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321	Placement of Controls in Construction Equipment Using Operators´Sitting Postures : Process and Recommendations Jalkebo, Charlotte January 2014 (has links) An ergonomically designed work environment may decrease work related musculoskeletal disorders, lead to less sick leaves and increase production time for operators and companies all around the world. Volvo Construction Equipment wants to deepen the knowledge and investigate more carefully how operators are actually sitting whilst operating the machines, how this affects placement of controls and furthermore optimize controls placements accordingly. The purpose is to enhance their product development process by suggesting guidelines for control placement with improved ergonomics based on operators’ sitting postures. The goal is to deliver a process which identifies and transfers sitting postures to RAMSIS and uses them for control placement recommendations in the cab and operator environments. Delimitations concerns: physical ergonomics, 80% usability of the resulted process on the machine types, and the level of detail for controls and their placements. Research, analysis, interviews, test driving of machines, video recordings of operators and the ergonomic software RAMSIS has served as base for analysis. The analysis led to (i) the conclusion that sitting postures affect optimal ergonomic placement of controls, though not ISO-standards, (ii) the conclusion that RAMSIS heavy truck postures does not seem to correspond to Volvo CE’s operators’ sitting postures and (iii) and to an advanced engineering project process suitable for all machine types and applicable in the product development process. The result can also be used for other machines than construction equipment. The resulted process consists of three independent sub-processes with step by step explanations and recommendations of; (i) what information that needs to be gathered, (ii) how to identify and transfer sitting postures into RAMSIS, (iii) how to use RAMSIS to create e design aid for recommended control placement. The thesis also contains additional enhancements to Volvo CE’s product development process with focus on ergonomics. A conclusion is that the use of motion capture could not be verified to work for Volvo Construction Equipment, though it was verified that if motion capture works, the process works. Another conclusion is that the suggested body landmarks not could be verified that they are all needed for this purpose except for those needed for control placement. Though they are based on previous sitting posture identification in vehicles and only those that also occur in RAMSIS are recommended, and therefore they can be used. This thesis also questions the most important parameters for interior vehicle design (hip- and eye locations) and suggests that shoulder locations are just as important. The thesis concluded five parameters for control categorization, and added seven categories in addition to those mentioned in the ISO-standards. Other contradictions and loopholes in the ISO-standards were identified, highlighted and discussed. Suggestions for improving the ergonomic analyses in RAMSIS can also be found in this report. More future research mentioned is more details on control placement as well as research regarding sitting postures are suggested. If the resulted process is delimited to concern upper body postures, other methods for posture identification may be used. Motion capture controls placement posture identification construction equipment operator environment volvo ce depth sensor inertial optical zone of reach zone of comfort H-point SIP Seat index point product development ergonomics Work Related Musculoskeletal Disorders Human Machine Interaction HMI WRMD Anthropometry Catia RAMSIS GEometric Kinematic backhoe loader wheel loader articulated hauler excavator Faro-Arm CMM Skeleton points skin points sitting posture operating posture categorization V model visual fields percentile road condition skill level sight point Abrasion speed visibility FOV field of view primary secondary tertiary ZoC ZoR mid-eye Design aid safety heavy truck
322	Modeling, Simulation, and Injection of Camera Images/Video to Automotive Embedded ECU : Image Injection Solution for Hardware-in-the-Loop Testing Lind, Anton January 2023 (has links) Testing, verification and validation of sensors, components and systems is vital in the early-stage development of new cars with computer-in-the-car architecture. This can be done with the help of the existing technique, hardware-in-the-loop (HIL) testing which, in the close loop testing case, consists of four main parts: Real-Time Simulation Platform, Sensor Simulation PC, Interface Unit (IU), and unit under test which is, for instance, a Vehicle Computing Unit (VCU). The purpose of this degree project is to research and develop a proof of concept for in-house development of an image injection solution (IIS) on the IU in the HIL testing environment. A proof of concept could confirm that editing, customizing, and having full control of the IU is a possibility. This project was initiated by Volvo Cars to optimize the use of the HIL testing environment currently available, making the environment more changeable and controllable while the IIS remains a static system. The IU is an MPSoC/FPGA based design that uses primarily Xilinx hardware and software (Vivado/Vitis) to achieve the necessary requirements for image injection in the HIL testing environment. It consists of three stages in series: input, image processing, and output. The whole project was divided in three parts based on the three stages and carried out at Volvo Cars in cooperation by three students, respectively. The author of this thesis was responsible for the output stage, where the main goal was to find a solution for converting, preferably, AXI4 RAW12 image data into data on CSI2 format. This CSI2 data can then be used as input to serializers, which in turn transmit the data via fiber-optic cable on GMSL2 format to the VCU. Associated with the output stage, extensive simulations and hardware tests have been done on a preliminary solution that partially worked on the hardware, producing signals in parts of the design that could be read and analyzed. However, a final definite solution that fully functions on the hardware has not been found, because the work is at the initial phase of an advanced and very complex project. Presented in this thesis is: important theory regarding, for example, protocols CSI2, AXI4, GMSL2, etc., appropriate hardware selection for an IIS in HIL (FPGA, MPSoC, FMC, etc.), simulations of AXI4 and CSI2 signals, comparisons of those simulations with the hardware signals of an implemented design, and more. The outcome was heavily dependent on getting a certain hardware (TEF0010) to transmit the GMSL2 data. Since the wrong card was provided, this was the main problem that hindered the thesis from reaching a fully functioning implementation. However, these results provide a solid foundation for future work related to image injection in a HIL environment. ADAS AD ADS HIL Hardware in the loop Hardware-in-the-loop ECU VCU Automotive Embedded System Systems Camera Image Video Injection FPGA MPSoC Vivado Vitis VHDL Volvo Cars FMC HPC LPC MIPI CSI2 GMSL2 AMBA AXI4 Xilinx RTL Implementation Synthesis Intelectual Property IP Vehicle computing unit Electronic control unit TEB0911 TEF0007 TEF0010 CSI2 Tx CSI2 Tx Subsystem Zynq SerDes AXI4 AXI4-Lite Programmable Logic PL Processor System PS C C++ Video test pattern generator VTPG Axi traffic generator ATG Ultrascale+ Virtual input/output VIO Integrated logic analyzer ILA Interface Unit Embedded Systems Inbäddad systemteknik
323	Dynamic Speed Adaptation for Curves using Machine Learning / Dynamisk hastighetsanpassning för kurvor med maskininlärning Narmack, Kirilll January 2018 (has links) The vehicles of tomorrow will be more sophisticated, intelligent and safe than the vehicles of today. The future is leaning towards fully autonomous vehicles. This degree project provides a data driven solution for a speed adaptation system that can be used to compute a vehicle speed for curves, suitable for the underlying driving style of the driver, road properties and weather conditions. A speed adaptation system for curves aims to compute a vehicle speed suitable for curves that can be used in Advanced Driver Assistance Systems (ADAS) or in Autonomous Driving (AD) applications. This degree project was carried out at Volvo Car Corporation. Literature in the field of speed adaptation systems and factors affecting the vehicle speed in curves was reviewed. Naturalistic driving data was both collected by driving and extracted from Volvo's data base and further processed. A novel speed adaptation system for curves was invented, implemented and evaluated. This speed adaptation system is able to compute a vehicle speed suitable for the underlying driving style of the driver, road properties and weather conditions. Two different artificial neural networks and two mathematical models were used to compute the desired vehicle speed in curves. These methods were compared and evaluated. / Morgondagens fordon kommer att vara mer sofistikerade, intelligenta och säkra än dagens fordon. Framtiden lutar mot fullständigt autonoma fordon. Detta examensarbete tillhandahåller en datadriven lösning för ett hastighetsanpassningssystem som kan beräkna ett fordons hastighet i kurvor som är lämpligt för förarens körstil, vägens egenskaper och rådande väder. Ett hastighetsanpassningssystem för kurvor har som mål att beräkna en fordonshastighet för kurvor som kan användas i Advanced Driver Assistance Systems (ADAS) eller Autonomous Driving (AD) applikationer. Detta examensarbete utfördes på Volvo Car Corporation. Litteratur kring hastighetsanpassningssystem samt faktorer som påverkar ett fordons hastighet i kurvor studerades. Naturalistisk bilkörningsdata samlades genom att köra bil samt extraherades från Volvos databas och bearbetades. Ett nytt hastighetsanpassningssystem uppfanns, implementerades samt utvärderades. Hastighetsanpassningssystemet visade sig vara kapabelt till att beräkna en lämplig fordonshastighet för förarens körstil under rådande väderförhållanden och vägens egenskaper. Två olika artificiella neuronnätverk samt två matematiska modeller användes för att beräkna fordonets hastighet. Dessa metoder jämfördes och utvärderades. Machine Learning Artificial Intelligence MachineLearning Machine-Learning AI ML MLP Neural Networks Multilayer-Perceptron RFB RBF-Network Vehicle Automation AutonomousVehicles Autonomous SelfDriving Self-Driving Self Driving Curve Speed Adaptation Adaption CSA ACC Adaptive Cruise Control Driving Style Driver Behavior Behaviour Sample Samples Distance Derivative Speed Velocity Curvature Road Inclination Lane Width Type Acceleration Longitudinal Lateral Data Training Test Validation Results Discussion Sustainability Ethics Ethical Sustainable Future Today Tomorrow Yesterday Robot Robotics System Systems Class Classes Bin Bins Tree One A Dynamic Using Time Delay Volvo Car Cars Corporation Zenuity School Computer Science Master Degree Project Thesis Paper Object GPS Map Length Research Advanced Машинное Обучение Искусственный Интеллект AI ML MLP RBF Автомобиль Машына Сеть Робот Водитель Заворот Дорога Сам Сама Едет Ехать Учить Учит Учится Автоматизация Адаптация Результат Один Одна Одно Вчера Сегодня Завтра Дерево Карта ГПС Информатика Компьютер Наука Научная Работа Школа Вольво Завод Транспорт Maskininlärning Artificiell Intelligens Inlärning Maskin AI ML MLP RBF Neural Neurala Nätverk Artificiella Automation Själv Självkörande Körande Bil Fordon Robot Robotik Körstil Stil Beteende Adaption Kurva Lutning Väg CSA ACC Farthållare Hastighet Fart Hållare Inclination Körfält Fält Spår Bredd Typ Acceleration Longitudinell Lateral Data Traingin Test Validation Resultat Diskussion Längd Hållbarhet Etik Etiskt Framtid Dåtid Idag Imorgon System Class Klass Classer Klasser Träd En Ett Fler Flera Dynamisk Använda Tid Fördröjning Tids Volvo Car Corporation Object Zenuity Skola Dator Vetenskap Forkning Avancerad Projekt Exjobb Examensarbete Betyg GPS Karta Computer Sciences Datavetenskap (datalogi)
324	Vysokovklopn lopata devn tÄpky / High tipping shovel for the wood chips ulk, Petr January 2021 (has links) High tipping shovel, wood chips, quick coupler, shovel, high tipping shovel frame, Volvo L60H loader, wheel loader accessories, linear hydraulic motor

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