Relevant bibliographies by topics / Autonomous vehicle systems

Academic literature on the topic 'Autonomous vehicle systems'

Author: Grafiati
Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Autonomous vehicle systems"

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Yaakub, Salma, and Mohammed Hayyan Alsibai. "A Review on Autonomous Driving Systems." International Journal of Engineering Technology and Sciences 5, no. 1 (June 20, 2018): 1–16. http://dx.doi.org/10.15282/ijets.v5i1.2800.

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Autonomous vehicles are one of the promising solutions to reduce traffic crashes and improve mobility and traffic system. An autonomous vehicle is preferable because it helps in reducing the need for redesigning the infrastructure and because it improves the vehicle power efficiency in terms of cost and time taken to reach the destination. Autonomous vehicles can be divided into 3 types: Aerial vehicles, ground vehicles and underwater vehicles. General, four basic subsystems are integrated to enable a vehicle to move by itself which are: Position identifying and navigation system, surrounding environment situation analysis system, motion planning system and trajectory control system. In this paper, a review on autonomous vehicles and their related technological applications is presented to highlight the aspects of this industry as a part of industry 4.0 concept. Moreover, the paper discusses the best autonomous driving systems to be applied on our wheelchair project which aims at converting a manual wheelchair to a smart electric wheelchair
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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Creating Autonomous Vehicle Systems." Synthesis Lectures on Computer Science 6, no. 1 (October 25, 2017): i—186. http://dx.doi.org/10.2200/s00787ed1v01y201707csl009.

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R. Sushma and J. Satheesh Kumar. "Dynamic Vehicle Modelling and Controlling Techniques for Autonomous Vehicle Systems." December 2022 4, no. 4 (January 9, 2023): 307–15. http://dx.doi.org/10.36548/jeea.2022.4.007.

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The driving scenario of an automated vehicle is the crucial technology in the design of autonomous cars. This suggested approach aims to address the shortcomings of autonomous cars, such as their poor real- time performance and low control precision. The process for building a virtual simulation environment for autonomous vehicle testing and validation is described in this study. Model Predictive Control and Proportional Integral and Derivative Control are used in MATLAB simulation to build three car models. These are related to the 2D and 3D animation used in collision detection and visualization. The virtual engine visualization is included throughout the model. A variety of test circumstances are used to validate the simulation model, and the model’s performance is assessed in the presence of various barriers. The simulation's findings demonstrate that the autonomous vehicle has a strong potential for self-adaptation even in challenging and complex working environments. No instances of car sideslip or track departure have been noted. It is discovered that this autonomous car performs remarkably well overall when compared to other autonomous vehicles. The suggested approach is essential for enhancing autonomous vehicle driving safety, maintaining vehicle control in challenging situations, and improving the advancement of intelligent vehicle driving assistance.
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Thieme, Christoph Alexander, and Ingrid Bouwer Utne. "A risk model for autonomous marine systems and operation focusing on human–autonomy collaboration." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 4 (August 2017): 446–64. http://dx.doi.org/10.1177/1748006x17709377.

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Autonomous marine systems, such as autonomous ships and autonomous underwater vehicles, gain increased interest in industry and academia. Expected benefits of autonomous marine system in comparison to conventional marine systems are reduced cost, reduced risk to operators, and increased efficiency of such systems. Autonomous underwater vehicles are applied in scientific, commercial, and military applications for surveys and inspections of the sea floor, the water column, marine structures, and objects of interest. Autonomous underwater vehicles are costly vehicles and may carry expensive payloads. Hence, risk models are needed to assess the mission success before a mission and adapt the mission plan if necessary. The operators prepare and interact with autonomous underwater vehicles to carry out a mission successfully. Risk models need to reflect these interactions. This article presents a Bayesian belief network to assess the human–autonomy collaboration performance, as part of a risk model for autonomous underwater vehicle operation. Human–autonomy collaboration represents the joint performance of the human operators in conjunction with an autonomous system to achieve a mission aim. A case study shows that the human–autonomy collaboration can be improved in two ways: (1) through better training and inclusion of experienced operators and (2) through improved reliability of autonomous functions and situation awareness of vehicles. It is believed that the human–autonomy collaboration Bayesian belief network can improve autonomous underwater vehicle design and autonomous underwater vehicle operations by clarifying relationships between technical, human, and organizational factors and their influence on mission risk. The article focuses on autonomous underwater vehicle, but the results should be applicable to other types of autonomous marine systems.
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Wallaschek, Jörg, Hendrik Honsel, and Michael Kleinkes. "Autonomous vehicle front lighting systems." International Journal of Vehicle Autonomous Systems 10, no. 3 (2012): 256. http://dx.doi.org/10.1504/ijvas.2012.051248.

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Islam, Mhafuzul, Mashrur Chowdhury, Hongda Li, and Hongxin Hu. "Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 12 (July 31, 2019): 494–507. http://dx.doi.org/10.1177/0361198119855606.

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Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.
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Oliveira, Luis, Karl Proctor, Christopher G. Burns, and Stewart Birrell. "Driving Style: How Should an Automated Vehicle Behave?" Information 10, no. 6 (June 25, 2019): 219. http://dx.doi.org/10.3390/info10060219.

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This article reports on a study to investigate how the driving behaviour of autonomous vehicles influences trust and acceptance. Two different designs were presented to two groups of participants (n = 22/21), using actual autonomously driving vehicles. The first was a vehicle programmed to drive similarly to a human, “peeking” when approaching road junctions as if it was looking before proceeding. The second design had a vehicle programmed to convey the impression that it was communicating with other vehicles and infrastructure and “knew” if the junction was clear so could proceed without ever stopping or slowing down. Results showed non-significant differences in trust between the two vehicle behaviours. However, there were significant increases in trust scores overall for both designs as the trials progressed. Post-interaction interviews indicated that there were pros and cons for both driving styles, and participants suggested which aspects of the driving styles could be improved. This paper presents user information recommendations for the design and programming of driving systems for autonomous vehicles, with the aim of improving their users’ trust and acceptance.
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Oktay, Tugrul, Harun Celik, and Ilke Turkmen. "Maximizing autonomous performance of fixed-wing unmanned aerial vehicle to reduce motion blur in taken images." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 232, no. 7 (March 28, 2018): 857–68. http://dx.doi.org/10.1177/0959651818765027.

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In this study, reducing motion blur in images taken by our unmanned aerial vehicle is investigated. Since shakes of unmanned aerial vehicle cause motion blur in taken images, autonomous performance of our unmanned aerial vehicle is maximized to prevent it from shakes. In order to maximize autonomous performance of unmanned aerial vehicle (i.e. to reduce motion blur), initially, camera mounted unmanned aerial vehicle dynamics are obtained. Then, optimum location of unmanned aerial vehicle camera is estimated by considering unmanned aerial vehicle dynamics and autopilot parameters. After improving unmanned aerial vehicle by optimum camera location, dynamics and controller parameters, it is called as improved autonomous controlled unmanned aerial vehicle. Also, unmanned aerial vehicle with camera fixed at the closest point to center of gravity is called as standard autonomous controlled unmanned aerial vehicle. Both improved autonomous controlled and standard autonomous controlled unmanned aerial vehicles are performed in real time flights, and approximately same trajectories are tracked. In order to compare performance of improved autonomous controlled and standard autonomous controlled unmanned aerial vehicles in reducing motion blur, a motion blur kernel model which is derived using recorded roll, pitch and yaw angles of unmanned aerial vehicle is improved. Finally, taken images are simulated to examine effect of unmanned aerial vehicle shakes. In comparison with standard autonomous controlled flight, important improvements on reducing motion blur are demonstrated by improved autonomous controlled unmanned aerial vehicle.
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Ding, Zhizhong, Chao Sun, Momiao Zhou, Zhengqiong Liu, and Congzhong Wu. "Intersection Vehicle Turning Control for Fully Autonomous Driving Scenarios." Sensors 21, no. 12 (June 9, 2021): 3995. http://dx.doi.org/10.3390/s21123995.

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Currently the research and development of autonomous driving vehicles (ADVs) mainly consider the situation whereby manual driving vehicles and ADVs run simultaneously on lanes. In order to acquire the information of the vehicle itself and the environment necessary for decision-making and controlling, the ADVs that are under development now are normally equipped with a lot of sensing units, for example, high precision global positioning systems, various types of radar, and video processing systems. Obviously, the current advanced driver assistance systems (ADAS) or ADVs still have some problems concerning high reliability of driving safety, as well as the vehicle’s cost and price. It is certain, however, that in the future there will be some roads, areas or cities where all the vehicles are ADVs, i.e., without any human driving vehicles in traffic. For such scenarios, the methods of environment sensing, traffic instruction indicating, and vehicle controlling should be different from that of the situation mentioned above if the reliability of driving safety and the production cost expectation is to be improved significantly. With the anticipation that a more sophisticated vehicle ad hoc network (VANET) should be an essential transportation infrastructure for future ADV scenarios, the problem of vehicle turning control based on vehicle to everything (V2X) communication at road intersections is studied. The turning control at intersections mainly deals with three basic issues, i.e., target lane selection, trajectory planning and calculation, and vehicle controlling and tracking. In this paper, control strategy, model and algorithms are proposed for the three basic problems. A model predictive control (MPC) paradigm is used as the vehicle upper layer controller. Simulation is conducted on the CarSim-Simulink platform with typical intersection scenes.
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Wu, Shu Yun, and Xu Hao Lv. "Four-Rotor Autonomous Vehicle." Applied Mechanics and Materials 505-506 (January 2014): 286–91. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.286.

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Four rotary-wing micro air vehicles use four motors as the power unit, by adjusting the motor speed control flight of underactuated systems [. In order to achieve four-rotor autonomous vehicle autonomous flight control, preliminary design of flight control system, and use F5F100LEA single-chip as computer control unit, Proposed the flight system hardware design. Vehicle has the advantages of light weight, small size, low power consumption. After several laboratory tests, the design and reliable performance, to meet the aircraft take off, hover, landing flight mode control requirements.
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Dissertations / Theses on the topic "Autonomous vehicle systems"

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Hejase, Mohammad. "Dynamic Probabilistic Risk Assessment of Autonomous Vehicle Systems." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1546473181365722.

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Chen, Qi. "Studies in autonomous ground vehicle control systems structure and algorithms /." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1165959992.

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Fithian, Jeff E. "A laser-guided, autonomous automated guided vehicle." Thesis, Virginia Tech, 1993. http://hdl.handle.net/10919/42957.

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The purpose of this research was to determine the feasibility of a laser-based positioning system as a primary navigation method. The system developed for this research consisted of an automated guided vehicle which navigated solely with the use of the laser-based positioning system in real-time. To date, there are no systems which can navigate a pre-defined path using such a positioning system. Some lessons were learned by the researcher, however, concerning the viability of this system in an industrial environment. The system should have had the following advantages over previous systems: 1) Greater range, 2) no prior structuring of environment, 3) real-time navigation, and 4) no reliance on dead-reckoning for navigation.

The results showed that goals two through four had been met and are advantages of this system over current systems. The range of this system is limited, however, but it is believed that the next generation system should have greater range than the system used in this research.


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Yan, Jingsheng. "Platoon modal operations under vehicle autonomous adaptive cruise control model." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-07102009-040612/.

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Korkmaz, Ozan. "Modeling And Control Of Autonomous Underwater Vehicle Manipulator Systems." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615051/index.pdf.

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In this thesis, dynamic modeling and nonlinear control of autonomous underwater vehicle manipulator systems are presented. Mainly, two types of systems consisting of a 6-DOF AUV equipped with a 6-DOF manipulator subsystem (UVMS) and with an 8-DOF redundant manipulator subsystem (UVRMS) are modeled considering hydrostatic forces and hydrodynamic effects such as added mass, lift, drag and side forces. The shadowing effects of the bodies on each other are introduced when computing the hydrodynamic forces. The system equations of motion are derived recursively using Newton&ndash
Euler formulation. The inverse dynamics control algorithms are formulated and trajectory tracking control of the systems is achieved by assigning separate tasks for the end effector of the manipulator and for the underwater vehicle. The proposed inverse dynamics controller utilizes the full nonlinear model of the system and consists of a linearizing control law that uses the feedback of positions and velocities of the joints and the underwater vehicle in order to cancel off the nonlinearities of the system. The PD control is applied after this complicated feedback linearization process yielding second order error dynamics. The thruster dynamics is also incorporated into the control system design. The stability analysis is performed in the presence of parametric uncertainty and disturbing ocean current. The effectiveness of the control methods are demonstrated by simulations for typical underwater missions.
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Kang, Yong Suk. "Development of Predictive Vehicle Control System using Driving Environment Data for Autonomous Vehicles and Advanced Driver Assistance Systems." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85106.

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In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, Driver Assistance Systems (DAS) such as cruise control, Anti-Lock Braking Systems (ABS), and Electronic Stability Control (ESC) have become widely popular in the automotive industry. Therefore, vehicle control research attracts attention from both academia and industry, and has been an active area of vehicle research for over 30 years, resulting in impressive DAS contributions. Although current vehicle control systems have improved vehicle safety and performance, there is room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming local driving environment such as terrain roughness, elevation grade, bank angle, curvature, and friction. The local driving environment is measured in advance with a terrain measurement system to provide terrain data. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing the response measurements of a preceding vehicle. The response measurements of a preceding vehicle are acquired through Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication. The identification method analyzes the response measurements of a preceding vehicle to estimate road data. The estimated road data or the pre-measured road data is used as the upcoming driving environment information for the developed vehicle control system. The metric that objectively quantifies vehicle performance, the Performance Margin, is developed to accomplish the control objectives in an efficient manner. The metric is used as a control reference input and continuously estimated to predict current and future vehicle performance. Next, the predictive control algorithm is developed based on the upcoming driving environment and the performance metric. The developed system predicts future vehicle dynamics states using the upcoming driving environment and the Performance Margin. If the algorithm detects the risks of future vehicle dynamics, the control system intervenes between the driver's input commands based on estimated future vehicle states. The developed control system maintains vehicle handling capabilities based on the results of the prediction by regulating the metric into an acceptable range. By these processes, the developed control system ensures that the vehicle maintains stability consistently, and improves vehicle performance for the near future even if there are undesirable and unexpected driving circumstances. To implement and evaluate the integrated systems of this work, the real-time driving simulator, which uses precise real-world driving environment data, has been developed for advanced high computational vehicle control systems. The developed vehicle control system is implemented in the driving simulator, and the results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS.
Ph. D.
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Sarton, Christopher J. "Autopilot using differential thrust for ARIES autonomous underwater vehicle." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FSarton.pdf.

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Strandell, Erstorp Elias. "Evaluation of the LSTS Toolchain for Networked Vehicle Systems on KTH Autonomous Maritime Vehicles." Thesis, KTH, Marina system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185273.

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The department of Naval Architecture at the Royal Institute of Technology is in posession of one Autonomous Underwater Vehicle (AUV) and a second is under construction. A project for doing hydrographic mapping using an Autonomous Surface Vehicle (ASV) is also initiated. These projects raises the need for a software to easily send commands to vehicles and to review collected data. The ability to use each vehicle as a node in a network of vehicles is also requested. This thesis examines a software toolchain developed at the Underwater Systems and Technology Laboratory (LSTS) in Portugal for mission planning and control of networked autonomous vehicles. The toolchain constitutes primarily of Neptus, which provides an operator with a user interface for realtime control and feedback from vehicles, and DUNE. DUNE is a software running on-board vehicles and communicates with Neptus over a wireless network. As a first step, and as a limitation to this thesis, the toolchain has been used to control an autonomous rover. An autopilot receives waypoints in form of latitude/longitude coordinates from DUNE and periodically sends position and various sensor readings back. DUNE is running on a GNU/Linux computer and is responsible for storing a mission of multiple waypoints and to keep track of the progress. DUNE forwards vehicle location and sensor data to Neptus for feedback in the user interface and generation of plots. In conclusion the author was able to create and execute missions of an arbitrary number of waypoints. Graphs of basically any sensor reading could be generated through the Mission Review and Analysis tool contained by Neptus. Implementing the toolchain on the departments marine vehicles releases valuable time during field tests and will in the future provide a way for experimentation with deliberate planning tools; the next natural step toward complete autonomy.
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RAHMAN, SHAHNUR. "Visual Perception in Autonomous Vehicles." Thesis, KTH, Hållbarhet och industriell dynamik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189346.

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The human factor accounts for nine out of ten out of all traffic accidents, and because more vehicles are being deployed on the roads, the number of accidents will increase. Because of this, various automated functions have been implemented in vehicles in order to minimize the human factor in driving. In recent year, this development has accelerated and vehicles able to perform the complete driving task without any human assistance have begun to emerge from different projects around the world. However, the autonomous vehicle still has many barriers to overcome before safe driving in traffic becomes a reality. One of these barriers is the difficulty to visually perceive the surrounding. This is partly because of the fact that something can cover the camera sensors, but it is also problematic to translate the perceived data, that the sensors are collecting, into something valuable for the passenger. The situation could be improved if wireless communications were available to the autonomous vehicle. Instead of trying to understand the surrounding by the use of camera sensors, the autonomous vehicle could obtain the necessary data via wireless communication, which was the subject of this study. The study showed that wireless communication will be significant for the autonomous vehicle in the future. The conclusion is based on the fact that wireless communication was a solution in other transport systems that have had the similar barrier as for the autonomous vehicle. There are also plans on managing the barrier via wireless communication in pilot projects related to autonomous vehicles.
Den mänskliga faktorn står för nio av tio utav alla trafikolyckor, och eftersom att allt fler fordon kommer ut på vägarna så leder det till att olycksantalet ökar. På grund av detta så har olika automatiserade funktioner applicerats i fordonet för att undvika den mänskliga faktorn i körningen. Denna utveckling har accelererat och fordon som ska kunna utföra hela det dynamiska framförandet utan mänsklig assistans har börjat utvecklas i olika projekt runt om i världen. Dock så har det autonoma fordonet många barriärer kvar att övervinna, för säkert framförande, varav en av dessa barriärer är fordonets förmåga att visuellt uppfatta omgivningen. Dels genom att något kan täcka kamerasensorerna men även att kunna omsätta det sensorerna uppfattar till något värdefullt för passageraren. Situationen skulle dock kunna förbättras om trådlös kommunikation gjordes tillgänglig för det autonoma fordonet. Istället för att försöka uppfatta omgivningen via kamerasensorer, skulle det autonoma fordonet kunna få den information som behövs via trådlös kommunikation, vilket är vad denna studie behandlade. Studien visade att trådlös kommunikation kommer att ha en betydelse för det autonoma fordonet i framtiden. Slutsatsen grundar sig på att trådlös kommunikation varit en lösning inom andra transportsystem som haft en liknande barriär som för det autonoma fordonet. Man planerar dessutom på att hantera det autonoma fordonets barriär via trådlös kommunikation i pilotprojekt i dagsläget
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Biondani, Luca. "Control system for agricultural autonomous electric vehicle." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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The subject of this thesis is the realization of the control system of an autonomous electric vehicle for agricultural applications. The robot will be used for field experimentation of innovative agricultural techniques. The software is developed in LabVIEW programming language, and is employed on an embedded system manufactured by National Instruments that is used as Platform Control Unit. MATLAB and Simulink software are used for simulations and processing of the collected experimental data. As a secondary activity, the electrical circuit was realized including both high-power and signal control wiring harness. The result of the thesis is a working prototype that will be used in a first section of the experimental plant, located at the DISTAL Experimental Center in Cadriano.
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Books on the topic "Autonomous vehicle systems"

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. Creating Autonomous Vehicle Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01805-3.

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. Creating Autonomous Vehicle Systems. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01802-2.

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Miller, Richard Kendall. Survey on autonomous vehicle guidance systems. Madison, GA: Future Technology Surveys, 1989.

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Wallace, Rodrick. Canonical Instabilities of Autonomous Vehicle Systems. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69935-6.

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H Liu, Hugh, and Bo Zhu, eds. Formation Control of Multiple Autonomous Vehicle Systems. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119263081.

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Wang, Yuanzhe, and Danwei Wang. Collaborative Fleet Maneuvering for Multiple Autonomous Vehicle Systems. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5798-7.

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Zuev, Sergey, Ruslan Maleev, and Aleksandr Chernov. Energy efficiency of electrical equipment systems of autonomous objects. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1740252.

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When considering the main trends in the development of modern autonomous objects (aircraft, combat vehicles, motor vehicles, floating vehicles, agricultural machines, etc.) in recent decades, two key areas can be identified. The first direction is associated with the improvement of traditional designs of autonomous objects (AO) with an internal combustion engine (ICE) or a gas turbine engine (GTD). The second direction is connected with the creation of new types of joint-stock companies, namely electric joint-stock companies( EAO), joint-stock companies with combined power plants (AOKEU). The energy efficiency is largely determined by the power of the generator set and the battery, which is given to the electrical network in various driving modes. Most of the existing methods for calculating power supply systems use the average values of disturbing factors (generator speed, current of electric energy consumers, voltage in the on-board network) when choosing the characteristics of the generator set and the battery. At the same time, it is obvious that when operating a motor vehicle, these parameters change depending on the driving mode. Modern methods of selecting the main parameters and characteristics of the power supply system do not provide for modeling its interaction with the power unit start-up system of a motor vehicle in operation due to the lack of a systematic approach. The choice of a generator set and a battery, as well as the concept of the synthesis of the power supply system is a problem studied in the monograph. For all those interested in electrical engineering and electronics.
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Trimble, Tammy E., Stephanie Baker, Jason Wagner, Wendy Wagner, Lisa Loftus-Otway, Brad Mallory, Susanna Gallun, et al. Implications of Connected and Automated Driving Systems, Vol. 4: Autonomous Vehicle Action Plan. Washington, D.C.: Transportation Research Board, 2018. http://dx.doi.org/10.17226/25292.

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Davis, Duane T. Precision control and maneuvering of the Phoenix autonomous underwater vehicle for entering a recovery tube. Monterey, Calif: Naval Postgraduate School, 1996.

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Trimble, Tammy E., Stephanie Baker, Jason Wagner, Myra Blanoo, Wendy Wagner, Lisa Loftus-Otway, Brad Mallory, et al. Implications of Connected and Automated Driving Systems, Vol. 5: Developing the Autonomous Vehicle Action Plan. Washington, D.C.: Transportation Research Board, 2018. http://dx.doi.org/10.17226/25291.

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Book chapters on the topic "Autonomous vehicle systems"

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Autonomous Vehicle Localization." In Creating Autonomous Vehicle Systems, 15–49. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01802-2_2.

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Autonomous Vehicle Localization." In Creating Autonomous Vehicle Systems, 15–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01805-3_2.

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Client Systems for Autonomous Driving." In Creating Autonomous Vehicle Systems, 155–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01802-2_8.

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Client Systems for Autonomous Driving." In Creating Autonomous Vehicle Systems, 159–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01805-3_8.

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Perception in Autonomous Driving." In Creating Autonomous Vehicle Systems, 51–67. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01802-2_3.

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Introduction to Autonomous Driving." In Creating Autonomous Vehicle Systems, 1–14. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01802-2_1.

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Introduction to Autonomous Driving." In Creating Autonomous Vehicle Systems, 1–13. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01805-3_1.

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Perception in Autonomous Driving." In Creating Autonomous Vehicle Systems, 51–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01805-3_3.

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "PerceptIn’s Autonomous Vehicles Lite." In Creating Autonomous Vehicle Systems, 203–13. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01805-3_11.

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Cloud Platform for Autonomous Driving." In Creating Autonomous Vehicle Systems, 169–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01802-2_9.

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Conference papers on the topic "Autonomous vehicle systems"

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Wang, Qian, and Beshah Ayalew. "Obstacle Filtering Algorithm for Control of an Autonomous Road Vehicle in Public Highway Traffic." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9835.

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This paper presents an obstacle filtering algorithm that mimics human driver-like grouping of objects within a model predictive control scheme for an autonomous road vehicle. In the algorithm, a time to collision criteria is first used as risk assessment indicator to filter the potentially dangerous obstacle object vehicles in the proximity of the autonomously controlled vehicle. Then, the filtered object vehicles with overlapping elliptical collision areas put into groups. A hyper elliptical boundary is regenerated to define an extended collision area for the group. To minimize conservatism, the parameters for the tightest hyper ellipse are determined by solving an optimization problem. By excluding undesired local minimums for the planning problem, the grouping alleviates limitations that arise from the limited prediction horizons used in the model predictive control. The computational details of the proposed algorithm as well as its performance are illustrated using simulations of an autonomously controlled vehicle in public highway traffic scenarios involving multiple other vehicles.
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Shapiro, Sondra S., and John F. Gilmore. "Autonomous Unmanned Vehicle Systems." In SPIE 1989 Technical Symposium on Aerospace Sensing, edited by Mohan M. Trivedi. SPIE, 1989. http://dx.doi.org/10.1117/12.969357.

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Kang, Namwoo, Fred M. Feinberg, and Panos Y. Papalambros. "Autonomous Electric Vehicle Sharing System Design." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46491.

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Car-sharing services promise “green” transportation systems. Two vehicle technologies offer marketable, sustainable sharing: Autonomous vehicles eliminate customer requirements for car pick-up and return, and battery electric vehicles entail zero-emissions. Designing an Autonomous Electric Vehicle (AEV) fleet must account for the relationships among fleet operations, charging station operations, electric powertrain performance, and consumer demand. This paper presents a system design optimization framework integrating four sub-system problems: Fleet size and assignment schedule; number and locations of charging stations; vehicle powertrain requirements; and service fees. A case study for an autonomous fleet operating in Ann Arbor, Michigan, is used to examine AEV sharing system profitability and feasibility for a variety of market scenarios.
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Aasted, Christopher M., Sunwook Lim, and Rahmat A. Shoureshi. "Vehicle Health Inferencing Using Feature-Based Neural-Symbolic Networks." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3831.

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In order to optimize the use of fault tolerant controllers for unmanned or autonomous aerial vehicles, a health diagnostics system is being developed. To autonomously determine the effect of damage on global vehicle health, a feature-based neural-symbolic network is utilized to infer vehicle health using historical data. Our current system is able to accurately characterize the extent of vehicle damage with 99.2% accuracy when tested on prior incident data. Based on the results of this work, neural-symbolic networks appear to be a useful tool for diagnosis of global vehicle health based on features of subsystem diagnostic information.
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Lee, Ungki, Sunghyun Jeon, and Ikjin Lee. "Shared Autonomous Vehicle System Design for Battery Electric Vehicle (BEV) and Fuel Cell Electric Vehicle (FCEV)." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-67734.

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Abstract Shared autonomous vehicles (SAVs) encompassing autonomous driving technology and car-sharing service are expected to become an essential part of transportation system in the near future. Although many studies related to SAV system design and optimization have been conducted, most of them are focused on shared autonomous battery electric vehicle (SABEV) systems, which employ battery electric vehicles (BEVs) as SAVs. As fuel cell electric vehicles (FCEVs) emerge as alternative fuel vehicles along with BEVs, the need for research on shared autonomous fuel cell electric vehicle (SAFCEV) systems employing FCEVs as SAVs is increasing. Therefore, this study newly presents a design framework of SAFCEV system by developing an SAFCEV design model based on a proton-exchange membrane fuel cell (PEMFC) model. The test bed for SAV system design is Seoul, and optimization is conducted for SABEV and SAFCEV systems to minimize the total cost while satisfying the customer wait time constraint, and the optimization results of both systems are compared. From the results, it is verified that the SAFCEV system is feasible and the total cost of the SAFCEV system is even lower compared to the SABEV system. In addition, several observations on various operating environments of SABEV and SAFCEV systems are obtained from parametric studies.
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De Novi, G., C. Melchiorri, J. C. Garcia, P. J. Sanz, P. Ridao, and G. Oliver. "A new approach for a Reconfigurable Autonomous Underwater Vehicle for Intervention." In 2009 3rd Annual IEEE Systems Conference. IEEE, 2009. http://dx.doi.org/10.1109/systems.2009.4815765.

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Yu, Huan, Shumon Koga, and Miroslav Krstic. "Stabilization of Traffic Flow With a Leading Autonomous Vehicle." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9239.

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This paper develops boundary control law for autonomous vehicles to stabilize the stop-and-go traffic on freeway. The macroscopic traffic dynamics is described by the Aw-Rascle-Zhang (ARZ) model in a time and state dependent domain. The leading autonomous vehicle aims to regulate the traffic behind it to uniform equilibrium and the domain length of the traffic to a setpoint. The traffic density and speed is governed by second-order, nonlinear hyperbolic partial differential equations (PDEs), coupled with a state-dependent ODE for the leading autonomous vehicle. The actuation is the speed of autonomous vehicle at the moving front boundary of the domain. We linearize the system around a uniform velocity and density reference and certain physical properties are discussed for the model validity. The linearized model describes the dynamics of deviations of density and velocity from the reference. By transforming the linearized system in a moving coordinate, we obtain a domain with a fixed boundary at one end and a state-dependent moving boundary at the other end. The well-posedness of the system is proved and the linear instability of open-loop system is shown. We further map the system to Riemann variables and based on it, propose the boundary feedback control law actuated by the leading autonomous vehicle. The exponential stability of state variables in L2 norm and convergence to the setpoint domain length is achieved for the closed-loop system.
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Roy, Debjit, Ananth Krishnamurthy, Sunderesh Heragu, and Charles Malmborg. "Vehicle interference effects in warehousing systems with autonomous vehicles." In 2010 IEEE International Conference on Automation Science and Engineering (CASE 2010). IEEE, 2010. http://dx.doi.org/10.1109/coase.2010.5584777.

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Kornhauser, Alain L. "Neural Network Approaches for Lateral Control of Autonomous Highway Vehicles." In Vehicle Navigation & Instrument Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1991. http://dx.doi.org/10.4271/912871.

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Behl, Madhur, Jackson DuBro, Taylor Flynt, Imaan Hameed, Grace Lang, and Felix Park. "Autonomous Electric Vehicle Charging System." In 2019 Systems and Information Engineering Design Symposium (SIEDS). IEEE, 2019. http://dx.doi.org/10.1109/sieds.2019.8735620.

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Reports on the topic "Autonomous vehicle systems"

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Sorrell, F. Y. Autonomous Underwater Vehicle Systems. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada627926.

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Kwiat, Paul, Eric Chitambar, Andrew Conrad, and Samantha Isaac. Autonomous Vehicle-Based Quantum Communication Network. Illinois Center for Transportation, September 2022. http://dx.doi.org/10.36501/0197-9191/22-020.

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Quantum communication was demonstrated using autonomous vehicle-to-vehicle (V2V), as well as autonomous vehicle-to-infrastructure (V2I). Supporting critical subsystems including compact size, weight, and power (SWaP) quantum sources; optical systems; and pointing, acquisition, and tracking (PAT) subsystems were designed, developed, and tested. Novel quantum algorithms were created and analyzed, including quantum position verification (QPV) for mobile autonomous vehicles. The results of this research effort can be leveraged in support of future cross-platform, mobile quantum communication networks that provide improved security, more accurate autonomous sensors, and connected quantum computing nodes for next-generation, smart-infrastructure systems.
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An, Edgar, and William E. Baxley. Multi-User Autonomous Underwater Vehicle Docking Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada629522.

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Razdan, Rahul. Unsettled Topics Concerning Human and Autonomous Vehicle Interaction. SAE International, December 2020. http://dx.doi.org/10.4271/epr2020025.

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This report examines the current interaction points between humans and autonomous systems, with a particular focus on advanced driver assistance systems (ADAS), the requirements for human-machine interfaces as imposed by human perception, and finally, the progress being made to close the gap. Autonomous technology has the potential to benefit personal transportation, last-mile delivery, logistics, and many other mobility applications enormously. In many of these applications, the mobility infrastructure is a shared resource in which all the players must cooperate. In fact, the driving task has been described as a “tango” where we—as humans—cooperate naturally to enable a robust transportation system. Can autonomous systems participate in this tango? Does that even make sense? And if so, how do we make it happen?
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Wongpiromsarn, Tichakorn, Sayan Mitra, Richard M. Murray, and Andrew Lamperski. Verification of Periodically Controlled Hybrid Systems: Application to An Autonomous Vehicle. Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada522591.

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Hovakimyan, Naira. Development of Adaptive Algorithms for Visual Control of Autonomous Multi-Vehicle Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada512547.

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Wang, Shenlong, and David Forsyth. Safely Test Autonomous Vehicles with Augmented Reality. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-015.

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This work exploits augmented reality to safely train and validate autonomous vehicles’ performance in the real world under safety-critical scenarios. Toward this goal, we first develop algorithms that create virtual traffic participants with risky behaviors and seamlessly insert the virtual events into real images perceived from the physical world. The resulting composed images are photorealistic and physically grounded. The manipulated images are fed into the autonomous vehicle during testing, allowing the self-driving vehicle to react to such virtual events within either a photorealistic simulator or a real-world test track and real hardware systems. Our presented technique allows us to develop safe, hardware-in-the-loop, and cost-effective tests for self-driving cars to respond to immersive safety-critical traffic scenarios.
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Moorehead, Stewart. Unsettled Issues Regarding the Commercialization of Autonomous Agricultural Vehicles. SAE International, February 2022. http://dx.doi.org/10.4271/epr2022003.

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Autonomous agricultural vehicles are entering the marketplace, performing jobs that current equipment cannot do or are too dangerous for humans to perform. They offer the prospect of greater farm productivity, and they will help to feed the world’s growing population. This report looks at several topics that impact the commercial success of autonomous agricultural vehicles. The economic benefit that an autonomous system brings to a farm will be discussed alongside machine utilization rates, job quality, and labor savings. The need for standards and regulations to help promote the development of safe systems—as well as to define the language around autonomous agriculture—is also considered. Additionally, this report will highlight the importance of reliability in agricultural machinery and how successful commercialization of autonomy will depend on the ability to do the job correctly and consistently. A critical part of commercial success is how the autonomous agricultural vehicle fits into existing farm processes to provide a complete solution for the farmer. It is hoped that this report will help developers interested in commercializing autonomous agricultural vehicles consider more than just the technical problems to solve and make choices beneficial to market adoption.
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DURRNAT-WHYTE, HUGH. A Critical Review of the State-of-the-Art in Autonomous Land Vehicle Systems and Technology. Office of Scientific and Technical Information (OSTI), November 2001. http://dx.doi.org/10.2172/792867.

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Parker, Michael, Alex Stott, Brian Quinn, Bruce Elder, Tate Meehan, and Sally Shoop. Joint Chilean and US mobility testing in extreme environments. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42362.

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Vehicle mobility in cold and challenging terrains is of interest to both the US and Chilean Armies. Mobility in winter conditions is highly vehicle dependent with autonomous vehicles experiencing additional challenges over manned vehicles. They lack the ability to make informed decisions based on what they are “seeing” and instead need to rely on input from sensors on the vehicle, or from Unmanned Aerial Systems (UAS) or satellite data collections. This work focuses on onboard vehicle Controller Area Network (CAN) Bus sensors, driver input sensors, and some externally mounted sensors to assist with terrain identification and overall vehicle mobility. Analysis of winter vehicle/sensor data collected in collaboration with the Chilean Army in Lonquimay, Chile during July and August 2019 will be discussed in this report.
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