Journal articles on the topic 'Autonomous Vehicle Network'
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1
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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Saber, O., and T. Mazri. "SECURITY OF AUTONOMOUS VEHICLES: 5G IOV (INTERNET OF VEHICLES) ENVIRONMENT." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W3-2022 (December 2, 2022): 157–63. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w3-2022-157-2022.
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Abstract. An autonomous vehicle is designed to move partially or totally without the intervention of the driver. It is a system equipped with sensors, communication and processing units that make it able to monitor and analyse traffic information in real time to improve road safety. In addition, Internet of Vehicles is the latest technology dedicated to autonomous vehicles, the integration of this technology with 5G serves as a platform to interconnect sensors, vehicles, infrastructure, pedestrian, and network. Hence, the 5G Internet of Vehicles environment provides significant benefits, including increased security, high reliability, wide communication coverage and low service latency. On the other hand, due to the ubiquity of network connectivity, it also presents serious confidentiality, integrity, and availability issues for autonomous vehicles. This paper provides an overview of the autonomous vehicle concept by highlighting its technologies and the 5G IoV environment, presenting some susceptible attacks that can touch the security of this environment, and some good practices to ensure the autonomous vehicle security.
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Fang, Ruoyu, and Cheng Cai. "Computer vision based obstacle detection and target tracking for autonomous vehicles." MATEC Web of Conferences 336 (2021): 07004. http://dx.doi.org/10.1051/matecconf/202133607004.
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Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.
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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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Mitrović, Miloš, Vladimir Popović, and Dragan Stamenković. "Implementation of traffic sign recognition on the scaled vehicle model." Industrija 50, no. 2 (2022): 51–60. http://dx.doi.org/10.5937/industrija50-41958.
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The popularity of autonomous vehicles has grown in the past few years as autonomous systems are more and more present on vehicles. The most accessible way for students of mechanical and software engineers to learn about autonomous vehicles is by applying algorithms and systems necessary for autonomous driving on the scaled vehicle model. These models are, as in this case, and are equipped with all systems necessary for autonomous driving, such as a four-wheel drive powertrain, a suspension system, an electrically controlled steering system, a brain-computer and a camera. The goal of projects such as this one is to make the vehicle capable of autonomous driving on a designated track, obeying regular traffic rules and signs (for example, the vehicle has to perform a full stop when it approaches the stop sign). To make this possible, it is necessary for a vehicle to "know" which traffic sign is nearby, i.e., traffic sign recognition is required. For this purpose, traffic sign recognition is done by an artificial neural network. The training process of the proper artificial neural network will be shown in this paper.
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Zhi-Yong Yang, Zhi-Yong Yang, Zhen-Ping Mou Zhi-Yong Yang, Long Wang Zhen-Ping Mou, and Yu Zhou Long Wang. "Application of Lightweight Neural Network in Speed Bump Recognition of Autonomous Vehicle." 電腦學刊 33, no. 5 (October 2022): 029–38. http://dx.doi.org/10.53106/199115992022103305003.
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<p>Vibration occurs when a vehicle passes through a speed bump, which has different intensities at different sizes and speeds. The recognition of speed bump type is an important step for vehicle to adjust speed automatically in time in automatic driving, which helps to improve the safety and comfort of passengers. In this paper, we put forward the technical requirements of speed bump image acquisition in automatic driving scene, and establish the speed bump image dataset. Based on improved EfficientNet basic block, we construct a lightweight convolutional neural network integrating edge detection, which is named Edge-Efficientnet. The experimental results show that its accuracy is improved by 3.3% and the model size is reduced by 53% compared with EfficientNetB0 model. In terms of computing speed, the model meets the real-time performance requirements. The Edge-Efficientnet model can be applied to the comfortable speed adjustment of autonomous vehicles passing through speed bump.</p> <p> </p>
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Savu, Tom, and Bogdan Alexandru Jugravu. "Remanent battery capacity estimation for autonomous ground industrial vehicles." MATEC Web of Conferences 290 (2019): 02009. http://dx.doi.org/10.1051/matecconf/201929002009.
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When travelling in an industrial system for completing their assigned tasks, autonomous ground vehicles must estimate the remanent capacity of their batteries and decide if they are able to assume the next task and afterward travel to the charging or replacement station. The amount of energy needed for moving on a certain distance depends on a set of parameters belonging to the vehicle, to the runway and to the vehicle’s trajectory. The paper proposes a model for estimating the remaining capacity of the batteries after a certain distance would be covered by a vehicle. Parameters values were obtained by simulation, capacity loss was computed using the proposed model and then a neural network was taught to perform the estimation. The neural network was further used to simulate the situation when a vehicle is estimating the needed capacity before accepting a task to be performed. The results proved that the model and the network, even developed using low data volume and processing time, are able to provide accurate enough estimations and are able to allow future developments.
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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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Kashyap, Aravind R. "Autonomous Vehicular Corridor using Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1254–58. http://dx.doi.org/10.22214/ijraset.2021.35272.
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This project considers the operational impact of Autonomous Vehicles by creating a corridor using the latest network available. The behaviour of these vehicles entering the corridor is monitored at the macroscopic level by modifying the data which can be extracted from the vehicle. This data is made to learn using machine learning called the Time Series Neural Network and the data is used as a parameter to make the vehicles Autonomous. The project resolves the location, develops and demonstrates the collision avoidance of the vehicles using Artificial Intelligence. Autonomous means the vehicles will be able to learn to act accordingly without human intervention
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Pauwels, Alex, Nadia Pourmohammad-Zia, and Frederik Schulte. "Safety and Sustainable Development of Automated Driving in Mixed-Traffic Urban Areas—Considering Vulnerable Road Users and Network Efficiency." Sustainability 14, no. 20 (October 19, 2022): 13486. http://dx.doi.org/10.3390/su142013486.
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Next to environmental aspects, establishing areas for safe and economically viable automated driving in mixed-traffic settings is one major challenge for sustainable development of Autonomous Vehicles (AVs). This work investigates safety in the interactions between AVs, human-driven vehicles, and vulnerable road users, including cyclists and pedestrians, within a simulated urban environment in the Dutch city of Rotterdam. New junction and pedestrian models are introduced, and virtual AVs with an occlusion-aware driving system are deployed to deliver cargo autonomously. The safety of applying this autonomous cargo delivery service is assessed using a large set of Surrogate Safety Indicators (SSIs). Furthermore, Macroscopic Fundamental Diagrams (MFDs) and travel time loss are incorporated to evaluate the network efficiency. By assessing the impact of various measures involving Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Everything (V2X) communications, infrastructure modifications, and driving behavior, we show that traffic safety and network efficiency can be achieved in a living lab setting for the considered case. Our findings further suggest that V2X gets implemented, new buildings are not placed close to intersections, and the speed limit of non-arterial roads is lowered.
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Wong, Joseph, Goldie Nejat, Robert Fenton, and Beno Benhabib. "A neural-network approach to high-precision docking of autonomous vehicles/platforms." Robotica 25, no. 4 (February 13, 2007): 479–92. http://dx.doi.org/10.1017/s0263574707003359.
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SUMMARYIn this paper, a Neural-Network- (NN) based guidance methodology is proposed for the high-precision docking of autonomous vehicles/platforms. The novelty of the overall online motion-planning methodology is its applicability to cases that do not allow for the direct proximity measurement of the vehicle's pose (position and orientation). In such instances, a guidance technique that utilizes Line-of-Sight- (LOS) based task-space sensory feedback is needed to minimize the detrimental impact of accumulated systematic motion errors. Herein, the proposed NN-based guidance methodology is implemented during the final stage of the vehicle's motion (i.e., docking). Systematic motion errors, which are accumulated after a long-range motion are reduced iteratively by executing corrective motion commands generated by the NN until the vehicle achieves its desired pose within random noise limits. The proposed guidance methodology was successfully tested via simulations for a 6-dof (degree-of-freedom) vehicle and via experiments for a 3-dof high-precision planar platform.
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Hunt, K. J., R. Haas, and J. C. Kalkkuhl. "Local Controller Network for Autonomous Vehicle Steering." IFAC Proceedings Volumes 29, no. 1 (June 1996): 8101–6. http://dx.doi.org/10.1016/s1474-6670(17)58997-1.
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Hunt, K. J., R. Haas, and J. C. Kalkkuhl. "Local controller network for autonomous vehicle steering." Control Engineering Practice 4, no. 8 (August 1996): 1045–51. http://dx.doi.org/10.1016/0967-0661(96)00104-9.
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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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Kim, Minji, Hong Ki Kim, and Sang Hyun Lee. "A Distributed Cooperative Localization Strategy in Vehicular-to-Vehicular Networks." Sensors 20, no. 5 (March 4, 2020): 1413. http://dx.doi.org/10.3390/s20051413.
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This work develops a distributed message-passing approach to cooperative localization for autonomous mobile vehicles that communicate via mm-wave wireless connection in vehicle-to-vehicle networks. Vehicles in the network obtain the measurement information about the relative distance and the angle of arrival from the mm-wave connections made with each other. Some vehicles may obtain knowledge about their absolute position information of different quality, for example, via additional localization feature. The main objective is to estimate the locations of all vehicles using reciprocal exchanges of simple information called a message in a distributed and autonomous way. A simulation is developed to examine the performance of the localization and navigation of vehicles under various network configurations. The results show that it does provide better positioning results in most cases and there are also several cases where the use of the cooperative technique adapts to design parameters such as accuracies of measurement equipment, and initial position estimates, that can affect the localization performance.
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Nie, Xiaobo, Chuan Min, Yongjun Pan, Ke Li, and Zhixiong Li. "Deep-Neural-Network-Based Modelling of Longitudinal-Lateral Dynamics to Predict the Vehicle States for Autonomous Driving." Sensors 22, no. 5 (March 4, 2022): 2013. http://dx.doi.org/10.3390/s22052013.
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Multibody models built in commercial software packages, e.g., ADAMS, can be used for accurate vehicle dynamics, but computational efficiency and numerical stability are very challenging in complex driving environments. These issues can be addressed by using data-driven models, owing to their robust generalization and computational speed. In this study, we develop a deep neural network (DNN) based model to predict longitudinal-lateral dynamics of an autonomous vehicle. Dynamic simulations of the autonomous vehicle are performed based on a semirecursive multibody method for data acquisition. The data are used to train and test the DNN model. The DNN inputs include the torque applied on wheels and the vehicle’s initial speed that imitates a double lane change maneuver. The DNN outputs include the longitudinal driving distance, the lateral driving distance, the final longitudinal velocities, the final lateral velocities, and the yaw angle. The predicted vehicle states based on the DNN model are compared with the multibody model results. The accuracy of the DNN model is investigated in detail in terms of error functions. The DNN model is verified within the framework of a commercial software package CarSim. The results demonstrate that the DNN model predicts accurate vehicle states in real time. It can be used for real-time simulation and preview control in autonomous vehicles for enhanced transportation safety.
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Kumar, Dr A. Dinesh. "Underwater Gripper using Distributed Network and Adaptive Control." Journal of Electrical Engineering and Automation 2, no. 1 (March 25, 2020): 43–49. http://dx.doi.org/10.36548/jeea.2020.1.005.
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Underwater identification and grasping of objects is a major challenge faced by the marine engineers even today. Nowadays, almost all underwater operations are either autonomous or tele-operated. In fact remotely operated vehicles (ROVs) are used to deal with inspection tasks and industrial maintenance whenever there is need for intervention. However, the field of autonomous underwater vehicle (AUV) is a blooming filed with research involving proper moving base control and forces interacting which leads to complicated configuration. Hence the presented work is focused implementation of end-effector with appropriate control and signal processing resulting in autonomous manipulation of movement under water.
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Obaid, Mohammed, and Arpad Torok. "Macroscopic Traffic Simulation of Autonomous Vehicle Effects." Vehicles 3, no. 2 (April 29, 2021): 187–96. http://dx.doi.org/10.3390/vehicles3020012.
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The increasing worldwide demand on urban road transportation systems requires more restrictive measures and policies to reduce congestion, time delay and pollution. Autonomous vehicle mobility services, both shared and private, are possibly a good step towards a better road transportation future. This article aims to study the expected impact of private autonomous vehicles on road traffic parameters from a macroscopic level. The proposed methodology focuses on finding the different effects of different combinations of autonomous vehicle penetration and Passenger Car Units (PCU) on the chosen road traffic model. Four parameters are studied: traveled daily kilometers, daily hours, total daily delay and average network speed. The analysis improves the four parameters differently by implementing autonomous vehicles. The parameter total delay has the most significant reduction. Finally, several mathematical models are developed for the percentage of improvement for each chosen parameter.
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Jain, Ankur, and B. K. Roy. "Online Control of a Nonlinear Autonomous Vehicle in the Presence of Network Delay." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (December 31, 2019): 344–51. http://dx.doi.org/10.5373/jardcs/v11sp12/20193230.
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Aldhyani, Theyazn H. H., and Hasan Alkahtani. "Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity." Sensors 22, no. 1 (January 4, 2022): 360. http://dx.doi.org/10.3390/s22010360.
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Rapid technological development has changed drastically the automotive industry. Network communication has improved, helping the vehicles transition from completely machine- to software-controlled technologies. The autonomous vehicle network is controlled by the controller area network (CAN) bus protocol. Nevertheless, the autonomous vehicle network still has issues and weaknesses concerning cybersecurity due to the complexity of data and traffic behaviors that benefit the unauthorized intrusion to a CAN bus and several types of attacks. Therefore, developing systems to rapidly detect message attacks in CAN is one of the biggest challenges. This study presents a high-performance system with an artificial intelligence approach that protects the vehicle network from cyber threats. The system secures the autonomous vehicle from intrusions by using deep learning approaches. The proposed security system was verified by using a real automatic vehicle network dataset, including spoofing, flood, replaying attacks, and benign packets. Preprocessing was applied to convert the categorical data into numerical. This dataset was processed by using the convolution neural network (CNN) and a hybrid network combining CNN and long short-term memory (CNN-LSTM) models to identify attack messages. The results revealed that the model achieved high performance, as evaluated by the metrics of precision, recall, F1 score, and accuracy. The proposed system achieved high accuracy (97.30%). Along with the empirical demonstration, the proposed system enhanced the detection and classification accuracy compared with the existing systems and was proven to have superior performance for real-time CAN bus security.
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Wang, Chang, Xia Zhao, Rui Fu, and Zhen Li. "Research on the Comfort of Vehicle Passengers Considering the Vehicle Motion State and Passenger Physiological Characteristics: Improving the Passenger Comfort of Autonomous Vehicles." International Journal of Environmental Research and Public Health 17, no. 18 (September 18, 2020): 6821. http://dx.doi.org/10.3390/ijerph17186821.
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Comfort is a significant factor that affects passengers’ choice of autonomous vehicles. The comfort of an autonomous vehicle is largely determined by its control algorithm. Therefore, if the comfort of passengers can be predicted based on factors that affect comfort and the control algorithm can be adjusted, it can be beneficial to improve the comfort of autonomous vehicles. In view of this, in the present study, a human-driven experiment was carried out to simulate the typical driving state of a future autonomous vehicle. In the experiment, vehicle motion parameters and the comfort evaluation results of passengers with different physiological characteristics were collected. A single-factor analysis method and binary logistic regression analysis model were used to determine the factors that affect the evaluation results of passenger comfort. A passenger comfort prediction model was established based on the bidirectional long short-term memory network model. The results demonstrate that the accuracy of the passenger comfort prediction model reached 84%, which can provide a theoretical basis for the adjustment of the control algorithm and path trajectory of autonomous vehicles.
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Pan, Ruoyu, Lihua Jie, Xinyue Zhang, Shengli Pang, Honggang Wang, and Zhaoying Wei. "A V2P Collision Risk Warning Method based on LSTM in IOV." Security and Communication Networks 2022 (July 31, 2022): 1–12. http://dx.doi.org/10.1155/2022/7507573.
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With the evolution of communication networks, the Internet of Vehicles (IOV) continues to accelerate the safe and rapid development of autonomous vehicles. Vehicle-to-Pedestrian (V2P) communication is a key technology in autonomous vehicles and a potential solution to realize collaborative intelligence between vehicles and pedestrians. However, the existing V2P communication early warning system does not consider the uncertainty of pedestrian trajectory, and the determination of the collision area is limited to a single point, resulting in an inaccurate system judgment and limited improvement of traffic efficiency. This paper designs a new autonomous-oriented V2P communication network architecture and completes a V2P communication early warning system based on Long Range (LoRa). A V2P anticollision model is established, and a new V2P collision risk early warning method is proposed. In this method, danger index is introduced into the early warning of collision between pedestrian and vehicle. The long short-term memory (LSTM) artificial neural network is used to predict the pedestrian’s trajectory, so as to deduce the pedestrian-vehicle collision risk area when the pedestrian trajectory is uncertain. Meanwhile, the confidence probability is used to judge whether the pedestrian and vehicle are warned. The simulation shows that the V2P collision risk warning method proposed in this paper has good performance, which can accurately warn the pedestrian and vehicle under different vehicle speeds and Global Positioning System (GPS) positioning errors. At the same time, it reflects the characteristics of intelligence brought by using LSTM methods. Using the V2P communication early warning system based on LoRa to verify the experimental results show that when the GPS positioning accuracy is submeter level, the prediction accuracy is greater than 98%. The results of the proposed method show good performance and high detection rate.
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CORSER, GEORGE, HUIRONG FU, MATHIAS MASASABI, and LARS KIVARI. "Properties of Vehicle Network Privacy." Michigan Academician 44, no. 3 (January 1, 2017): 287–303. http://dx.doi.org/10.7245/0026-2005-44.3.287.
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ABSTRACT Contemporary automobiles utilize a range of safety devices, such as seatbelts and airbags, to reduce injuries in accidents. Future vehicles, especially autonomous cars, will also utilize computer networks to avoid accidents. These vehicular ad hoc networks, VANETs, may one day save thousands of lives and billions of dollars, reduce fuel consumption and pollution, and expand ubiquitous connectivity and mobile application functionality to the world's roadways. One problem: privacy. VANETs may expose motorists to surveillance by eavesdroppers, from casual stalkers to Big Brother. The problem has perplexed researchers for decades, perhaps partly because the desired properties of vehicle network privacy have not been sufficiently defined. The purpose of this paper is to provide a taxonomy to classify privacy properties in vehicular contexts.
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Tanveer, Muhammad, Faizan Ahmad Kashmiri, Huimin Yan, Tianshi Wang, and Huapu Lu. "A Cellular Automata Model for Heterogeneous Traffic Flow Incorporating Micro Autonomous Vehicles." Journal of Advanced Transportation 2022 (January 28, 2022): 1–21. http://dx.doi.org/10.1155/2022/8815026.
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Despite the fact that significant research efforts have been made to the traffic flow theory of autonomous vehicles and manual vehicles, few existing studies have incorporated different modes of both vehicles in their analysis. In this study, we develop a cellular automata simulation model to investigate the impact of different modes of autonomous vehicles (autonomous car, autonomous bus, and autonomous micro car) and conventional vehicles (manual car, manual bus, and manual micro car) on the characteristics of traffic flow. A new type of autonomous mode, i.e., autonomous micro car, is investigated in the model to study the effects of this vehicle mode on the overall capacity of the network. Furthermore, two types of lane-changing behavior, i.e., aggressive lane changing and polite lane changing, are incorporated into the model. The results reveal that micro cars (manual and autonomous) have the potential to reduce traffic congestions and increase the capacity or flow rate (vehicles/hour) of the road. Where the average vehicle occupancy is less than 2, if autonomous micro cars are deployed alongside autonomous cars, the flow rate (vehicles/hour) can be increased significantly. The results highlight the significance of the autonomous micro cars to traffic flow, passenger occupancy, and road capacity.
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Park, Chulsun, and Sungkwon Park. "Performance Evaluation of Zone-Based In-Vehicle Network Architecture for Autonomous Vehicles." Sensors 23, no. 2 (January 6, 2023): 669. http://dx.doi.org/10.3390/s23020669.
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In recent years, various functions such as advanced driver assistance systems (ADAS) and infotainment systems are being mounted in vehicles for safety and convenience to drivers. Among the various functions, autonomous driving-related technologies are being added to all vehicles, from low options to high options. For autonomous driving, hundreds of new electronic control units (ECUs) including various advanced sensors would be needed. Adding more ECUs would enhance safety and convenience for the driver. On the other hand, wiring between these ECUs would be more complex and heavier. The wiring harness is essential for communication and power supply. Currently, the in-vehicle network (IVN) uses the domain-based IVN architecture (DIA) that separates ECUs into domains based on their functions. Recently, in order to minimize the complexity of wiring harness and IVN, zone-based IVN architecture (ZIA) that groups ECUs according to their physical locations is attracting attention. In this paper, we propose a new DIA and ZIA for autonomous driving in the context of time-sensitive networking (TSN). These two new IVN architectures are simulated using the OMNeT++ network simulator. In the simulation process, a mid-size vehicle is assumed. It is shown in this paper that ZIA not only reduces wiring harnesses in both lengths and weights by approximately 24.6% compared to the DIAs, but also reduces data transmission delay.
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Fujii, Teruo, and Tamaki Ura. "Control with Neural Network For Autonomous Underwater Vehicle." Journal of the Society of Naval Architects of Japan 1989, no. 166 (1989): 503–11. http://dx.doi.org/10.2534/jjasnaoe1968.1989.166_503.
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S. Raj, Jennifer. "Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors." March 2021 3, no. 2 (July 17, 2021): 93–106. http://dx.doi.org/10.36548/jucct.2021.2.003.
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The advent of autonomous vehicles is indeed a potential field of research in today's situation. Connected Vehicles (CV) have received a lot of attention in the last decade, which has resulted in CV as a Service (CVaaS). With the advent of taxi services, there is a need for or demand for robust, seamless, and secure information transmission between the vehicles connected to a vehicular network. Thus, the concept of vehicular networking is transformed into novel concept of autonomous and connected vehicles. These autonomous vehicles will serve as a better experience by providing instant information from the vehicles via congestion reduction. The significant drawback faced by the invention of autonomous vehicles is the malicious floor of intruders, who tend to mislead the communication between the vehicles resulting in the compromised smart devices. To address these concerns, the best methodology that will protect and secure the control system of the autonomous vehicle in real time is blockchain. This research work proposes a blockchain framework in order to address the security challenges in autonomous vehicles. This research work enhances the security of smart vehicles thereby preventing intruders from accessing the vehicular network. To validate the suggested technique, money security criteria such as changing stored user ratings, probabilistic authentication scenarios, smart device compromise, and bogus user requests were employed. The observed findings have been documented and analysed, revealing an 82% success rate.
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Guo, Jinghua, Yugong Luo, and Keqiang Li. "Adaptive coordinated collision avoidance control of autonomous ground vehicles." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 232, no. 9 (May 22, 2018): 1120–33. http://dx.doi.org/10.1177/0959651818774991.
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This article presents a novel coordinated nonlinear adaptive backstepping collision avoidance control strategy for autonomous ground vehicles with uncertain and unmodeled terms. A nonlinear vehicle collision avoidance vehicle model which describes the coupled lateral and longitudinal dynamic features of autonomous ground vehicles is constructed. Then, a modified artificial potential field approach which can ensure that the total potential field of the target is goal minimum, is proposed to produce a collision-free trajectory for autonomous ground vehicles in real-time. Furthermore, in order to handle with the features of coupled and parameter uncertainties of autonomous ground vehicles, an adaptive neural network–based backstepping trajectory tracking control approach is proposed for collision avoidance control system of autonomous ground vehicles, and the stability of this proposed control system is proven by the Lyapunov theory. Finally, the co-simulation and experimental tests are implemented and the results show that the proposed collision avoidance control strategy has excellent tracking performance.
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Kim, Dae-Young, Minwoo Jung, and Seokhoon Kim. "An Internet of Vehicles (IoV) Access Gateway Design Considering the Efficiency of the In-Vehicle Ethernet Backbone." Sensors 21, no. 1 (December 25, 2020): 98. http://dx.doi.org/10.3390/s21010098.
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A vehicular network is composed of an in-vehicle network (IVN) and Internet of Vehicles (IoV). IVN exchanges information among in-vehicle devices. IoV constructs Vehicle-to-X (V2X) networks outside vehicles and exchanges information among V2X elements. These days, in-vehicle devices that require high bandwidth is increased for autonomous driving services. Thus, the spread of data for vehicles is exploding. This kind of data is exchanged through IoV. Even if the Ethernet backbone of IVN carries a lot of data in the vehicle, the explosive increase in data from outside the vehicle can affect the backbone. That is, the transmission efficiency of the IVN backbone will be reduced due to excessive data traffic. In addition, when IVN data traffic is transmitted to IoV without considering IoV network conditions, the transmission efficiency of IoV is also reduced. Therefore, in this paper, we propose an IoV access gateway to controls the incoming data traffic to the IVN backbone and the outgoing data traffic to the IoV in the network environment where IVN and IoV are integrated. Computer simulations are used to evaluate the performance of the proposed system, and the proposed system shows better performance in the accumulated average transmission delay.
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Tang, Xianzhi, Longfei Shi, Bo Wang, and Anqi Cheng. "Weight Adaptive Path Tracking Control for Autonomous Vehicles Based on PSO-BP Neural Network." Sensors 23, no. 1 (December 30, 2022): 412. http://dx.doi.org/10.3390/s23010412.
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In order to improve the tracking adaptability of autonomous vehicles under different vehicle speeds and road curvature, this paper develops a weight adaptive model prediction control system (AMPC) based on PSO-BP neural network, which consists of a dynamics-based model prediction controller (MPC) and an optimal weight adaptive regulator. Based on the application of MPC to achieve high-precision tracking control, the optimal weight under different operating conditions obtained by automated simulation is used to train the PSO-BP neural network offline to achieve online adjustment of MPC weight. The validation results of the Prescan-Carsim-Simulink joint simulation platform show that the adaptive control system has better tracking adaptation capability compared with the original classical MPC control. The control strategy was also verified on an autonomous vehicle test platform, and the test results showed that the adaptive control strategy improved tracking accuracy while meeting the vehicle’s requirements for real-time control and lateral stability.
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Hai, Huang, Zhang Guocheng, Qing Hongde, and Zhou Zexing. "Autonomous underwater vehicle precise motion control for target following with model uncertainty." International Journal of Advanced Robotic Systems 14, no. 4 (July 1, 2017): 172988141771980. http://dx.doi.org/10.1177/1729881417719808.
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Target following plays an important role in oceanic detection and target capturing for autonomous underwater vehicles. Due to the model nonlinearity and external disturbance, the dynamic model of a portable autonomous underwater vehicle was usually established with parameter uncertainties. In this article, a petri-based recurrent type 2 fuzzy neural network has been built to approximate the unknown autonomous underwater vehicle dynamics. The type 2 fuzzy logic system has been applied to the network to improve the approximation accuracy for systematic nonlinearity, and the petri layer in the network can improve estimation speed and reduce energy consumption. A petri-based recurrent type 2 fuzzy neural network–based adaptive robust controller has been proposed for target tracking. In the offshore experiments, the proposed controller has not only realized stable position and pose control but also successfully followed mobile target on the surface. In the tank underwater experiments, the pipeline target has been successfully followed to further verify the controller performance.
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Ramesh, G., and J. Praveen. "Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles." E3S Web of Conferences 309 (2021): 01167. http://dx.doi.org/10.1051/e3sconf/202130901167.
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An electric vehicle with autonomous driving is a possibility provided technology innovations in multi-disciplinary approach. Electric vehicles leverage environmental conditions and are much desired in the contemporary world. Another great possibility is to strive for making the vehicle to drive itself (autonomous driving) provided instructions. When the two are combined, it leads to a different dimension of environmental safety and technology driven driving that has many pros and cons as well. It is still in its infancy and there is much research to be carried out. In this context, this paper is aimed at building an Artificial Intelligence (AI) framework that has dual goal of “monitoring and regulating power usage” and facilitating autonomous driving with technology-driven and real time knowledge required. A methodology is proposed with multiple deep learning methods. For instance, deep learning is used for localization of vehicle, path planning at high level and path planning for low level. Apart from this, there is reinforcement learning and transfer learning to speed up the process of gaining real time business intelligence. To facilitate real time knowledge discovery from given scenarios, both edge and cloud resources are appropriately exploited to benefit the vehicle as driving safety is given paramount importance. There is power management module where modular Recurrent Neural Network is used. Another module known as speed control is used to have real time control over the speed of the vehicle. The usage of AI framework makes the electronic and autonomous vehicles realize unprecedented possibilities in power management and safe autonomous driving. Key words: Artificial Intelligence Autonomous Driving Recurrent Neural Network Transfer Learning
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Budisusila, Eka Nuryanto, Sri Arttini Dwi Prasetyowati, Bhakti Yudho Suprapto, and Zainuddin Nawawi. "Neural network training for serial multisensor of autonomous vehicle system." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 5415. http://dx.doi.org/10.11591/ijece.v12i5.pp5415-5426.
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<span>This study aims to find the best artificial neural network weight values to be applied to the autonomous vehicle system with ultrasonic multisensor. The implementation of neural network in the system required long time process due to its training process. Therefore, this research is using offline training before implementing to online training by embedding the best network weight values to obtain the outputs faster according to desired targets. Simulink were used to train the system offline. Eight ultrasonic sensors are used on all sides of the vehicle and arranged in a serial multisensory configuration as inputs of neural network. With eight inputs, one sixteen-depth hidden layer, and five outputs, it was trained using the back-propagation algorithm of artificial neural network. By 100000 iterations, the output values and the target values are almost the same, indicating its convergency with minimum of errors. The result of this training is the best weights of the networks. These weight values can be implemented as fixed-weight in online training.</span>
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Wang, Zhuwei, Yuehui Guo, Yu Gao, Chao Fang, Meng Li, and Yang Sun. "Fog-Based Distributed Networked Control for Connected Autonomous Vehicles." Wireless Communications and Mobile Computing 2020 (November 3, 2020): 1–11. http://dx.doi.org/10.1155/2020/8855655.
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With the rapid developments of wireless communication and increasing number of connected vehicles, Vehicular Ad Hoc Networks (VANETs) enable cyberinteractions in the physical transportation system. Future networks require real-time control capability to support delay-sensitive application such as connected autonomous vehicles. In recent years, fog computing becomes an emerging technology to deal with the insufficiency in traditional cloud computing. In this paper, a fog-based distributed network control design is proposed toward connected and automated vehicle application. The proposed architecture combines VANETs with the new fog paradigm to enhance the connectivity and collaboration among distributed vehicles. A case study of connected cruise control (CCC) is introduced to demonstrate the efficiency of the proposed architecture and control design. Finally, we discuss some future research directions and open issues to be addressed.
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Jefferson, Jacelyn, and Anthony D. McDonald. "The autonomous vehicle social network: Analyzing tweets after a recent Tesla autopilot crash." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 2071–75. http://dx.doi.org/10.1177/1071181319631510.
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Automated vehicle technologies offer a potentially safer alternative than manually driven vehicles, but only if they are accepted and used appropriately. Social media platforms may offer an opportunity to assess peoples’ willingness to accept and use automated vehicle technology, but questions remain on the structure and content of the social media conversation. To answer these questions, we performed an analysis of tweets surrounding a recent Tesla Autopilot incident. Tweets were analyzed at three levels: term frequency, account tweet and retweet frequency, and sentiment. The most frequent terms of the conversation shifted from “amazon” and “startup” to “autopilot” and “vehicle” following the crash, however, the specific tweet content referenced an earlier event. A small portion of accounts were responsible for the majority of the tweets in the dataset, and were rarely retweeted. Positive and negative sentiment decreased following the crash, suggesting that a more complex sentiment analysis is needed to gauge changes in public opinion of automated vehicles.
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Kwon, Donghwoon, Ritesh Malaiya, Geumchae Yoon, Jeong-Tak Ryu, and Su-Young Pi. "A Study on Development of the Camera-Based Blind Spot Detection System Using the Deep Learning Methodology." Applied Sciences 9, no. 14 (July 23, 2019): 2941. http://dx.doi.org/10.3390/app9142941.
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One of the recent news headlines is that a pedestrian was killed by an autonomous vehicle because safety features in this vehicle did not detect an object on a road correctly. Due to this accident, some global automobile companies announced plans to postpone development of an autonomous vehicle. Furthermore, there is no doubt about the importance of safety features for autonomous vehicles. For this reason, our research goal is the development of a very safe and lightweight camera-based blind spot detection system, which can be applied to future autonomous vehicles. The blind spot detection system was implemented in open source software. Approximately 2000 vehicle images and 9000 non-vehicle images were adopted for training the Fully Connected Network (FCN) model. Other data processing concepts such as the Histogram of Oriented Gradients (HOG), heat map, and thresholding were also employed. We achieved 99.43% training accuracy and 98.99% testing accuracy of the FCN model, respectively. Source codes with respect to all the methodologies were then deployed to an off-the-shelf embedded board for actual testing on a road. Actual testing was conducted with consideration of various factors, and we confirmed 93.75% average detection accuracy with three false positives.
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Cottam, Bobby J. "Transportation Planning for Connected Autonomous Vehicles: How It All Fits Together." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 51 (March 29, 2018): 12–19. http://dx.doi.org/10.1177/0361198118756632.
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As connected and autonomous vehicle (CAV) technology continues to evolve and rapidly develop new capabilities, it is becoming increasingly important for transportation planners to consider the effects that these vehicles will have on the transportation network. It is evident that this trend has already started; over 60% of long-range transportation plans in the largest urban areas now include some discussion of CAVs, up from just 6% in 2015. There are also numerous CAV pilot programs currently underway that entail testing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interaction in both isolated and real-world environments. In this review of the current assessments for CAV impacts, two primary trends are identified. First, there is a great deal of uncertainty that is not being explicitly considered and properly accounted for in the transportation-network planning process. Second, the predictions that are being made are not considering potential policy or planning actions that could shape or affect the impacts of CAVs. This paper provides a picture of how ongoing CAV research interacts with current transportation planning practices by examining how the methods, the ranges of predictions, and the different sources of uncertainty in each method impact the planning process and potential system outcomes. Finally, it will identify best practices from decision analysis to help plan the best possible future transportation networks.
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Cao, Xiang, and Liqiang Guo. "A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments." International Journal of Advanced Robotic Systems 16, no. 4 (July 2019): 172988141987066. http://dx.doi.org/10.1177/1729881419870664.
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As one of the challenging tasks of multiple autonomous underwater vehicles systems, the realization of target hunting is the great significance. The multiple autonomous underwater vehicle target hunting is studied in this article. In some research, because the hunting members cannot reach the hunting point at the same time, the hunting time is long or the target escapes. To improve the efficiency of the target hunting, the leader–follower formation algorithm is introduced. Firstly, the task is assigned based on the distance between the autonomous underwater vehicle and the target. Then, the autonomous underwater vehicles with the same task are formed based on leader–follower mode, and the formation is kept to track the target. In the final capture phase, multiple autonomous underwater vehicle system use angle matching algorithm to round up target. The simulation results show that the proposed algorithm can effectively accomplish the target hunting task, save the hunting time, and avoid the target escape. Compared with the bioinspired neural network algorithm, the proposed algorithm shows better performance.
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Lee, Heung-Gu, Dong-Hyun Kang, and Deok-Hwan Kim. "Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles." Electronics 10, no. 19 (October 1, 2021): 2405. http://dx.doi.org/10.3390/electronics10192405.
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Currently, the existing vehicle-centric semi-autonomous driving modules do not consider the driver’s situation and emotions. In an autonomous driving environment, when changing to manual driving, human–machine interface and advanced driver assistance systems (ADAS) are essential to assist vehicle driving. This study proposes a human–machine interface that considers the driver’s situation and emotions to enhance the ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used and applied to control semi-autonomous vehicles. The possibility of semi-autonomous driving is confirmed by classifying four driving scenarios and controlling the speed of the vehicle. In the experiment, by using a driving simulator and hardware-in-the-loop simulation equipment, we confirm that the response speed of the driving assistance system is 351.75 ms and the system recognizes four scenarios and eight emotions through bio-signal data.
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Kim, Hyunkun, Hyeongoo Pyeon, Jong Sool Park, Jin Young Hwang, and Sejoon Lim. "Autonomous Vehicle Fuel Economy Optimization with Deep Reinforcement Learning." Electronics 9, no. 11 (November 13, 2020): 1911. http://dx.doi.org/10.3390/electronics9111911.
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The ever-increasing number of vehicles on the road puts pressure on car manufacturers to make their car fuel-efficient. With autonomous vehicles, we can find new strategies to optimize fuels. We propose a reinforcement learning algorithm that trains deep neural networks to generate a fuel-efficient velocity profile for autonomous vehicles given road altitude information for the planned trip. Using a highly accurate industry-accepted fuel economy simulation program, we train our deep neural network model. We developed a technique for adapting the heterogeneous simulation program on top of an open-source deep learning framework, and reduced dimension of the problem output with suitable parameterization to train the neural network much faster. The learned model combined with reinforcement learning-based strategy generation effectively generated the velocity profile so that autonomous vehicles can follow to control itself in a fuel efficient way. We evaluate our algorithm’s performance using the fuel economy simulation program for various altitude profiles. We also demonstrate that our method can teach neural networks to generate useful strategies to increase fuel economy even on unseen roads. Our method improved fuel economy by 8% compared to a simple grid search approach.
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Mahmoud, Yaqub, Yuichi Okuyama, Tomohide Fukuchi, Tanaka Kosuke, and Iori Ando. "Optimizing Deep-Neural-Network-Driven Autonomous Race Car Using Image Scaling." SHS Web of Conferences 77 (2020): 04002. http://dx.doi.org/10.1051/shsconf/20207704002.
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In this work we propose scaling down the image resolution of an autonomous vehicle and measuring the performance difference using pre-determined metrics. We formulated a testing strategy and provided suitable testing metrics for RC driven autonomous vehicles. Our goal is to measure and prove that scaling down an image will result in faster response time and higher speeds. Our model shows an increase in response rate of the neural models, improving safety and results in the car having higher speeds.