Autonomous Cars: Past, Present and Future
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Innovation in Ecosystem Business Models: an Application to Maas and Autonomous Vehicles in Urban Mobility System
Innovation in ecosystem business models : An application to MaaS and Autonomous vehicles in urban mobility system Rodrigo Gandia To cite this version: Rodrigo Gandia. Innovation in ecosystem business models : An application to MaaS and Autonomous vehicles in urban mobility system. Economics and Finance. Université Paris-Saclay; University of Lavras, UFLA (Brésil), 2020. English. NNT : 2020UPASC018. tel-02895349 HAL Id: tel-02895349 https://tel.archives-ouvertes.fr/tel-02895349 Submitted on 9 Jul 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Innovation in Ecosystem Business Models: An Application to MaaS and Autonomous Vehicles in Urban Mobility System Thèse de doctorat de l'université Paris-Saclay École doctorale n° 573 Interfaces : approches interdisciplinaires, fon- dements, applications et innovation (Interfaces) Spécialité de doctorat : Ingénierie des systèmes complexes Unité de recherche : Université Paris-Saclay, CentraleSupélec, Laboratoire Genie Industriel, 91190, Gif-sur-Yvette, France. Référent : CentraleSupélec Thèse présentée et -
Autonomous Vehicle, Sensing and Communication Survey 1 Introduction
Autonomous Vehicle, Sensing and Communication Survey Edited and submitted by Shraga Shoval Prepared by Shlomi Hacohen and Oded Medina Department of Mechanical Engineering, Ariel University, Ariel, Israel. August 2020 Abstract Autonomous vehicles introduce dramatic changes to the way in which we travel. This technology has the potential to impact the transportation across a wide array of categories including safety, congestion, and travel behavior. In this report we review the main issues involved in autonomous driving as discussed in the literature and shed light on topics that we believe require further development. 1 Introduction The challenges in autonomous driving require a combination of many capabilities, among them: localization, motion planning, vehicle’s systems control (steering, acceleration/deceleration, signaling, etc.), road perception, prediction of other road-users behavior, awareness of dangers, etc. These capabilities have a various level of importance for various levels of driving automation. Even though the first autonomous vehicle (AV) was experimented in 1926 [104], a real-modern autonomous vehicle was first presented in 1986 by a team from Carnegie Mellon University [58]. Since 2010, many major automotive manufacturers, such as GM, Mercedes Benz, Ford, Toyota, and many more are developing AVs [90]. A demonstration of AV equipped with numerous sensors and communication systems was presented by Toyota in 2013 [10]. In 2016, Google’s AV has passed over one million kilometers. These events present a glimpse to the landmarks in the development of AV that, due to the complexity of the task, progresses slowly with considerable amount of attention to safety and reliability. AVs need a large amount of data for reliable decision making. -
Optimized Route Network Graph As Map Reference for Autonomous Cars Operating on German Autobahn
Optimized Route Network Graph as Map Reference for Autonomous Cars Operating on German Autobahn Paul Czerwionka, Miao Wang Artificial Intelligence Group Institute of Computer Science Freie Universitat¨ Berlin, Germany Abstract— This paper describes several optimization tech- mapping errors are more crucial when driving at high speeds, niques used to create an adequate route network graph for performing lane changes or overtaking maneuvers. autonomous cars as a map reference for driving on German An universal format to define such digital road maps has autobahn or similar highway tracks. We have taken the Route Network Definition File Format (RNDF) specified by DARPA been issued by the Defense Advanced Research Projects and identified multiple flaws of the RNDF for creating digital Agency (DARPA) for its autonomous vehicle competitions in maps for autonomous vehicles. Thus, we introduce various desert terrain in 2004 and 2005, and in urban environments enhancements to it to form a digital map graph called RND- in 2007. The Grand Challenges 2004 and 2005 used a route FGraph, which is well suited to map almost any urban trans- definition data format (RDDF) which consists of a list of portation infrastructure. We will also outline and show results of fast optimizations to reduce the graph size. The RNDFGraph longitudes, latitudes, speed limits, and corridor widths that has been used for path-planning and trajectory evaluation by define the course boundary. It was later updated to a route the behavior module of our two autonomous cars “Spirit of network definition file (RNDF) for the Urban Challenge Berlin” and “MadeInGermany”. We have especially tuned the including stop signs, parking spots and intersections. -
Remote and Autonomous Controlled Robotic Car Based on Arduino with Real Time Obstacle Detection and Avoidance
Universal Journal of Engineering Science 7(1): 1-7, 2019 http://www.hrpub.org DOI: 10.13189/ujes.2019.070101 Remote and Autonomous Controlled Robotic Car based on Arduino with Real Time Obstacle Detection and Avoidance Esra Yılmaz, Sibel T. Özyer* Department of Computer Engineering, Çankaya University, Turkey Copyright©2019 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License Abstract In robotic car, real time obstacle detection electronic devices. The environment can be any physical and obstacle avoidance are significant issues. In this study, environment such as military areas, airports, factories, design and implementation of a robotic car have been hospitals, shopping malls, and electronic devices can be presented with regards to hardware, software and smartphones, robots, tablets, smart clocks. These devices communication environments with real time obstacle have a wide range of applications to control, protect, image detection and obstacle avoidance. Arduino platform, and identification in the industrial process. Today, there are android application and Bluetooth technology have been hundreds of types of sensors produced by the development used to implementation of the system. In this paper, robotic of technology such as heat, pressure, obstacle recognizer, car design and application with using sensor programming human detecting. Sensors were used for lighting purposes on a platform has been presented. This robotic device has in the past, but now they are used to make life easier. been developed with the interaction of Android-based Thanks to technology in the field of electronics, incredibly device. -
Future Outlook in Commercial Vehicles
ADAS In Commercial Pulse Q2’20 Vehicles What’s inside ? 1. Activities of 4 key commercial vehicle manufacturers in ADAS and higher autonomy • Tesla, Daimler Trucks, Traton and Volvo Trucks Image: Daimler Freightliner Inspiration Truck Activities of 4 key emerging players in autonomous CVs • Embark, TuSimple, Nikola Motors, Einride 2. Regulations impacting autonomous CVs in the U.S & EU 3. Future outlook THEMES AND Themes covered in this scope Key Takeaways . Players are focusing on Level-4 technologies KEY TAKEAWAYS Activities of 4 key commercial vehicle that take over on the highways, aiming at manufacturers in ADAS & higher autonomy improving safety and gaining greater fuel o Tesla efficiency. e.g. from platooning. IN ADAS in CVs o Daimler Trucks . Collaborative business models and o Traton investments could accelerate L4-L5 capabilities o Volvo Trucks in the near future and find use cases in last-mile delivery, construction and mining areas Today, we are seeing vehicle automation quickly becoming available throughout the Activities of 4 key emerging players in commercial vehicle market with significant autonomous commercial vehicles . Players like Embark plans to skip Level 3 and go interest and investment by the players. o Embark straight to Level 4, while TuSimple has ambitious o TuSimple plans to scale up Level 4 freight network by 2021 o Nikola Motors . Hydrogen powered autonomous trucks could gain ADAS functionalities such as adaptive cruise o Einride traction and fuel the freight chain control (ACC), automatic emergency braking (AEB) and lane keeping assist (LKA) are . Currently, no federal regulation on autonomous accelerating for commercial vehicles. Truck Regulation impacting autonomous commercial vehicles in the U.S and EU trucking technology exists in the U.S. -
AUV Adaptive Sampling Methods: a Review
applied sciences Review AUV Adaptive Sampling Methods: A Review Jimin Hwang 1 , Neil Bose 2 and Shuangshuang Fan 3,* 1 Australian Maritime College, University of Tasmania, Launceston 7250, TAS, Australia; [email protected] 2 Department of Ocean and Naval Architectural Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada; [email protected] 3 School of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China * Correspondence: [email protected] Received: 16 July 2019; Accepted: 29 July 2019; Published: 2 August 2019 Abstract: Autonomous underwater vehicles (AUVs) are unmanned marine robots that have been used for a broad range of oceanographic missions. They are programmed to perform at various levels of autonomy, including autonomous behaviours and intelligent behaviours. Adaptive sampling is one class of intelligent behaviour that allows the vehicle to autonomously make decisions during a mission in response to environment changes and vehicle state changes. Having a closed-loop control architecture, an AUV can perceive the environment, interpret the data and take follow-up measures. Thus, the mission plan can be modified, sampling criteria can be adjusted, and target features can be traced. This paper presents an overview of existing adaptive sampling techniques. Included are adaptive mission uses and underlying methods for perception, interpretation and reaction to underwater phenomena in AUV operations. The potential for future research in adaptive missions is discussed. Keywords: autonomous underwater vehicle(s); maritime robotics; adaptive sampling; underwater feature tracking; in-situ sensors; sensor fusion 1. Introduction Autonomous underwater vehicles (AUVs) are unmanned marine robots. Owing to their mobility and increased ability to accommodate sensors, they have been used for a broad range of oceanographic missions, such as surveying underwater plumes and other phenomena, collecting bathymetric data and tracking oceanographic dynamic features. -
Development & Policy Forecast for Global and Chinese NEV Markets
Development & Policy Forecast for Global and Chinese NEV Markets in 2021 Invited by China EV 100, officials and experts from domestic and foreign government agencies, industry associations, research institutions and businesses attended the 7th China EV 100 Forum in January 15-17, 2021. The summary below captures the observations and insight of the speakers at the forum on the industry trend and policy forecast in the world and China in 2021. Ⅰ. 2021 Global & China Auto Market Trend 1. In 2021, the global auto market may resume growth, and the NEV boom is set to continue. 2020 saw a prevalent downturn of the auto sector in major countries due to the onslaught of COVID-19, yet the sales of NEVs witnessed a spike despite the odds, with much greater penetration in various countries. The monthly penetration of electric vehicles in Germany jumped from 7% to 20% in half a year and is expected to hit 12% in 2020, up 220% year on year; Norway reported an 80% market share of EVs in November, which is projected to exceed 70% for the whole year, topping the global ranking. Multiple consultancy firms foresee a comeback of global sales growth and a continuance of NEV boom in 2021 as coronavirus eases. 2. China's auto market as a whole is expected to remain stable in 2021, 1 with a strong boost in NEV sales. In 2020, China spearheaded global NEV market growth with record sales of 1.367 million units. The Development Research Center of the State Council expects overall auto sales to grow slightly in 2021, which ranges 0-2%. -
Using Fuzzy Logic in Automated Vehicle Control
Intelligent Transportation Systems Using Fuzzy Logic in Automated Vehicle Control José E. Naranjo, Carlos González, Ricardo García, and Teresa de Pedro, Instituto de Automática Industrial Miguel A. Sotelo, Universidad de Alcalá de Henares ntil recently, in-vehicle computing has been largely relegated to auxiliary U tasks such as regulating cabin temperature, opening doors, and monitoring Automated versions fuel, oil, and battery-charge levels. Now, however, computers are increasingly assum- of a mass-produced ing driving-related tasks in some commercial models. Among those tasks are vehicle use fuzzy logic • maintaining a reference velocity or keeping a safe nomous robots and fuzzy control systems and the distance from other vehicles, Universidad de Alcalá de Henares’s knowledge of techniques to both • improving night vision with infrared cameras, and artificial vision. (The “Intelligent-Vehicle Systems” • building maps and providing alternative routes. sidebar discusses other such projects.) We’re devel- address common oping a testbed infrastructure for vehicle driving that Still, many traffic situations remain complex and includes control-system experimentation, strategies, challenges and difficult to manage, particularly in urban settings. and sensors. All of our facilities and instruments are The driving task belongs to a class of problems that available for collaboration with other research groups incorporate human depend on underlying systems for logical reasoning in this field. So far, we’ve automated and instru- and dealing with uncertainty. So, to move vehicle mented two Citroën Berlingo mass-produced vans procedural knowledge computers beyond monitoring and into tasks related to carry out our objectives. to environment perception or driving, we must inte- into the vehicle control grate aspects of human intelligence and behaviors Automated-vehicle equipment so that vehicles can manage driving actuators in a Figure 1 shows two mass-produced electric Cit- algorithms. -
Public Support Measures for Connected and Automated Driving
Public support measures for connected and automated driving PUBLIC SUPPORT MEASURES FOR CONNECTED AND AUTOMATED DRIVING Final Report Tender No. GROW-SME-15-C-N102 within the Framework contract No. ENTR/300/PP/2013/FC Written by SPI, VTT and ECORYS May - 2017 Page | i Public support measures for connected and automated driving Europe Direct is a service to help you find answers to your questions about the European Union. Freephone number (*): 00 800 6 7 8 9 10 11 (*) The information given is free, as are most calls (though some operators, phone boxes or hotels may charge you). Reference No. GROW-SME-15-C-N102 Framework contract No. ENTR/300/PP/2013/FC 300/PP/2013/FC The information and views set out in this study are those of the authors and do not necessarily reflect the official opinion of the European Commission. The European Commission does not guarantee the accuracy of the data included in this study. Neither the European Commission nor any person acting on the European Commission’s behalf may be held responsible for the use which may be made of the information contained herein. More information on the European Union is available on the Internet (http://www.europa.eu). ISBN 978-92-9202-254-9 doi: 10.2826/083361 AUTHORS OF THE STUDY Sociedade Portuguesa de Inovação (SPI – Coordinator): Augusto Medina, Audry Maulana, Douglas Thompson, Nishant Shandilya and Samuel Almeida. VTT Technical Research Centre of Finland (VTT): Aki Aapaoja and Matti Kutila. ECORYS: Erik Merkus and Koen Vervoort Page | ii Public support measures for connected and automated driving Contents Acronym Glossary ................................................................................................ -
ADVANCED DRIVER ASSISTANCE SYSTEMS (ADAS) JUNE 2019 a Guide to Advanced Driver Assistance Systems
ADVANCED DRIVER ASSISTANCE SYSTEMS (ADAS) JUNE 2019 A Guide to Advanced Driver Assistance Systems As technology becomes more advanced, a growing number of vehicles are being built with intelligent systems to help motorists. Advanced Driver Assistance Systems, or ADAS, is a term used to describe these smart features. ADAS includes relatively simple features like rear view cameras to help with parking through to more complicated systems like Lane Departure Warning (LDW) that can detect a vehicle’s surroundings. These advanced systems can actually take some control of the vehicle, such as Autonomous Emergency Braking (AEB). In this guide, we describe the ADAS technology available and their benefits, which could be useful when you and your drivers are selecting your next vehicle. What is ADAS? Whether your car has adaptive high beams, a collision detection system or autonomous night vision, these are all classed as Advanced Driver Assistance Systems (ADAS). If you and your drivers understand what these smart features are and what they do, you can get the most benefit from them, improving your driving experience and making other road users safer. Please be aware that the information in this guide is correct as at June 2019 but things move fast in this area. 1 A guide to advanced driver assistance systems Light Assisted Technology AFLS - Adaptive Front Lighting System System that automatically turns the headlight beam to the right or left dependent on the vehicle’s direction. AHBC – Adaptive High Beam Control ALC - Adaptive Light Control Detects oncoming traffic and vehicles in front, automatically adjusting the headlamp beam high and low. -
State of the Art of Adaptive Cruise Control and Stop and Go Systems
1st AUTOCOM Workshop on Preventive and Active Safety Systems for Road Vehicles State of the Art of Adaptive Cruise Control and Stop and Go Systems Emre Kural, Tahsin Hacıbekir, and Bilin Aksun Güvenç Department of Mechanical Engineering, østanbul Technical University, Gümüúsuyu, Taksim, østanbul, TR-34437, Turkey Abstract—This paper presents the state of the art of Number of Traffic Accidents in European Country Adaptive Cruise Control (ACC) and Stop and Go systems as x1000 well as Intelligent Transportation Systems enhanced with inter vehicle communication. The sensors used in these 400 systems and the level of their current technology are introduced. Simulators related to ACC and Stop and Go 300 (S&G) systems are also surveyed and the MEKAR 200 simulator is presented. Finally, future trends of ACC and Stop and Go systems and their advantages are emphasized. 100 0 I. INTRODUCTION y in e ly K y n a c a U e a n It rk a u New trends in automotive technology result in new rm Sp r e F T systems to produce safer and more comfortable vehicles. G Many driver assistant systems are introduced by automotive manufacturers in order to prevent a possible Figure 1. Traffic Accident Statistics in Europe accident from happening either by intervening in the And as a result, the driver’s routine interventions are control of the vehicle temporarily or by warning the lessened and due to a more controlled driving, fuel driver audibly and visually. consumption will be reduced. On the other hand, the The key reason as to why researchers focus on the automation of highways will also allow an increase in the subject of producing safer cars is related to the statistics number of vehicles traveling on roads without causing that expose the serious consequences of accidents. -
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.