Model Based Systems Engineering Approach to Autonomous Driving
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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. -
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: -
Information Technology and Software Development
Course Title: Information Technology and Software Development NATIONAL OPEN UNIVERSITY OF NIGERIA SCHOOL OF SCIENCE AND TECHNOLOGY COURSE CODE: CIT 703 COURSE TITLE: Information Technology and Software Development National Open University of Nigeria, Victoria Island, Lagos Page 1 Course Title: Information Technology and Software Development Course Code Course Title Information Technology and Software Development Course Developer/Writer Eze, Festus Chux Department of Computer Science Ebonyi State University Abakaliki Course Editor Programme Leader Course Coordinator National Open University of Nigeria, Victoria Island, Lagos Page 2 Course Title: Information Technology and Software Development Introduction Information Technology and Software Development is a three credit load course for all the students offering Post Graduate Diploma (PGD) in Computer Science, Information Technology and other allied courses. Software Development is a major branch in computing and information Technology. A software development professional oversees the processes of software development, the management of software development project, the maintenance of the installed software in an organisation. For sometime the field has been dominated with what is the definitive process of software development. Furthermore there has been the running battle between professionals and managers on who should control a software development project. There is an attempt to classify it as any other project that an organisation handles hence anybody could manage it. Whereas others see it as a highly professional issue that requires high precision in design, management and implementation. However, software development is all involving. It involves the user (client) whose interest is paramount. The developing organisation and her professionals ( team)are of great importance. Therefore a successful exercise can only take place when all these variegated interests are harmonised. -
APECS: Polychrony Based End-To-End Embedded System Design and Code Synthesis
APECS: Polychrony based End-to-End Embedded System Design and Code Synthesis Matthew E. Anderson Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Engineering Sandeep K. Shukla, Chair Lamine Mili Alireza Haghighat Chao Wang Yi Deng April 3, 2015 Blacksburg, Virginia Keywords: AADL, CPS, Model-based code synthesis, correct-by-construction code synthesis, Polychrony, code generators, OSATE, Ocarina Copyright 2015, Matthew E. Anderson APECS: Polychrony based End-to-End Embedded System Design and Code Synthesis Matthew E. Anderson (ABSTRACT) The development of high integrity embedded systems remains an arduous and error-prone task, despite the efforts by researchers in inventing tools and techniques for design automa- tion. Much of the problem arises from the fact that the semantics of the modeling languages for the various tools, are often distinct, and the semantics gaps are often filled manually through the engineer's understanding of one model or an abstraction. This provides an op- portunity for bugs to creep in, other than standardising software engineering errors germane to such complex system engineering. Since embedded systems applications such as avionics, automotive, or industrial automation are safety critical, it is very important to invent tools, and methodologies for safe and reliable system design. Much of the tools, and techniques deal with either the design of embedded platforms (hardware, networking, firmware etc), and software stack separately. The problem of the semantic gap between these two, as well as between models of computation used to capture semantics must be solved in order to design safer embedded systems. -
Waymo Rolls out Autonomous Vans Without Human Drivers 7 November 2017, by Tom Krisher
Waymo rolls out autonomous vans without human drivers 7 November 2017, by Tom Krisher get drowsy, distracted or drunk. Google has long stated its intent to skip driver- assist systems and go directly to fully autonomous driving. The Waymo employee in the back seat won't be able to steer the minivan, but like all passengers, will be able to press a button to bring the van safely to a stop if necessary, Waymo said. Within a "few months," the fully autonomous vans will begin carrying volunteer passengers who are now taking part in a Phoenix-area test that includes use of backup drivers. Waymo CEO John Krafcik, who was to make the In this Sunday, Jan. 8, 2017, file photo, a Chrysler announcement Tuesday at a conference in Pacifica hybrid outfitted with Waymo's suite of sensors Portugal, said the company intends to expand the and radar is shown at the North American International testing to the entire 600-square-mile Phoenix area Auto Show in Detroit. Waymo is testing vehicles on and eventually bring the technology to more cities public roads with only an employee in the back seat. The around the world. It's confident that its system can testing started Oct. 19 with an automated Chrysler Pacifica minivan in the Phoenix suburb of Chandler, Ariz. handle all situations on public roads without human It's a major step toward vehicles driving themselves intervention, he said. without human backups on public roads. (AP Photo/Paul Sancya, File) "To have a vehicle on public roads without a person at the wheel, we've built some unique safety features into this minivan," Krafcik said in remarks prepared for the conference. -
Examples of UML Diagrams
UML Diagrams Examples Examples by Technology or Application Domain Online shopping UML diagrams Ticket vending machine UML diagrams Bank ATM UML diagrams Hospital management UML diagrams Digital imaging and communications in medicine (DICOM) UML diagrams Java technology UML diagrams Application development for Android UML diagrams Software licensing and protection using SafeNet Sentinel HASP security solution Examples by Types of Diagrams Activity diagram examples Class diagram examples Communication diagram examples Component diagram examples Composite structure diagram examples Deployment diagram examples Information flow diagram example Interaction overview diagram examples Object diagram example Package diagram examples Profile diagram examples http://www.uml-diagrams.org/index-examples.html 1/15/17, 1034 AM Page 1 of 33 Sequence diagram examples State machine diagram examples Timing diagram examples Use case diagram examples Use Case Diagrams Business Use Case Diagrams Airport check-in and security screening business model Restaurant business model System Use Case Diagrams Ticket vending machine http://www.uml-diagrams.org/index-examples.html 1/15/17, 1034 AM Page 2 of 33 Bank ATM UML use case diagrams examples Point of Sales (POS) terminal e-Library online public access catalog (OPAC) http://www.uml-diagrams.org/index-examples.html 1/15/17, 1034 AM Page 3 of 33 Online shopping use case diagrams Credit card processing system Website administration http://www.uml-diagrams.org/index-examples.html 1/15/17, 1034 AM Page 4 of 33 Hospital -
A Hierarchical Control System for Autonomous Driving Towards Urban Challenges
applied sciences Article A Hierarchical Control System for Autonomous Driving towards Urban Challenges Nam Dinh Van , Muhammad Sualeh , Dohyeong Kim and Gon-Woo Kim *,† Intelligent Robotics Laboratory, Department of Control and Robot Engineering, Chungbuk National University, Cheongju-si 28644, Korea; [email protected] (N.D.V.); [email protected] (M.S.); [email protected] (D.K.) * Correspondence: [email protected] † Current Address: Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Korea. Received: 23 April 2020; Accepted: 18 May 2020; Published: 20 May 2020 Abstract: In recent years, the self-driving car technologies have been developed with many successful stories in both academia and industry. The challenge for autonomous vehicles is the requirement of operating accurately and robustly in the urban environment. This paper focuses on how to efficiently solve the hierarchical control system of a self-driving car into practice. This technique is composed of decision making, local path planning and control. An ego vehicle is navigated by global path planning with the aid of a High Definition map. Firstly, we propose the decision making for motion planning by applying a two-stage Finite State Machine to manipulate mission planning and control states. Furthermore, we implement a real-time hybrid A* algorithm with an occupancy grid map to find an efficient route for obstacle avoidance. Secondly, the local path planning is conducted to generate a safe and comfortable trajectory in unstructured scenarios. Herein, we solve an optimization problem with nonlinear constraints to optimize the sum of jerks for a smooth drive. In addition, controllers are designed by using the pure pursuit algorithm and the scheduled feedforward PI controller for lateral and longitudinal direction, respectively. -
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. -
AUTONOMOUS VEHICLES the EMERGING LANDSCAPE an Initial Perspective
AUTONOMOUS VEHICLES THE EMERGING LANDSCAPE An Initial Perspective 1 Glossary Abbreviation Definition ACC Adaptive Cruise Control - Adjusts vehicle speed to maintain safe distance from vehicle ahead ADAS Advanced Driver Assistance System - Safety technologies such as lane departure warning AEB Autonomous Emergency Braking – Detects traffic situations and ensures optimal braking AUV Autonomous Underwater Vehicle – Submarine or underwater robot not requiring operator input AV Autonomous Vehicle - vehicle capable of sensing and navigating without human input CAAC Cooperative Adaptive Cruise Control – ACC with information sharing with other vehicles and infrastructure CAV Connected and Autonomous Vehicles – Grouping of both wirelessly connected and autonomous vehicles DARPA US Defense Advanced Research Projects Agency - Responsible for the development of emerging technologies EV Electric Vehicle – Vehicle that used one or more electric motors for propulsion GVA Gross Value Added - The value of goods / services produced in an area or industry of an economy HGV Heavy Goods Vehicle – EU term for any truck with a gross combination mass over 3,500kg (same as US LGV) HMI Human Machine Interface – User interface between a vehicle and the driver / passenger IATA International Air Transport Association - Trade association of the world’s airlines LIDAR Light Detection and Ranging - Laser-based 3D scanning and sensing MaaS Mobility as a Service - Mobility solutions that are consumed as a service rather than purchased as a product ODD Operational Design -