of autonomy, i.e., to be able to realistically demonstrate capability potential of the system with the given mission.