Relevant bibliographies by topics / Autonomous Vehicle Network

Academic literature on the topic 'Autonomous Vehicle Network'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Autonomous Vehicle Network"

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Alsuwian, Turki, Mian Hamza Usman, and Arslan Ahmed Amin. "An Autonomous Vehicle Stability Control Using Active Fault-Tolerant Control Based on a Fuzzy Neural Network." Electronics 11, no. 19 (October 1, 2022): 3165. http://dx.doi.org/10.3390/electronics11193165.

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Due to instability issues in autonomous vehicles, the risk of danger is increasing rapidly. These problems arise due to unwanted faults in the sensor or the actuator, which decrease vehicle efficiency. In this modern era of autonomous vehicles, the risk factor is also increased as the vehicles have become automatic, so there is a need for a fault-tolerant control system (FTCS) to avoid accidents and reduce the risk factors. This paper presents an active fault-tolerant control (AFTC) for autonomous vehicles with a fuzzy neural network that can autonomously identify any wheel speed problem to avoid instability issues in an autonomous vehicle. MATLAB/Simulink environment was used for simulation experiments and the results demonstrate the stable operation of the wheel speed sensors to avoid accidents in the event of faults in the sensor or actuator if the vehicle becomes unstable. The simulation results establish that the AFTC-based autonomous vehicle using a fuzzy neural network is a highly reliable solution to keep cars stable and avoid accidents. Active FTC and vehicle stability make the system more efficient and reliable, decreasing the chance of instability to a minimal point.
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Tran, Quang-Duy, and Sang-Hoon Bae. "An Efficiency Enhancing Methodology for Multiple Autonomous Vehicles in an Urban Network Adopting Deep Reinforcement Learning." Applied Sciences 11, no. 4 (February 8, 2021): 1514. http://dx.doi.org/10.3390/app11041514.

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To reduce the impact of congestion, it is necessary to improve our overall understanding of the influence of the autonomous vehicle. Recently, deep reinforcement learning has become an effective means of solving complex control tasks. Accordingly, we show an advanced deep reinforcement learning that investigates how the leading autonomous vehicles affect the urban network under a mixed-traffic environment. We also suggest a set of hyperparameters for achieving better performance. Firstly, we feed a set of hyperparameters into our deep reinforcement learning agents. Secondly, we investigate the leading autonomous vehicle experiment in the urban network with different autonomous vehicle penetration rates. Thirdly, the advantage of leading autonomous vehicles is evaluated using entire manual vehicle and leading manual vehicle experiments. Finally, the proximal policy optimization with a clipped objective is compared to the proximal policy optimization with an adaptive Kullback–Leibler penalty to verify the superiority of the proposed hyperparameter. We demonstrate that full automation traffic increased the average speed 1.27 times greater compared with the entire manual vehicle experiment. Our proposed method becomes significantly more effective at a higher autonomous vehicle penetration rate. Furthermore, the leading autonomous vehicles could help to mitigate traffic congestion.
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Alpos, Theodoros, Christina Iliopoulou, and Konstantinos Kepaptsoglou. "Nature-Inspired Optimal Route Network Design for Shared Autonomous Vehicles." Vehicles 5, no. 1 (December 24, 2022): 24–40. http://dx.doi.org/10.3390/vehicles5010002.

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Emerging forms of shared mobility call for new vehicle routing models that take into account vehicle sharing, ride sharing and autonomous vehicle fleets. This study deals with the design of an optimal route network for autonomous vehicles, considering both vehicle sharing and ride sharing. The problem is modeled as a one-to-many-to-one vehicle routing problem with vehicle capacity and range constraints. An ant colony optimization algorithm is applied to the problem in order to construct a set of routes that satisfies user requests under operational constraints. Results show that the algorithm is able to produce solutions in relatively short computational times, while exploiting the possibility of ride sharing to reduce operating costs. Results also underline the potential of exploiting shared autonomous vehicles in the context of a taxi service for booking trips through electronic reservation systems.
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Faris, Waleed F. "Cyber-Attack Detection in Autonomous Vehicle Networks by Energy Aware Optimal Data Transmission with Game Fuzzy Q-Learning based Heuristic Routing Protocol." International Journal on Future Revolution in Computer Science & Communication Engineering 8, no. 3 (September 15, 2022): 75–85. http://dx.doi.org/10.17762/ijfrcsce.v8i3.2096.

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The automotive sector has seen a dramatic transition due to rapid technological advancement. Network connection has improved, enabling the transfer of the cars' technologies from being fully machine- to software-controlled. Controller area network (CAN) bus protocol manages network for autonomous vehicles. However, due to the intricacy of data and traffic patterns that facilitate unauthorised access to a can bus and many sorts of assaults, the autonomous vehicle network still has security flaws as well as vulnerabilities. This research proposes novel technique in cyber attack detection in autonomous vehicle networks enhanced data transmission based optimization and routing technique. Here the autonomous vehicle network optimal data transmission has been carried out using energy aware lagrangian multipliers based optimal data transmission. The cyber attack detection has been carried out using fuzzy q-learning based heuristic routing protocol. The experimental results has been carried out based on optimal data transmission and attack detection in terms of throughput of 95%, PDR of 94%, End-end delay of 46%, energy efficiency of 96%, network lifetime of 95%, attack detection rate of 88%.
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Yu, Chun Yan, Ming Hui Wu, and Xiao Sheng He. "Vehicle Swarm Motion Coordination through Independent Local-Reactive Agents." Advanced Materials Research 108-111 (May 2010): 619–24. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.619.

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Vehicle swarm refers to a group of autonomous vehicles. Vehicle swarm motion coordination is a difficult problem in Intelligent Transport System. Due to similar characteristics of reactive agents and autonomous vehicles relying on self-organization principles, this paper presents reactive agent driven motion coordination for vehicle swarm that adopts large-scale independent local-reactive agents to perform a self-organized motion coordination control mechanism, which is composed of a network of swarm collaborative agents, a set of dynamic hybrid local networks of individual swarm collaborative agent and vehicle autonomic agents, and a homogenous self-organized motion coordination control protocol for individual vehicle autonomic agent’s self-adapting motion.
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Fakhrmoosavi, Fatemeh, Ramin Saedi, Ali Zockaie, and Alireza Talebpour. "Impacts of Connected and Autonomous Vehicles on Traffic Flow with Heterogeneous Drivers Spatially Distributed over Large-Scale Networks." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (August 10, 2020): 817–30. http://dx.doi.org/10.1177/0361198120940997.

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Connected and automated vehicle technologies are expected to significantly contribute in improving mobility and safety. As connected and autonomous vehicles have not been used in practice at large scale, there are still some uncertainties in relation to their applications. Therefore, researchers utilize traffic simulation tools to model the presence of these vehicles. There are several studies on the impacts of vehicle connectivity and automation at the segment level. However, only a few studies have investigated these impacts on traffic flow at the network level. Most of these studies consider a uniform distribution of connected or autonomous vehicles over the network. They also fail to consider the interactions between heterogeneous drivers, with and without connectivity, and autonomous vehicles at the network level. Therefore, this study aims to realistically observe the impacts of these emerging technologies on traffic flow at the network level by incorporating adaptive fundamental diagrams in a mesoscopic simulation tool. The adaptive fundamental diagram concept considers spatially and temporally varying distributions of different vehicle types with heterogeneous drivers. Furthermore, this study considers the intersection capacity variations and fundamental diagram adjustments for arterial links resulting from the presence of different vehicle types and driver classes. The proposed methodology is applied to a large-scale network of Chicago. The results compare network fundamental diagrams and hysteresis loop areas for different proportions of connected and autonomous vehicles. In addition to quantifying impacts of connected and autonomous vehicles, the results demonstrate the impacts of various factors associated with these vehicles on traffic flow at the network level.
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Khayyat, Mashael, Abdullah Alshahrani, Soltan Alharbi, Ibrahim Elgendy, Alexander Paramonov, and Andrey Koucheryavy. "Multilevel Service-Provisioning-Based Autonomous Vehicle Applications." Sustainability 12, no. 6 (March 23, 2020): 2497. http://dx.doi.org/10.3390/su12062497.

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With the recent advances and development of autonomous control systems of cars, the design and development of reliable infrastructure and communication networks become a necessity. The recent release of the fifth-generation cellular system (5G) promises to provide a step towards reliability or a panacea. However, designing autonomous vehicle networks has more requirements due to the high mobility and traffic density of such networks and the latency and reliability requirements of applications run over such networks. To this end, we proposed a multilevel cloud system for autonomous vehicles which was built over the Tactile Internet. In addition, base stations at the edge of the radio-access network (RAN) with different technologies of antennas are used in our system. Finally, simulation results show that the proposed system with multilevel clouding can significantly reduce the round-trip latency and the network congestion. In addition, our system can be adapted in the mobility scenario.
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Lee, Juho, and Sungkwon Park. "Time-Sensitive Network (TSN) Experiment in Sensor-Based Integrated Environment for Autonomous Driving." Sensors 19, no. 5 (March 5, 2019): 1111. http://dx.doi.org/10.3390/s19051111.

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Recently, large amounts of data traffic from various sensors and image and navigation systems within vehicles are generated for autonomous driving. Broadband communication networks within vehicles have become necessary. New autonomous Ethernet networks are being considered as alternatives. The Ethernet-based in-vehicle network has been standardized in the IEEE 802.1 time-sensitive network (TSN) group since 2006. The Ethernet TSN will be revised and integrated into a subsequent version of IEEE 802.1Q-2018 published in 2018 when various new TSN-related standards are being newly revised and published. A TSN integrated environment simulator is developed in this paper to implement the main functions of the TSN standards that are being developed. This effort would minimize the performance gaps that can occur when the functions of these standards operate in an integrated environment. As part of this purpose, we analyzed the simulator to verify that the traffic for autonomous driving satisfies the TSN transmission requirements in the in-vehicle network (IVN) and the preemption (which is one of the main TSN functions) and reduces the overall End-to-End delay. An optimal guard band size for the preemption was also found for autonomous vehicles in our work. Finally, an IVN model for autonomous vehicles was designed and the performance test was conducted by configuring the traffic to be used for various sensors and electronic control units (ECUs).
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Tengg, Allan, Michael Stolz, and Joachim Hillebrand. "A Feasibility Study of a Traffic Supervision System Based on 5G Communication." Sensors 22, no. 18 (September 8, 2022): 6798. http://dx.doi.org/10.3390/s22186798.

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At present, autonomous driving vehicles are designed in an ego-vehicle manner. The vehicles gather information from their on-board sensors, build an environment model from it and plan their movement based on this model. Mobile network connections are used for non-mission-critical tasks and maintenance only. In this paper, we propose a connected autonomous driving system, where self-driving vehicles exchange data with a so-called road supervisor. All vehicles under supervision provide their current position, velocity and other valuable data. Using the received information, the supervisor provides a recommended trajectory for every vehicle, coordinated with all other vehicles. Since the supervisor has a much better overview of the situation on the road, more elaborate decisions, compared to each individual autonomous vehicle planning for itself, are possible. Experiments show that our approach works efficiently and safely when running our road supervisor on top of a popular traffic simulator. Furthermore, we show the feasibility of offloading the trajectory planning task into the network when using ultra-low-latency 5G networks.
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Brocklehurst, Callum, and Milena Radenkovic. "Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles." Journal of Sensor and Actuator Networks 11, no. 3 (July 13, 2022): 35. http://dx.doi.org/10.3390/jsan11030035.

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The increased interest in autonomous vehicles has led to the development of novel networking protocols in VANETs In such a widespread safety-critical application, security is paramount to the implementation of the networks. We view new autonomous vehicle edge networks as opportunistic networks that bridge the gap between fully distributed vehicular networks based on short-range vehicle-to-vehicle communication and cellular-based infrastructure for centralized solutions. Experiments are conducted using opportunistic networking protocols to provide data to autonomous trams and buses in a smart city. Attacking vehicles enter the city aiming to disrupt the network to cause harm to the general public. In the experiments the number of vehicles and the attack length is altered to investigate the impact on the network and vehicles. Considering different measures of success as well as computation expense, measurements are taken from all nodes in the network across different lengths of attack. The data gathered from each node allow exploration into how different attacks impact metrics including the delivery probability of a message, the time taken to deliver and the computation expense to each node. The novel multidimensional analysis including geospatial elements provides evidence that the state-of-the-art MaxProp algorithm outperforms the benchmark as well as other, more complex routing protocols in most of the categories. Upon the introduction of attacking nodes however, PRoPHET provides the most reliable delivery probability when under attack. Two different attack methods (black and grey holes) are used to disrupt the flow of messages throughout the network and the more basic protocols show that they are less consistent. In some metrics, the PRoPHET algorithm performs better when under attack due to the benefit of reduced network traffic.
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Dissertations / Theses on the topic "Autonomous Vehicle Network"

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BRUCE, WILLIAM, and OTTER EDVIN VON. "Artificial Neural Network Autonomous Vehicle : Artificial Neural Network controlled vehicle." Thesis, KTH, Maskinkonstruktion (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191192.

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This thesis aims to explain how a Artificial Neural Network algorithm could be used as means of control for a Autonomous Vehicle. It describes the theory behind the neural network and Autonomous Vehicles, and how a prototype with a camera as its only input can be designed to test and evaluate the algorithms capabilites, and also drive using it. The thesis will show that the Artificial Neural Network can, with a image resolution of 100 × 100 and a training set with 900 images, makes decisions with a 0.78 confidence level.
Denna rapport har som mal att beskriva hur en Artificiellt Neuronnatverk al- goritm kan anvandas for att kontrollera en bil. Det beskriver teorin bakom neu- ronnatverk och autonoma farkoster samt hur en prototyp, som endast anvander en kamera som indata, kan designas for att testa och utvardera algoritmens formagor. Rapporten kommer visa att ett neuronnatverk kan, med bildupplos- ningen 100 × 100 och traningsdata innehallande 900 bilder, ta beslut med en 0.78 sakerhet.
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Moore, Christopher, Dylan Crocker, Garret Coffman, and Bryce Nguyen. "Telemetry Network for Ground Vehicle Navigation." International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595750.

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ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada
This paper describes a short distance telemetry network which measures and relays time, space, and position information among a group of ground vehicles. The goal is to allow a lead vehicle to be under human control, or perhaps controlled using advanced autonomous path planning and navigation tools. The telemetry network will then allow a series of inexpensive, unmanned vehicles to follow the lead vehicle at a safe distance. Ultrasonic and infrared signals will be relayed between the vehicles, to allow the following vehicles to locate their position, and track the lead vehicle.
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Schmiterlöw, Maria. "Autonomous Path Following Using Convolutional Networks." Thesis, Linköpings universitet, Datorseende, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-78670.

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Autonomous vehicles have many application possibilities within many different fields like rescue missions, exploring foreign environments or unmanned vehicles etc. For such system to navigate in a safe manner, high requirements of reliability and security must be fulfilled. This master's thesis explores the possibility to use the machine learning algorithm convolutional network on a robotic platform for autonomous path following. The only input to predict the steering signal is a monochromatic image taken by a camera mounted on the robotic car pointing in the steering direction. The convolutional network will learn from demonstrations in a supervised manner. In this thesis three different preprocessing options are evaluated. The evaluation is based on the quadratic error and the number of correctly predicted classes. The results show that the convolutional network has no problem of learning a correct behaviour and scores good result when evaluated on similar data that it has been trained on. The results also show that the preprocessing options are not enough to ensure that the system is environment dependent.
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Darr, Matthew John. "DEVELOPMENT AND EVALUATION OF A CONTROLLER AREA NETWORK BASED AUTONOMOUS VEHICLE." UKnowledge, 2004. http://uknowledge.uky.edu/gradschool_theses/192.

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Through the work of researchers and the development of commercially availableproducts, automated guidance has become a viable option for agricultural producers.Some of the limitations of commercially available technologies are that they onlyautomate one function of the agricultural vehicle and that the systems are proprietary toa single machine model.The objective of this project was to evaluate a controller area network (CAN bus)as the basis of an automated guidance system. The prototype system utilized severalmicrocontroller-driven nodes to act as control points along a system wide CAN bus.Messages were transferred to the steering, transmission, and hitch control nodes from atask computer. The task computer utilized global positioning system data to determinethe appropriate control commands.Infield testing demonstrated that each of the control nodes could be controlledsimultaneously over the CAN bus. Results showed that the task computer adequatelyapplied a feedback control model to the system and achieved guidance accuracy levelswell within the range sought. Testing also demonstrated the system's ability tocomplete normal field operations such as headland turning and implement control.
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Magnusson, Filip. "Evaluating Deep Learning Algorithms for Steering an Autonomous Vehicle." Thesis, Linköpings universitet, Programvara och system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153450.

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With self-driving cars on the horizon, vehicle autonomy and its problems is a hot topic. In this study we are using convolutional neural networks to make a robot car avoid obstacles. The robot car has a monocular camera, and our approach is to use the images taken by the camera as input, and then output a steering command. Using this method the car is to avoid any object in front of it. In order to lower the amount of training data we use models that are pretrained on ImageNet, a large image database containing millions of images. The model are then trained on our own dataset, which contains of images taken directly by the robot car while driving around. The images are then labeled with the steering command used while taking the image. While training we experiment with using different amounts of frozen layers. A frozen layer is a layer that has been pretrained on ImageNet, but are not trained on our dataset. The Xception, MobileNet and VGG16 architectures are tested and compared to each other. We find that a lower amount of frozen layer produces better results, and our best model, which used the Xception architecture, achieved 81.19% accuracy on our test set. During a qualitative test the car avoid collisions 78.57% of the time.
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Garratt, Matthew Adam, and m. garratt@adfa edu au. "Biologically Inspired Vision and Control for an Autonomous Flying Vehicle." The Australian National University. Research School of Biological Sciences, 2008. http://thesis.anu.edu.au./public/adt-ANU20090116.154822.

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This thesis makes a number of new contributions to control and sensing for unmanned vehicles. I begin by developing a non-linear simulation of a small unmanned helicopter and then proceed to develop new algorithms for control and sensing using the simulation. The work is field-tested in successful flight trials of biologically inspired vision and neural network control for an unstable rotorcraft. The techniques are more robust and more easily implemented on a small flying vehicle than previously attempted methods.¶ Experiments from biology suggest that the sensing of image motion or optic flow in insects provides a means of determining the range to obstacles and terrain. This biologically inspired approach is applied to control of height in a helicopter, leading to the World’s first optic flow based terrain following controller for an unmanned helicopter in forward flight. Another novel optic flow based controller is developed for the control of velocity in hover. Using the measurements of height from other sensors, optic flow is used to provide a measure of the helicopters lateral and longitudinal velocities relative to the ground plane. Feedback of these velocity measurements enables automated hover with a drift of only a few cm per second, which is sufficient to allow a helicopter to land autonomously in gusty conditions with no absolute measurement of position.¶ New techniques for sensor fusion using Extended Kalman Filtering are developed to estimate attitude and velocity from noisy inertial sensors and optic flow measurements. However, such control and sensor fusion techniques can be computationally intensive, rendering them difficult or impossible to implement on a small unmanned vehicle due to limitations on computing resources. Since neural networks can perform these functions with minimal computing hardware, a new technique of control using neural networks is presented. First a hybrid plant model consisting of exactly known dynamics is combined with a black-box representation of the unknown dynamics. Simulated trajectories are then calculated for the plant using an optimal controller. Finally, a neural network is trained to mimic the optimal controller. Flight test results of control of the heave dynamics of a helicopter confirm the neural network controller’s ability to operate in high disturbance conditions and suggest that the neural network outperforms a PD controller. Sensor fusion and control of the lateral and longitudinal dynamics of the helicopter are also shown to be easily achieved using computationally modest neural networks.
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Matson, Nathan C. "Communications network design, simulation, and analysis for an autonomous unmanned vehicle system." Thesis, Monterey California. Naval Postgraduate School, 2011. http://hdl.handle.net/10945/33993.

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Approved for public release; distribution is unlimited.
In this thesis, we designed, simulated, and analyzed a wireless communications system for an autonomous unmanned vehicle system. The system used for the design context is the Unmanned Vehicle (UV) Sentry, which is a system of autonomous unmanned vehicles that can be tasked for a variety of missions that involve the patrolling and protecting of a geographical region. Accordingly, the communications network needs to allow for flexibility of the vehicle topography to enable large amounts of delay intolerant sensor data to be transmitted between nodes and a capability that allows vehicles to act as relays for other vehicles. To meet these requirements, a medium access control (MAC) relay protocol based on the IEEE 802.16 standard was developed. To evaluate the performance of the protocol, Simulink was used to model the performance of the protocol as it was implemented in a specific UV Sentry scenario. Several network parameters were chosen as factors for the model, and these factors were systematically varied to yield a full factorial design of experiments. The network quality of service parameters for the tests were then analyzed to determine the best communication network configuration for the UV Sentry scenario and to illuminate the tradeoffs between the factors.
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Tekin, Mim Kemal. "Vehicle Path Prediction Using Recurrent Neural Network." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166134.

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Vehicle Path Prediction can be used to support Advanced Driver Assistance Systems (ADAS) that covers different technologies like Autonomous Braking System, Adaptive Cruise Control, etc. In this thesis, the vehicle’s future path, parameterized as 5 coordinates along the path, is predicted by using only visual data collected by a front vision sensor. This approach provides cheaper application opportunities without using different sensors. The predictions are done by deep convolutional neural networks (CNN) and the goal of the project is to use recurrent neural networks (RNN) and to investigate the benefits of using reccurence to the task. Two different approaches are used for the models. The first approach is a single-frame approach that makes predictions by using only one image frame as input and predicts the future location points of the car. The single-frame approach is the baseline model. The second approach is a sequential approach that enables the network the usage of historical information of previous image frames in order to predict the vehicle’s future path for the current frame. With this approach, the effect of using recurrence is investigated. Moreover, uncertainty is important for the model reliability. Having a small uncertainty in most of the predictions or having a high uncertainty in unfamiliar situations for the model will increase success of the model. In this project, the uncertainty estimation approach is based on capturing the uncertainty by following a method that allows to work on deep learning models. The uncertainty approach uses the same models that are defined by the first two approaches. Finally, the evaluation of the approaches are done by the mean absolute error and defining two different reasonable tolerance levels for the distance between the prediction path and the ground truth path. The difference between two tolerance levels is that the first one is a strict tolerance level and the the second one is a more relaxed tolerance level. When using strict tolerance level based on distances on test data, 36% of the predictions are accepted for single-frame model, 48% for the sequential model, 27% and 13% are accepted for single-frame and sequential models of uncertainty models. When using relaxed tolerance level on test data, 60% of the predictions are accepted by single-frame model, 67% for the sequential model, 65% and 53% are accepted for single-frame and sequential models of uncertainty models. Furthermore, by using stored information for each sequence, the methods are evaluated for different conditions such as day/night, road type and road cover. As a result, the sequential model outperforms in the majority of the evaluation results.
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Youmans, Elisabeth A. "Neural network control of space vehicle orbit transfer, intercept, and rendezvous maneuvers." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-06062008-162101/.

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Flores, Javier Alejandro. "Autonomous vehicle navigation a comparative study of classical logic and neural network technique /." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Books on the topic "Autonomous Vehicle Network"

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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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Marsilio, Alan M. Use of Hopfield networks for system identification and failure detection in autonomous underwater vehicles. Monterey, Calif: Naval Postgraduate School, 1991.

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Young, Forrest C. Phoenix autonomous underwater vehicle (AUV): Networked control of multiple analog and digital devices using LonTalk. Monterey, Calif: Naval Postgraduate School, 1997.

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Dubanov, Aleksandr. Computer simulation in pursuit problems. ru: Publishing Center RIOR, 2022. http://dx.doi.org/10.29039/02102-6.

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Currently, computer simulation in virtual reality systems has a special status. In order for a computer model to meet the requirements of the tasks it models, it is necessary that the mathematical apparatus correctly describe the simulated phenomena. In this monograph, the simulation of pursuit problems is carried out. An adaptive modeling of the behavior of both pursuers and targets is carried out. An iterative calculation of the trajectories of the participants in the pursuit problem is carried out. The main attention is paid to the methods of pursuit and parallel rendezvous. These methods are taken as the basis of the study and are modified in the future. The scientific novelty of the study is the iterative calculation of the trajectories of the participants in the pursuit task when moving at a constant speed, while following the predicted trajectories. The predicted trajectories form a one-parameter network of continuous lines of the first order of smoothness. The predicted trajectories are calculated taking into account the restrictions on the curvature of the participant in the pursuit problem. The fact of restrictions on curvature can be interpreted as restrictions on the angular frequency of rotation of the object of the pursuit problem. Also, the novelty is the calculation of the iterative process of group pursuit of multiple targets, when targets are hit simultaneously or at specified intervals. The calculation of the parameters of the network of predicted trajectories is carried out with a curvature variation in order to achieve the desired temporal effect. The work also simulates the adaptive behavior of the pursuer and the target. The principle of behavior can be expressed on the example of a pursuer with a simple phrase: "You go to the left - I go to the left." This happens at each iteration step in terms of choosing the direction of rotation. For the purpose, the principle of adaptive behavior is expressed by the phrase: "You go to the left - I go to the right." The studies, algorithms and models presented in the monograph can be in demand in the design of autonomously controlled unmanned aerial vehicles with elements of artificial intelligence. The task models in the monograph are supplemented with many animated images, where you can see the research process. Also, the tasks have an implementation in a computer mathematics system and can be transferred to virtual reality systems if necessary.
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Oh, Jonghak. Communication/Security for Vehicular Environments of Autonomous Vehicles: In-Vehicle Security, in-vehicle Network, V2x Communication, Communication Security for Vehicular Environments. Independently Published, 2021.

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Paret, Dominique, and Hassina Rebaine. Autonomous and Connected Vehicles: Network Architectures from Legacy Networks to Automotive Ethernet. Wiley & Sons, Limited, John, 2022.

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Paret, Dominique, and Hassina Rebaine. Autonomous and Connected Vehicles: Network Architectures from Legacy Networks to Automotive Ethernet. Wiley & Sons, Incorporated, John, 2022.

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Paret, Dominique, and Hassina Rebaine. Autonomous and Connected Vehicles: Network Architectures from Legacy Networks to Automotive Ethernet. Wiley & Sons, Incorporated, John, 2022.

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Paret, Dominique, and Hassina Rebaine. Autonomous and Connected Vehicles: Network Architectures from Legacy Networks to Automotive Ethernet. Wiley & Sons, Incorporated, John, 2022.

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Repole, Donato. Research of Parallel Computing Neuro-fuzzy Networks for Unmanned Vehicles. RTU Press, 2021. http://dx.doi.org/10.7250/9789934226922.

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The Doctoral Thesis illustrates the author’s research in the field of VHDL based ‘neuro-fuzzy controllers’. The Thesis examines a novel software tool for the high-level ‘neuro-fuzzy controller’ description capable of executing controller simulations, optimisation tasks, performing learning / training tasks, and exporting the controller in VHDL code. The author introduces a design strategy that is looking for developing solutions for complex controller architecture of mobile robotic vehicles (of any nature) or even for multiple industrial application. This work enables further investigative research into autonomous robotics, particularly into the physical implementation of an autonomous aerial unmanned vehicle from an inexpensive RC plane.
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Book chapters on the topic "Autonomous Vehicle Network"

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Tahir, Muhammad Naeem, and Marcos Katz. "ITS Performance Evaluation in Direct Short-Range Communication (IEEE 802.11p) and Cellular Network (5G) (TCP vs UDP)." In Towards Connected and Autonomous Vehicle Highways, 257–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66042-0_10.

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Yim, Qi Yao, and Kester Yew Chong Wong. "Simulation-Based Analysis of a Network Model for Autonomous Vehicles with Vehicle-to-Vehicle Communication." In IRC-SET 2018, 389–400. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9828-6_31.

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Cheung, YauKa, Meikang Qiu, and Meiqin Liu. "Autonomous Vehicle Communication in V2X Network with LoRa Protocol." In Lecture Notes in Computer Science, 398–410. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34139-8_40.

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Maciel-Pearson, Bruna G., Patrice Carbonneau, and Toby P. Breckon. "Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation Within the Forest Canopy." In Towards Autonomous Robotic Systems, 147–58. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96728-8_13.

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Furukawa, Hiroto, Masashi Saito, Yuichi Tokunaga, and Ryozo Kiyohara. "A Method for Vehicle Control at T-Junctions for the Diffusion Period of Autonomous Vehicles." In Advances in Network-Based Information Systems, 295–305. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65521-5_25.

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Sharma, Garima, Praveen Kumar Singh, and Laxmi Shrivastava. "Autonomous Vehicle Power Scavenging Analysis for Vehicular Ad Hoc Network." In International Conference on Intelligent Computing and Smart Communication 2019, 879–87. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0633-8_91.

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Babu Naik, G., Prerit Ameta, N. Baba Shayeer, B. Rakesh, and S. Kavya Dravida. "Convolutional Neural Network Based on Self-Driving Autonomous Vehicle (CNN)." In Innovative Data Communication Technologies and Application, 929–43. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7167-8_68.

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James, Alice, Avishkar Seth, and S. C. Mukhopadhyay. "Autonomous Ground Vehicle for Off-the-Road Applications Based on Neural Network." In Algorithms for Intelligent Systems, 285–93. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3368-3_27.

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Barauskas, Andrius, Agnė Brilingaitė, Linas Bukauskas, Vaida Čeikutė, Alminas Čivilis, and Simonas Šaltenis. "Semi-synthetic Data and Testbed for Long-Distance E-Vehicle Routing." In New Trends in Database and Information Systems, 61–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85082-1_6.

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AbstractElectric and autonomous mobility will increasingly rely on advanced route planning algorithms. Robust testing of these algorithms is dependent on the availability of large realistic data sets. Such data sets should capture realistic time-varying traffic patterns and corresponding travel-time and energy-use predictions. Ideally, time-varying availability of charging infrastructure and vehicle-specific charging-power curves should be included in the data to support advanced planning.We contribute with a modular testbed architecture including a semi-synthetic data generator that uses a state-of-the-art traffic simulator, real traffic distribution patterns, EV-specific data, and elevation data to generate time-dependent travel-time and energy-use weights in a road-network graph. The experimental study demonstrates that the testbed can reproduce travel-time and energy-use patterns for long-distance trips similar to commercially available services.
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Vu, Van-Son, and Duc-Nam Nguyen. "Application of MobileNet-SSD Deep Neural Network for Real-Time Object Detection and Lane Tracking on an Autonomous Vehicle." In Advances in Asian Mechanism and Machine Science, 559–65. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91892-7_53.

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Conference papers on the topic "Autonomous Vehicle Network"

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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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Compere, Marc, Garrett Holden, Otto Legon, and Roberto Martinez Cruz. "MoVE: A Mobility Virtual Environment for Autonomous Vehicle Testing." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-10936.

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Abstract Autonomous vehicle researchers need a common framework in which to test autonomous vehicles and algorithms along a realism spectrum from simulation-only to real vehicles and real people. The community needs an open-source, publicly available framework, with source code, in which to develop, simulate, execute, and post-process multi-vehicle tests. This paper presents a Mobility Virtual Environment (MoVE) for testing autonomous system algorithms, vehicles, and their interactions with real and simulated vehicles and pedestrians. The result is a network-centric framework designed to represent multiple real and multiple virtual vehicles interacting and possibly communicating with each other in a common coordinate frame with a common timestamp. This paper presents a literature review of comparable autonomous vehicle softwares, presents MoVE concepts and architecture, and presents three experimental tests with multiple virtual and real vehicles, with real pedestrians. The first scenario is a traffic wave simulation using a real lead vehicle and 3 real follower vehicles. The second scenario is a medical evacuation scenario with 2 real pedestrians and 1 real vehicles. Real pedestrians are represented using live-GPS-followers streaming GPS position from mobile phones over the cellular network. Time-history and spatial plots of real and virtual vehicles are presented with vehicle-to-vehicle distance calculations indicating where and when potential collisions were detected and avoided. The third scenario highlights the avoid() behavior successfully avoiding other virtual vehicles and 1 real pedestrian in a small outdoor area. The MoVE set of concepts and interfaces are implemented as open-source software available for use and customization within the autonomous vehicle community. MoVE is freely available under the GPLv3 open-source license at gitlab.com/comperem/move.
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Tarmizi, Izzah Amani, and Azrina Abd Aziz. "Vehicle Detection Using Convolutional Neural Network for Autonomous Vehicles." In 2018 International Conference on Intelligent and Advanced System (ICIAS). IEEE, 2018. http://dx.doi.org/10.1109/icias.2018.8540563.

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Papalia, Alan, and John Leonard. "Network Localization Based Planning for Autonomous Underwater Vehicles with Inter-Vehicle Ranging." In 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV). IEEE, 2020. http://dx.doi.org/10.1109/auv50043.2020.9267910.

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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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Mieyeville, Fabien, David Navarro, Olivier Bareille, and Mateusz Zielinski. "Autonomous Wireless Sensor Network for Distributed Active Control." In 2017 IEEE Vehicle Power and Propulsion Conference (VPPC). IEEE, 2017. http://dx.doi.org/10.1109/vppc.2017.8330909.

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Shit, Rathin Chandra, and Suraj Sharma. "Localization for Autonomous Vehicle: Analysis of Importance of IoT Network Localization for Autonomous Vehicle Applications." In 2018 International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC). IEEE, 2018. http://dx.doi.org/10.1109/aespc44649.2018.9033329.

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Kang, Hyunjae, Byung Il Kwak, Young Hun Lee, Haneol Lee, Hwejae Lee, and Huy Kang Kim. "Car Hacking and Defense Competition on In-Vehicle Network." In Third International Workshop on Automotive and Autonomous Vehicle Security. Reston, VA: Internet Society, 2021. http://dx.doi.org/10.14722/autosec.2021.23035.

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Scholz-Reiter, Bernd, Henning Rekersbrink, Bernd-Ludwig Wenning, and Thomas Makuschewitz. "A Survey of Autonomous Control Algorithms by Means of Adapted Vehicle Routing Problems." In ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59077.

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The German Collaborative Research Centre 637 “Autonomous Cooperating Logistic Processes – A Paradigm Shift and its Limitations”, develops, among other things, autonomous routing algorithms for transport networks. The discussed algorithm is designed to match goods and vehicles and to continuously make route decisions within a dynamic transport network. Here, each object makes its own decisions. It is called Distributed Logistics Routing Protocol – DLRP. Because of obvious similarities to the Vehicle Routing Problem (VRP), one question arises for both practitioners and researchers: How efficient is this protocol compared to traditional, established algorithms or in comparison to the optimal solution? This article tries to answer this question, which appears simple on the first and challenging on the second view.
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Kebkal, Konstantin G., Veronika K. Kebkal, Oleksiy G. Kebkal, Dmitry D. Minaev, Roman Leonenkov, and Andrey S. Korytko. "An Example of Underwater Acoustic Network based on Modems Incorporating Open-Source Networking Software Framework." In 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV). IEEE, 2018. http://dx.doi.org/10.1109/auv.2018.8729762.

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Reports on the topic "Autonomous Vehicle Network"

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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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Larkin, Lance, Thomas Carlson, William D’Andrea, Andrew Johnson, and Natalie Myers. Network development and autonomous vehicles : a smart transportation testbed at Fort Carson : project report summary and recommendations. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/45941.

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In this work, a smart transportation testbed was utilized at Fort Carson to demonstrate three use cases for the primary purpose to plan, develop, demonstrate, and employ autonomous vehicle technologies at military installations and within the surrounding communities to evaluate commercially available Connected and Automated Vehicles and the potential to reduce base operating costs, improve safety and quality of life for military service members and their families, and deliver services more efficiently and effectively. To meet this purpose, an automated vehicle shuttle, an unmanned aerial system, and a wireless network were used and tested during the project. Results for the automated shuttle indicated that despite the quantity of data generated by operations, the contractors may not be ready to share information in a readily usable format. Additionally, successful use by the public is predicated on both knowing their mobility pat-terns and staff members promoting trust in the technology to prospective riders. Results for the unmanned aerial system showed successful identification of foreign object debris and runway cracks at the airfield. The wireless network is now operational and is used for additional work which utilizes the installed traffic cameras.
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Wang, Chaojie, Yu Wang, and Srinivas Peeta. Development of Dynamic Network Traffic Simulator for Mixed Traffic Flow under Connected and Autonomous Vehicle Technologies. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317564.

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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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Fratantoni, David M. Development of Oceanographic Sampling Networks Using Autonomous Gliding Vehicles. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada629092.

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Fratantoni, David M. Development of Oceanographic Sampling Networks Using Autonomous Gliding Vehicles. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada629472.

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She, Ruifeng, and Yanfeng Ouyang. Generalized Link-Cost Function and Network Design for Dedicated Truck-Platoon Lanes to Improve Energy, Pavement Sustainability, and Traffic Efficiency. Illinois Center for Transportation, November 2021. http://dx.doi.org/10.36501/0197-9191/21-037.

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Recent development of autonomous and connected trucks (ACT) has provided the freight industry with the option of using truck platooning to improve fuel efficiency, traffic throughput, and safety. However, closely spaced and longitudinally aligned trucks impose frequent and concentrated loading on pavements, which often accelerates pavement deterioration and increases the life cycle costs for the highway agency. Also, effectiveness of truck platooning can be maximized only in dedicated lanes; and its benefits and costs need to be properly balanced between stakeholders. This paper proposes a network-design model to optimize (i) placement of dedicated truck-platoon lanes and toll price in a highway network, (ii) pooling and routing of ACT traffic from multiple origins and destinations to utilize these lanes, and (iii) configuration of truck platoons within these lanes (e.g., lateral displacements and vehicle separations). The problem is formulated as an integrated bi-level optimization model. The upper level makes decisions on converting existing highway lanes into dedicated platoon lanes, as well as setting user fees. The lower-level decisions are made by independent shippers regarding the choice of routes and use of platoon lanes vs. regular lanes; and they collectively determine truck traffic in all lanes. Link-cost functions for platoon lanes are obtained by simultaneously optimizing, through dynamic programming, pavement-rehabilitation activities and platoon configuration in the pavement's life cycle. A numerical case study is used to demonstrate the applicability and performance of the proposed model framework over the Illinois freeway system. It is shown that the freight traffic is effectively channelized on a few corridors of platoon lanes and, by setting proper user fees to cover pavement-rehabilitation costs, systemwide improvements for both freight shippers and highway agencies can be achieved.
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Razdan, Rahul. Unsettled Issues Regarding Autonomous Vehicles and Open-source Software. SAE International, April 2021. http://dx.doi.org/10.4271/epr2021009.

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As automobiles morph from stand-alone mechanical objects to highly connected, autonomous systems with increasing amounts of electronic components. To manage these complex systems, some semblance of in-car decision-making is also being built and networked to a cloud architecture. This cloud can also enable even deeper capabilities within the broader automotive ecosystem. Unsettled Issues Regarding Autonomous Vehicles and Open-source Software introduces the impact of software in advanced automotive applications, the role of open-source communities in accelerating innovation, and the important topic of safety and cybersecurity. As electronic functionality is captured in software and a bigger percentage of that software is open-source code, some critical challenges arise concerning security and validation.
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Kilfoyle, Daniel B., and Lee Freitag. Application of Spatial Modulation to the Underwater Acoustic Communication Component of Autonomous Underwater Vehicle Networks. Fort Belvoir, VA: Defense Technical Information Center, August 2005. http://dx.doi.org/10.21236/ada437524.

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Kilfoyle, Daniel B. Application of Spatial Modulation to the Underwater Acoustic Communication Component of Autonomous Underwater Vehicle Networks. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada633556.

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