DOCSLIB.ORG

Computing Systems for Autonomous Driving: State-Of-The-Art And

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Computing Systems for Autonomous Driving: State-Of-The-Art And 1 Computing Systems for Autonomous Driving: State-of-the-Art and Challenges Liangkai Liu∗, Sidi Lu∗, Ren Zhong∗, Baofu Wu∗, Yongtao Yao∗, Qingyang Zhangy, Weisong Shi∗ ∗Department of Computer Science, Wayne State University, Detroit, MI, USA, 48202 ySchool of Computer Science and Technology, Anhui University, Hefei, China, 230601 {liangkai, lu.sidi, ren.zhong, baofu.wu, yongtaoyao, weisong}@wayne.edu, [email protected] Abstract— The recent proliferation of computing technologies autonomous vehicle’s computing systems are defined to cover (e.g., sensors, computer vision, machine learning, and hardware everything (excluding the vehicle’s mechanical parts), in- acceleration), and the broad deployment of communication mech- cluding sensors, computation, communication, storage, power anisms (e.g., DSRC, C-V2X, 5G) have pushed the horizon of autonomous driving, which automates the decision and control management, and full-stack software. Plenty of algorithms of vehicles by leveraging the perception results based on multiple and systems are designed to process sensor data and make sensors. The key to the success of these autonomous systems a reliable decision in real-time. is making a reliable decision in real-time fashion. However, accidents and fatalities caused by early deployed autonomous However, news of fatalities caused by early developed vehicles arise from time to time. The real traffic environment autonomous vehicles (AVs) arises from time to time. Until is too complicated for current autonomous driving computing August 2020, five self-driving car fatalities happened for level- systems to understand and handle. In this paper, we present 2 autonomous driving: four of them from Tesla and one from state-of-the-art computing systems for autonomous driving, in- Uber [19]. Table I summarizes the date, place, company, cluding seven performance metrics and nine key technologies, followed by twelve challenges to realize autonomous driving. We and reasons for these five fatalities. The first two fatalities hope this paper will gain attention from both the computing attributed to Tesla happened in 2016 with the first accident and automotive communities and inspire more research in this occurring because neither the Autopilot system nor the driver direction. failed to recognize the truck under thick haze. The vehicle in the second incident mistook the truck for open space. In the 2018 incident involving Tesla, the autopilot failed to recognize I. INTRODUCTION the highway divider and crashed into it. The most recent Recently, with the vast improvements in computing tech- fatality from Tesla happened in 2019 because the vehicle failed nologies, e.g., sensors, computer vision, machine learning, to recognize a semi-trailer. The fatality from Uber happened hardware acceleration, and the wide deployment of commu- because the autonomous driving system failed to recognize nication mechanisms, e.g., Dedicated short-range communica- that pedestrians jaywalk. tions (DSRC), Cellular Vehicle-to-Everything (C-V2X), and 5G, autonomous driving techniques have attracted massive In summary, all four incidents associated with Tesla are due attention from both the academic and automotive communi- to perception failure, while Uber’s incident happened because ties [1], [2]. According to [3], the global autonomous driving of the failure to predict human behavior. Another fact to pay market expects to grow up to $173.15B by 2030. Many au- attention to is that currently, the field-testing of level 2 au- tomotive companies have made enormous investments in this tonomous driving vehicles mostly happens in places with good weather and light traffic conditions like Arizona and Florida. arXiv:2009.14349v3 [cs.DC] 7 Dec 2020 domain, including ArgoAI, Audi, Baidu, Cruise, Mercedes- Benz, Tesla, Uber, and Waymo, to name a few [4], [5], [6], The real traffic environment is too complicated for the current [7]. Several fleets of SAE (Society of Automotive Engineers) autonomous driving systems to understand and handle easily. L4 vehicles have been deployed around the world [8], [9]. The objectives of level 4 and level 5 autonomous driving To achieve autonomous driving, determining how to make require colossal improvement of the computing systems for the vehicle understand the environment correctly and make autonomous vehicles. safe controls in real-time is the essential task. Rich sensors This paper presents state-of-the-art computing systems for including camera, LiDAR (Light Detection and Ranging), autonomous driving, including seven performance metrics and Radar, Inertial Measurement Unit (IMU), Global Navigation nine key technologies, followed by eleven challenges and Satellite System (GNSS), and Sonar, as well as powerful opportunities to realize autonomous driving. The remaining computation devices, are installed on the vehicle [10], [11], parts of this paper are organized as follows: Section II dis- [12], [13], [14]. This design makes autonomous driving a real cusses the reference architecture of the computing systems powerful "computer on wheels." In addition to hardware, the for autonomous driving. In Section III, we show some metrics rapid development of deep learning algorithms in object/lane used in the evaluation of the computing system. Section IV dis- detection, simultaneous localization and mapping (SLAM), cusses the key technologies for autonomous driving. Section V and vehicle control also promotes the real deployment and presents possible challenges. Finally, this paper concludes in prototyping of autonomous vehicles [15], [16], [17], [18]. The Section VI. 2 TABLE I LIST OF FATALITIES CAUSED BY LEVEL 2 AUTONOMOUS DRIVING VEHICLES. Date Place Company Reason 20 Jan. 2016 Handan, Hebei China Tesla fail to recognize truck under a thick haze 07 May 2016 Williston, Florida USA Tesla mistook the truck for open sky 18 Mar. 2018 Tempe, Arizona USA Uber fail to recognize pedestrians jaywalk at night 23 Mar. 2018 Mountain View, California USA Tesla fail to recognize the highway divider 1 Mar. 2019 Delray Beach, Florida USA Tesla fail to recognize semi-trailer TABLE II Figure 1 shows a representative reference architecture of the END-TO-END APPROACHES FOR AUTONOMOUS DRIVING. computing system on autonomous vehicles. Generally, the computing system for autonomous driving vehicles can be Work Methods Characteristics divided into computation, communication, storage, security raw image to steering angles for off-road [24] supervised DNN and privacy, and power management. Each part covers four obstacle avoidance on mobile robots map an input image to a small number of layers with sensors, operating system (OS), middleware, and [25] supervised DNN key perception indicators applications. The following paragraphs will discuss the corre- CNN to map raw pixels from a camera [26] supervised DNN sponding components. directly to steering commands FCN-LSTM network to predict multi-modal For safety, one of the essential tasks is to enable the “com- [27] supervised DNN discrete and continuous driving behaviors puter” to understand the road environment and send correct automated driving framework in simulator [28] DQN control messages to the vehicle. The whole pipeline starts environment lane following in a countryside road without with the sensors. Plenty of sensors can be found on an au- [29] DQN traffic using a monocular image as input tonomous driving vehicle: camera, LiDAR, radar, GPS/GNSS, ultrasonic, inertial measurement unit (IMU), etc. These sensors capture real-time environment information for the computing II. REFERENCE ARCHITECTURE system, like the eyes of human beings. Operating system As an essential part of the whole autonomous driving (OS) plays a vital role between hardware devices (sensors, vehicle, the computing system plays a significant role in the computation, communication) and applications. Within the OS, whole pipeline of driving autonomously. There are two types drivers are bridges between the software and hardware devices; of designs for computing systems on autonomous vehicles: the network module provides the abstraction communication modular-based and end-to-end based. interface; the scheduler manages the competition to all the Modular design decouples the localization, perception, con- resources; the file system provides the abstraction to all the trol, etc. as separate modules and make it possible for people resources. For safety-critical scenarios, the operating system with different backgrounds to work together [20]. The DARPA must satisfy real-time requirements. challenges is a milestone for the prototyping of autonomous As the middle layer between applications and operating sys- driving vehicles, including Boss from CMU [21], Junior from tems [31], middleware provides usability and programmability Stanford [22], TerraMax and BRAiVE from University of to develop and improve systems more effectively. Generally, Parma [23], etc. Their designs are all based on modules middleware supports publish/subscriber, remote procedure call including perception, mission planning, motion planning, and (RPC) or service, time synchronization, and multi-sensor col- vehicle controls. Similarly, the survey fleet vehicles developed laboration. A typical example of the middleware system is the by Google and Uber are also modular-based [4], [7]. The main Robot Operating System (ROS) [32]. On top of the operating differences for these AV prototypes are the software and the system and middleware system, several applications, including configuration of sensors like camera, LiDAR,
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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:
    [Show full text]
  • 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.
    [Show full text]
  • Brand Armani Jeans Celebry Tees Rochas Roberto Cavalli Capcho
    Brand Armani Jeans Celebry Tees Rochas Roberto Cavalli Capcho Lady Million Just Over The Top Tommy Hilfiger puma TJ Maxx YEEZY Marc Jacobs British Knights ROSALIND BREITLING Polo Vicuna Morabito Loewe Alexander Wang Kenzo Redskins Little Marcel PIGUET Emu Affliction Bensimon valege Chanel Chance Swarovski RG512 ESET Omega palace Serge Pariente Alpinestars Bally Sven new balance Dolce & Gabbana Canada Goose thrasher Supreme Paco Rabanne Lacoste Remeehair Old Navy Gucci Fjallraven Zara Fendi allure bridals BLEU DE CHANEL LensCrafters Bill Blass new era Breguet Invictus 1 million Trussardi Le Coq Sportif Balenciaga CIBA VISION Kappa Alberta Ferretti miu miu Bottega Veneta 7 For All Mankind VERNEE Briston Olympea Adidas Scotch & Soda Cartier Emporio Armani Balmain Ralph Lauren Edwin Wallace H&M Kiss & Walk deus Chaumet NAKED (by URBAN DECAY) Benetton Aape paccbet Pantofola d'Oro Christian Louboutin vans Bon Bebe Ben Sherman Asfvlt Amaya Arzuaga bulgari Elecoom Rolex ASICS POLO VIDENG Zenith Babyliss Chanel Gabrielle Brian Atwood mcm Chloe Helvetica Mountain Pioneers Trez Bcbg Louis Vuitton Adriana Castro Versus (by Versace) Moschino Jack & Jones Ipanema NYX Helly Hansen Beretta Nars Lee stussy DEELUXE pigalle BOSE Skechers Moncler Japan Rags diamond supply co Tom Ford Alice And Olivia Geographical Norway Fifty Spicy Armani Exchange Roger Dubuis Enza Nucci lancel Aquascutum JBL Napapijri philipp plein Tory Burch Dior IWC Longchamp Rebecca Minkoff Birkenstock Manolo Blahnik Harley Davidson marlboro Kawasaki Bijan KYLIE anti social social club
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Opening & Welcoming Remarks
    Tuesday, April 10 – Wednesday, April 11, 2018, Washington, DC Opening & Welcoming Remarks Speaker 1 Joe Bhatia, President and CEO, ANSI Welcoming Remarks from ANSI Joe Bhatia has been president and CEO of the American National Standards Institute (ANSI) since January 2006. He previously served as executive vice president and COO of the international group at Underwriters Laboratories (UL). Mr. Bhatia serves as vice chairman of the Industry Trade Advisory Committee on Standards and Technical Trade Barriers (ITAC 16), a joint program of the U.S. Department of Commerce and U.S. Trade Representative. He is a member of the International Organization for Standardization (ISO) Council and its Council Standing Committee on Finance, and holds a seat on the Oakton Community College Education Foundation Board. In 2017 he concluded his term as president of the Pan American Standards Commission (COPANT), where he also served as vice president for four years. Speaker 2 Christoph Winterhalter, Chairman of the Executive Board of DIN Welcoming Remarks from DIN After studying computer science at the University of Karlsruhe Winterhalter started his career at ABB. After assignments in Norway, USA and Germany he took over the business units robot automation and robotics products. In 2010 he became director of the German Research Center of ABB until he was promoted global Product Group manager heading ABB’s global Machinery Controls and Automation business and later Hub Business Manager Control Technologies. Since July 2016 he is Chairman of the Executive Board of DIN. Speaker 3 Thomas Sentko, Standards Manager, International of DKE Welcoming remarks from DKE 2 Thomas studied electrical engineering/telecommunications at the University of Applied Sciences Darmstadt and graduated with the degree Dipl.Ing.
    [Show full text]
  • Athletic Bid Awards 2020-104
    DCMO BOCES COOPERATIVE PURCHASING SERVICE ATHLETIC EQUIPMENT SUPPLY & UNIFORM BID 2021-104 Prices expire April 30, 2022 Page 1 BID AWARD INFORMATION To: Participating Districts From: Beth Heinlein CPA Date: April 29, 2021 Re: Cooperative Purchasing award results for: Athletic Equipment and Supply Bid #2021-104 On April 28, 2021, the BOCES Board of Education reviewed the bid award recommendations, and approved and awarded the bids. The attached “Successful Bids” sheets are the results of the awards of the bid. These sheets list each item that was on the bid and references the awarded bidder, bid price, vendor product number. Any alternate information is found within the actual item description. This means that if an alternate brand was accepted and awarded, the alternate brand has become the item’s description for this bid cycle. If you need additional information on an item, in most cases, the item can be found on the awarded vendor’s website by using the vendor product number to conduct a search for that item number within the vendor’s online catalog. The bidder MUST provide the item to the brand and model listed within the item’s Description. Please let me know if any item is received that does not match the brands listed on the award sheets. Also attached is the “Awarded Vendor Summary” sheet. This sheet will provide you with pertinent information regarding the address, phone number, website and contact person for each awarded vendor, along with a discount being offered for additional items which were not listed on the bid. Please make sure that the appropriate bid number appears on each purchase order sent to the awarded, corresponding vendor, along with any reference numbers.
    [Show full text]
  • Navigating Automotive LIDAR Technology
    Navigating Automotive LIDAR Technology Mial Warren VP of Technology October 22, 2019 Outline • Introduction to ADAS and LIDAR for automotive use • Brief history of LIDAR for autonomous driving • Why LIDAR? • LIDAR requirements for (personal) automotive use • LIDAR technologies • VCSEL arrays for LIDAR applications • Conclusions What is the big deal? • “The automotive industry is the largest industry in the world” (~$1 Trillion) • “The automotive industry is > 100 years old, the supply chains are very mature” • “The advent of autonomy has opened the automotive supply chain to new players” (electronics, optoelectronics, high performance computing, artificial intelligence) (Quotations from 2015 by LIDAR program manager at a major European Tier 1 supplier.) LIDAR System Revenue The Automotive Supply Chain OEMs (car companies) Tier 1 Suppliers (Subsystems) Tier 2 Suppliers (components) 3 ADAS (Advanced Driver Assistance Systems) Levels SAE and NHTSA • Level 0 No automation – manual control by the driver • Level 1 One automatic control (for example: acceleration & braking) • Level 2 Automated steering and acceleration capabilities (driver is still in control) • Level 3 Environment detection – capable of automatic operation (driver expected to intervene) • Level 4 No human interaction required – still capable of manual override by driver • Level 5 Completely autonomous – no driver required Level 3 and up need the full range of sensors. The adoption of advanced sensors (incl LIDAR) will not wait for Level 5 or full autonomy! The Automotive LIDAR Market Image courtesy of Autonomous Stuff Emerging US $6 Billion LIDAR Market by 2024 (Source: Yole) ~70% automotive Note: Current market is >$300M for software test vehicles only! Sensor Fusion Approach to ADAS and Autonomous Vehicles Much of the ADAS development is driven by NHTSA regulation LIDAR Vision & Radar Radar Radar Vision Vision Vision & Radar Each technology has weaknesses and the combination of sensors provides high confidence.
    [Show full text]
  • Disc Sports 1984-08
    ~ ~' ->'.J ·· . DISC WARES UNLIMITED. INC. ' .3:;t St..::·"'cr1t., BULK RATE P.O. BOX 333 U.S. POST AGE AMHERST, MA 01004 PAID Permit No. 10 PH.'FN ~X A1.. Amherst, MA 01004 Forwarding Postage Guaranteed Address Correction Requested Plecue Fonvard If Necessary From the Editor Amid this season of exciting tour­ league; subcribe to regional newsletters; AMF VOlT INTRODUCES naments, yet more new discs on the teach a disc class in your school or park; market, and more popular recreational support clubs and player organizations; Vol. 2, No.2 disc play, Disc Sport Magazine is seeing invite media personnel to disc events; THE ULTIMATE IN ULTIMATE™ new growth, and strong support from the -sell quality discs, etc. Together, we can disc community. We have plans for con­ build flying disc sports. Publisher-: tinued expansion and improvements and The new discs on the market are a full Di sc Wares nlimited, Inc. we have new advertising support and line from Ed Headrick, the Phantom new subscribers! With this issue we have from Discraft and the A viar from Innova­ Editot-: introduced our first four-color page Champion Discs. All are for golf and/or Daryl Elliott which we hope to increase in number in distance. each issue heretofore. We also hope to In closing, I'd like for us to consider expand to 36 or 40 pages and we may the benefits of a national inter­ ub cription : even move to a glossy format! disciplinary umbrella disc organization. One year: 7.00 To successfully expand DSM, we must It could help with public relations, coor­ Two year : 12.00 increase our subscription base.
    [Show full text]
  • LIDAR – a New (Self-Driving) Vehicle for Introducing Optics to Broader Engineering and Non-Engineering Audiences Corneliu Rablau a Akettering University, Dept
    LIDAR – A new (self-driving) vehicle for introducing optics to broader engineering and non-engineering audiences Corneliu Rablau a aKettering University, Dept. of Physics, 1700 University Ave., Flint, MI USA 48504 ABSTRACT Since Stanley, the self-driven Stanford car equipped with five SICK LIDAR sensors won the 2005 DARPA Challenge, the race to developing and deploying fully autonomous, self-driving vehicles has come to a full swing. By now, it has engulfed all major automotive companies and suppliers, major trucking and taxi companies, not to mention companies like Google (Waymo), Apple and Tesla. With the notable exception of the Tesla self-driving cars, a LIDAR (Light, Detection and Ranging) unit is a key component of the suit of sensors that allow autonomous vehicles to see and navigate the world. The market space for lidar units is by now downright crowded, with a number of companies and their respective technologies jockeying for long-run leading positions in the field. Major lidar technologies for autonomous driving include mechanical scanning (spinning) lidar, MEMS micro-mirror lidar, optical-phased array lidar, flash lidar, frequency- modulated continuous-wave (FMCW) lidar and others. A major technical specification of any lidar is the operating wavelength. Many existing systems use 905 nm diode lasers, a wavelength compatible with CMOS-technology detectors. But other wavelengths (like 850 nm, 940 nm and 1550 nm) are also investigated and, in the long run, the telecom near- infrared range (1550 nm) is expected to experience significant growth because it offers a larger detecting distance range (200-300 meters) within eye safety laser power limits while also offering potential better performance in bad weather conditions.
    [Show full text]