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Journal articles on the topic 'Vehicles of the self-weighted layer'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Li, Qin, Bingguang Ou, Yifa Liang, Yong Wang, Xuan Yang, and Linchao Li. "TCN-SA: A Social Attention Network Based on Temporal Convolutional Network for Vehicle Trajectory Prediction." Journal of Advanced Transportation 2023 (December 9, 2023): 1–12. http://dx.doi.org/10.1155/2023/1286977.

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Vehicle trajectory prediction can provide important support for intelligent transportation systems in areas such as autonomous driving, traffic control, and traffic flow optimization. Predicting vehicle trajectories is an extremely challenging task that not only depends on the vehicle’s historical trajectory but also on the dynamic and complex social-temporal relationships of the surrounding traffic network. The trajectory of the target vehicle is influenced by surrounding vehicles. However, existing methods have shortcomings in considering both time dependency and interactive dependency betwe

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2

Dun Cao, Dun Cao, Jiawen Liu Dun Cao, Qinghua Liu Jiawen Liu, Jin Wang Qinghua Liu, and Min Zhu Jin Wang. "Relay-node Selection Method Based on Weighted Strategy for 3D Scenario in Internet of Vehicles." 網際網路技術學刊 25, no. 2 (2024): 233–40. http://dx.doi.org/10.53106/160792642024032502006.

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<p>In recent years, Internet of Vehicles (IoV), as a supporting technology for Intelligent Transportation System (ITS), is flourishing with the emergence and development of new technologies such as edge computing, 5G communication, and Artificial Intelligence (AI). However, the more complexity of wireless channels and vehicle distribution in 3D scenario brings a great challenge for relay-node selection in ITS. In this paper, we focus on how to alleviate the problem that the decline of two-hop distance and two-hop connection probability caused by the relative fading of inter-layer communi

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Kramer, Louisa J., Leigh R. Crilley, Thomas J. Adams, Stephen M. Ball, Francis D. Pope, and William J. Bloss. "Nitrous acid (HONO) emissions under real-world driving conditions from vehicles in a UK road tunnel." Atmospheric Chemistry and Physics 20, no. 9 (2020): 5231–48. http://dx.doi.org/10.5194/acp-20-5231-2020.

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Abstract. Measurements of atmospheric boundary layer nitrous acid (HONO) and nitrogen oxides (NOx) were performed in summer 2016 inside a city centre road tunnel in Birmingham, United Kingdom. HONO and NOx mixing ratios were strongly correlated with traffic density, with peak levels observed during the early evening rush hour as a result of traffic congestion in the tunnel. A day-time ΔHONO∕ΔNOx ratio of 0.85 % (0.72 % to 1.01 %, 95 % confidence interval) was calculated using reduced major axis regression for the overall fleet average (comprising 59 % diesel-fuelled vehicles). A comparison wit

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Zou, Wang, and Hanyu Gan. "Free motion characteristics of the ventilated supercavitating vehicles." Journal of Physics: Conference Series 2756, no. 1 (2024): 012046. http://dx.doi.org/10.1088/1742-6596/2756/1/012046.

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Abstract Supercavitation theory and technology are important methods for reducing the drag of underwater vehicles to achieve motion with high speed. A ventilated supercavitating vehicle can break the limitation of natural supercavitation on speed and ambient pressure and has broad application prospects. The presence of hydrodynamic forces only at the head and tail restricts the significant improvement of the motion performance of the vehicle. Therefore, this study improves the dynamic model of a supercavitating vehicle using a self-developed shear-layer gas loss model and simulates the vehicle

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Li, Linhui, Xin Sui, Jing Lian, Fengning Yu, and Yafu Zhou. "Vehicle Interaction Behavior Prediction with Self-Attention." Sensors 22, no. 2 (2022): 429. http://dx.doi.org/10.3390/s22020429.

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The structured road is a scene with high interaction between vehicles, but due to the high uncertainty of behavior, the prediction of vehicle interaction behavior is still a challenge. This prediction is significant for controlling the ego-vehicle. We propose an interaction behavior prediction model based on vehicle cluster (VC) by self-attention (VC-Attention) to improve the prediction performance. Firstly, a five-vehicle based cluster structure is designed to extract the interactive features between ego-vehicle and target vehicle, such as Deceleration Rate to Avoid a Crash (DRAC) and the lan

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Yin, Yuming, Shengbo Eben Li, Keqiang Li, Jue Yang, and Fei Ma. "Self-learning drift control of automated vehicles beyond handling limit after rear-end collision." Transportation Safety and Environment 2, no. 2 (2020): 97–105. http://dx.doi.org/10.1093/tse/tdaa009.

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Abstract Vehicles involved in traffic accidents generally experience divergent vehicle motion, which causes severe damage. This paper presents a self-learning drift-control method for the purpose of stabilizing a vehicle's yaw motions after a high-speed rear-end collision. The struck vehicle generally experiences substantial drifting and/or spinning after the collision, which is beyond the handling limit and difficult to control. Drift control of the struck vehicle along the original lane was investigated. The rear-end collision was treated as a set of impact forces, and the three-dimensional

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Adu-Kyere, Akwasi, Ethiopia Nigussie, and Jouni Isoaho. "Self-Aware Cybersecurity Architecture for Autonomous Vehicles: Security through System-Level Accountability." Sensors 23, no. 21 (2023): 8817. http://dx.doi.org/10.3390/s23218817.

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The inherent dynamism of recent technological advancements in intelligent vehicles has seen multitudes of noteworthy security concerns regarding interactions and data. As future mobility embraces the concept of vehicles-to-everything, it exacerbates security complexities and challenges concerning dynamism, adaptiveness, and self-awareness. It calls for a transition from security measures relying on static approaches and implementations. Therefore, to address this transition, this work proposes a hierarchical self-aware security architecture that effectively establishes accountability at the sy

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Du, Zhiqiang, Jiaheng Zhang, Yanfang Fu, Muhong Huang, Liangxin Liu, and Yunliang Li. "A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles." Applied Sciences 13, no. 19 (2023): 10663. http://dx.doi.org/10.3390/app131910663.

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As blockchain technology plays an increasingly important role in the Internet of Vehicles, how to further enhance the data consensus between the areas of the Internet of Vehicles has become a key issue in blockchain design. The traditional blockchain-based vehicle networking consensus mechanism adopts the double-layer PBFT architecture, through the grouping of nodes for first intra-group consensus, and then global consensus. To further reduce delay, we propose a CRMWSL-PBFT algorithm (C-PBFT) for vehicle networking. Firstly, in order to ensure the security of RSU nodes in the network of vehicl

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Rui, Li, Xie Xiaoyu, and Duan Xueyan. "Fatigue Load Spectrum of Highway Bridge Vehicles in Plateau Mountainous Area Based on Wireless Sensing." Mobile Information Systems 2021 (April 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/9955988.

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In Yunnan and other plateau mountainous areas, hydropower and mineral resources are abundant, and there are relatively many vehicles used for the transportation of large hydropower facilities. The widespread phenomenon of vehicle overload causes severe fatigue among the drivers. However, there is no reference vehicle load spectrum for fatigue analysis in the existing research. The application of wireless sensing technology to bridge health monitoring is favorable for the entire monitoring system’s low-cost and intelligent development. In this study, wireless sensors are used to collect sensing

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10

Feng, Jianbo, Zepeng Gao, and Bingying Guo. "An Adaptive Vehicle Stability Enhancement Controller Based on Tire Cornering Stiffness Adaptations." World Electric Vehicle Journal 16, no. 7 (2025): 377. https://doi.org/10.3390/wevj16070377.

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This study presents an adaptive integrated chassis control strategy for enhancing vehicle stability under different road conditions, specifically through the real-time estimation of tire cornering stiffness. A hierarchical control architecture is developed, combining active front steering (AFS) and direct yaw moment control (DYC). A recursive regularized weighted least squares algorithm is designed to estimate tire cornering stiffness from measurable vehicle states, eliminating the need for additional tire sensors. Leveraging this estimation, an adaptive sliding mode controller (ASMC) is propo

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11

Kathole, Atul B., Kapil Vhatkar, Swapnaja Amol Ubale, Vinod V. Kimbahune, Amol Dhumane, and Ankur Goyal. "Enhanced security mechanism in vehicular networks using ensemble machine learning to detect malicious activity in VANETs." Journal of Discrete Mathematical Sciences and Cryptography 27, no. 7 (2024): 2005–14. http://dx.doi.org/10.47974/jdmsc-2075.

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One Vehicular Ad-Hoc Network (VANET) consists of numerous active and static vehicles connected in a wireless network. It is an easy and affordable method for transferring information related to traffic and vehicles to the traffic control rooms but may cause the security issues. VANET uses certain protocols for securely transmitting the information and providing internet connectivity to vehicle nodes. An AODV is commonly used in VANET. It is a fast and low-processing machine language model with memory overhead. Vehicles are equipped with OBU, which executes transferring message. If numerous veh

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12

Yuan, Xiaodong, Xize Jiao, Mingshen Wang, Huachun Han, Shukang Lv, and Fei Zeng. "Pricing Strategies for Distribution Network Electric Vehicle Operators Considering the Uncertainty of Renewable Energy." Processes 12, no. 6 (2024): 1230. http://dx.doi.org/10.3390/pr12061230.

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In the future, the active load of the distribution network side will be dominated by electric vehicles (EVs), showing that the charging power demand of electric vehicles will change with the change in charging electricity price. With the popularity of electric vehicles in the distribution network, their aggregation operators will play a more prominent role in pricing management and charging behavior, and setting an appropriate charging price can achieve a win–win situation for operators and electric vehicle users. At the same time, the proportion of scenery in the distribution network is relat

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13

Zheng, Ling, Bijun Li, Hongjuan Zhang, Yunxiao Shan, and Jian Zhou. "A High-Definition Road-Network Model for Self-Driving Vehicles." ISPRS International Journal of Geo-Information 7, no. 11 (2018): 417. http://dx.doi.org/10.3390/ijgi7110417.

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High-definition (HD) maps have gained increasing attention in highly automated driving technology and show great significance for self-driving cars. An HD road network (HDRN) is one of the most important parts of an HD map. To date, there have been few studies focusing on road and road-segment extraction in the automatic generation of an HDRN. To improve the precision of an HDRN further and represent the topological relations between road segments and lanes better, in this paper, we propose an HDRN model (HDRNM) for a self-driving car. The HDRNM divides the HDRN into a road-segment network lay

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Iroshnikov, Denis V., Lyubov Yu Larina, and Aleksandr I. Sidorkin. "Autonomous Vehicles within the Urban Space and Transport Security Challenges: Legal Aspect." Journal of Politics and Law 13, no. 3 (2020): 133. http://dx.doi.org/10.5539/jpl.v13n3p133.

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Nowadays autonomous vehicles are getting widespread use in different parts of the world. In some countries, they are being tested within the urban traffic whereas other counties have been already operating them. Such vehicles possess a number of obvious advantages. We cannot but agree that these cars are the future.  However, before complete implementation and mass use of autonomous transport on public roads, it is necessary to resolve a number of problems concerning their safety towards road-users. Except for ethical, economic, and other aspects, it also embraces the legal aspect.

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15

Li, Qiran, Qian Chen, Chengli Jian, Qingnan Wang, and Jihui Tu. "GCN-SATO: A Graph Convolutional Network with Self-Attention based Car-Following Model." Journal of Physics: Conference Series 2832, no. 1 (2024): 012017. http://dx.doi.org/10.1088/1742-6596/2832/1/012017.

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Abstract Car following is critical for the overall safety, efficiency, and smooth operation of autonomous vehicles in traffic. However, the existing car-following model primarily focuses on local feature extraction, overlooking the spatial-temporal relationships between vehicles and key information within the time series. This limitation negatively impacts prediction accuracy and generalization capabilities. To tickle these problems, this paper proposes a novel car-following method based on a Graph Convolutional Network (GCN) and a self-attention mechanism. Firstly, the GCN network is utilized

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Sherin, Cynthia, and Kayalvizhi Jayavel. "Regional feature learning using attribute structural analysis in bipartite attention framework for vehicle re-identification." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5824–32. https://doi.org/10.11591/ijece.v13i5.pp5824-5832.

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Vehicle re-identification identifies target vehicles using images obtained by numerous non-overlapping real-time surveillance cameras. The effectiveness of re-identification is further challenging because of illumination changes, pose differences of captured images, and resolution. Fine-grained appearance changes in vehicles are recognized in addition to the coarse-grained characteristics like color of the vehicle along with model, and other custom features like logo stickers, annual service signs, and hangings to overcome these challenges. To prove the efficiency of our proposed bipartite att

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17

Hao, Shuai, Beiyi An, Hu Wen, Xu Ma, and Keping Yu. "A Heterogeneous Image Fusion Method Based on DCT and Anisotropic Diffusion for UAVs in Future 5G IoT Scenarios." Wireless Communications and Mobile Computing 2020 (June 27, 2020): 1–11. http://dx.doi.org/10.1155/2020/8816818.

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Unmanned aerial vehicles, with their inherent fine attributes, such as flexibility, mobility, and autonomy, play an increasingly important role in the Internet of Things (IoT). Airborne infrared and visible image fusion, which constitutes an important data basis for the perception layer of IoT, has been widely used in various fields such as electric power inspection, military reconnaissance, emergency rescue, and traffic management. However, traditional infrared and visible image fusion methods suffer from weak detail resolution. In order to better preserve useful information from source image

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18

Liang, Mincui, Khalil El Khamlichi Drissi, and Christopher Pasquier. "Self- and Mutual-Inductance Cross-Validation of Multi-Turn, Multi-Layer Square Coils for Dynamic Wireless Charging of Electric Vehicles." Energies 16, no. 20 (2023): 7033. http://dx.doi.org/10.3390/en16207033.

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Dynamic Wireless Power Transfer (DWPT) has high potential to overcome electric vehicles’ battery issues of size and range and to achieve fully autonomous driving. Accurately extracting the self- and mutual-inductance of the coils is essential for controlling and optimizing the overall performance of the DWPT system under real driving conditions. Due to the limited space for coil installation at the bottom of the vehicles, multi-turn, multi-layer square coils are proposed to maximize the space utilization of the DWPT system. For the first time, this paper presents a theoretical model for calcul

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19

Khanum, Abida, Chao-Yang Lee, and Chu-Sing Yang. "Deep-Learning-Based Network for Lane Following in Autonomous Vehicles." Electronics 11, no. 19 (2022): 3084. http://dx.doi.org/10.3390/electronics11193084.

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The research field of autonomous self-driving vehicles has recently become increasingly popular. In addition, motion-planning technology is essential for autonomous vehicles because it mitigates the prevailing on-road obstacles. Herein, a deep-learning-network-based architecture that was integrated with VGG16 and the gated recurrent unit (GRU) was applied for lane-following on roads . The normalized input image was fed to the three-layer VGG16 output layer as a pattern and the GRU output layer as the last layer. Next, the processed data were fed to the two fully connected layers, with a dropou

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20

Luna-Álvarez, Antonio, Dante Mújica-Vargas, Arturo Rendón-Castro, Manuel Matuz-Cruz, and Jean Marie Vianney Vianney Kinani. "Neurofuzzy Data Aggregation in a Multisensory Systemfor Self-Driving Car Steering." Electronics 12, no. 2 (2023): 314. http://dx.doi.org/10.3390/electronics12020314.

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In the self-driving vehicles domain, steering control is a process that transforms information obtained from sensors into commands that steer the vehicle on the road and avoid obstacles. Although a greater number of sensors improves perception and increases control precision, it also increases the computational cost and the number of processes. To reduce the cost and allow data fusion and vehicle control as a single process, this research proposes a data fusion approach by formulating a neurofuzzy aggregation deep learning layer; this approach integrates aggregation using fuzzy measures μ as f

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Chudzikiewicz, Andrzej, Rafał Melnik, and Ignacy Góra. "Wheelsets’ self-lubricating coatings in terms of rail vehicle dynamic properties." WUT Journal of Transportation Engineering 124 (March 1, 2019): 19–30. http://dx.doi.org/10.5604/01.3001.0013.6631.

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Exploitation of railway vehicles, especially in the case of goods transport, on lines with small-radius curves, results in accelerated wear of wheel profiles and rails in curves. This results in increased operating costs and can lead to derailments on such the lines. One of the ways to reduce these negative effects and to improve the wheel-rail interaction in sharp curves is to cover the surface of the wheel flange with coatings of materials with self-lubricating properties. Covering the wheel flange surface with a suitable coating reduces friction coefficient in case of flange-rail head inter

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Kang, Xu, Bin Song, Jie Guo, Xiaojiang Du, and Mohsen Guizani. "Attention-mechanism-based tracking method for intelligent Internet of vehicles." International Journal of Distributed Sensor Networks 14, no. 10 (2018): 155014771880594. http://dx.doi.org/10.1177/1550147718805946.

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Vehicle tracking task plays an important role on the Internet of vehicles and intelligent transportation system. Beyond the traditional Global Positioning System sensor, the image sensor can capture different kinds of vehicles, analyze their driving situation, and can interact with them. Aiming at the problem that the traditional convolutional neural network is vulnerable to background interference, this article proposes vehicle tracking method based on human attention mechanism for self-selection of deep features with an inter-channel fully connected layer. It mainly includes the following co

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Gouthami Kathala. "The role of integration in the future of autonomous vehicles: A data integration perspective." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 716–23. https://doi.org/10.30574/wjarr.2025.26.2.1697.

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Autonomous vehicles represent a transformative force in transportation, with middleware functioning as the critical integration layer enabling their operation. This technological backbone facilitates communication between vehicle subsystems, manages sensor data fusion, and coordinates interactions with external infrastructure. The integration challenges faced in autonomous vehicle development highlight the essential role of middleware architecture in creating reliable, responsive systems capable of operating in complex environments. Intelligence-enhanced middleware leverages artificial intelli

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Wang, Hui, En Lu, Xin Zhao, and Jialin Xue. "Vibration and Image Texture Data Fusion-Based Terrain Classification Using WKNN for Tracked Robots." World Electric Vehicle Journal 14, no. 8 (2023): 214. http://dx.doi.org/10.3390/wevj14080214.

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For terrain recognition needs during vehicle driving, this paper carries out terrain classification research based on vibration and image information. Twenty time-domain features and eight frequency-domain features of vibration signals that are highly correlated with terrain are selected, and principal component analysis (PCA) is used to reduce the dimensionality of the time-domain and frequency-domain features and retain the main information. Meanwhile, the texture features of the terrain images are extracted using the gray-level co-occurrence matrix (GLCM) technique, and the feature informat

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Mazloomi, Fateme, Shahram Shah Heydari, and Khalil El-Khatib. "A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing." Future Internet 17, no. 7 (2025): 315. https://doi.org/10.3390/fi17070315.

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Federated learning (FL) has emerged as a powerful approach for privacy-preserving model training in autonomous vehicle networks, where real-world deployments rely on multiple roadside units (RSUs) serving heterogeneous clients with intermittent connectivity. While most research focuses on single-server or hierarchical cloud-based FL, multi-server FL can alleviate the communication bottlenecks of traditional setups. To this end, we propose an edge-based, multi-server FL (MS-FL) framework that combines performance-driven aggregation at each server—including statistical weighting of peer updates

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26

Yadav, Abhishek. "Integrated Swarm Intelligence Framework for Dynamic Traffic Optimization in Delhi: A Three-Layer PSO-Fuzzy-MAS Approach." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/isjem03921.

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Abstract—This study presents a hybrid, three-layer framework for intelligent traffic optimization in Delhi, combining Particle Swarm Optimization (PSO), Fuzzy Logic Controllers (FLC), and Multi-Agent Systems (MAS). It tackles challenges posed by real-time traffic fluctuations, dense vehicle networks, and limited infrastructure scalability. The system uses PSO for global and MAS for self-organized traffic agent collaboration. SUMO simulations with 628 vehicles show a 32.1% reduction in average travel time and a 28.3% drop in fuel use. Field testing at ITO Junction validated 41% peak-hour conges

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Galatro, Daniela, Manav Shroff, and Cristina H. Amon. "Adaptive Transfer Learning Strategy for Predicting Battery Aging in Electric Vehicles." Batteries 11, no. 1 (2025): 21. https://doi.org/10.3390/batteries11010021.

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This work presents an adaptive transfer learning approach for predicting the aging of lithium-ion batteries (LiBs) in electric vehicles using capacity fade as the metric for the battery state of health. The proposed approach includes a similarity-based and adaptive strategy in which selected data from an original dataset are transferred to a clean dataset based on the combined/weighted similarity contribution of feature and stress factor similarities and times series similarities. Transfer learning (TL) is then performed by pre-training a model with clean data, with frozen weights and biases t

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28

Sreenivas, Dr M. "Traffic Sign Recognition Using CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 3522–34. http://dx.doi.org/10.22214/ijraset.2022.44532.

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Abstract: You've probably heard about self-driving automobiles, in which the passenger can completely rely on the vehicle for transportation. Cars must, however, understand and follow all traffic rules in order to achieve level 5 autonomy. Many researchers and large organisations, including as Tesla, Uber, Google, Mercedes-Benz, Toyota, Ford, Audi, and others, are working on autonomous vehicles and self-driving automobiles in the world of artificial intelligence and technological innovation. As a result, in order for this technology to be accurate, the vehicles must be able to understand traff

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29

Chen, Yicheng, Dayi Qu, Tao Wang, Shanning Cui, and Dedong Shao. "Quantification Method of Driving Risks for Networked Autonomous Vehicles Based on Molecular Potential Fields." Applied Sciences 15, no. 3 (2025): 1306. https://doi.org/10.3390/app15031306.

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Connected autonomous vehicles (CAVs) face constraints from multiple traffic elements, such as the vehicle, road, and environmental factors. Accurately quantifying the vehicle’s operational status and driving risk level in complex traffic scenarios is crucial for enhancing the efficiency and safety of connected autonomous driving. To continuously and dynamically quantify the driving risks faced by CAVs in the road environment—arising from the front, rear, and lateral directions—this study focused s on the self-driving particle characteristics that enable CAVs to perceive their surrounding envir

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Sherin, Cynthia, and Kayalvizhi Jayavel. "Regional feature learning using attribute structural analysis in bipartite attention framework for vehicle re-identification." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 5824. http://dx.doi.org/10.11591/ijece.v13i5.pp5824-5832.

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<p><span lang="EN-US">Vehicle re-identification identifies target vehicles using images obtained by numerous non-overlapping real-time surveillance cameras. The effectiveness of re-identification is further challenging because of illumination changes, pose differences of captured images, and resolution. Fine-grained appearance changes in vehicles are recognized in addition to the coarse-grained characteristics like color of the vehicle along with model, and other custom features like logo stickers, annual service signs, and hangings to overcome these challenges. To prove the effici

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31

Hu, Gangqiang, Donglin Zhu, Jiaying Shen, Jialing Hu, Jianmin Han, and Taiyong Li. "FedBeam: Reliable Incentive Mechanisms for Federated Learning in UAV-Enabled Internet of Vehicles." Drones 8, no. 10 (2024): 567. http://dx.doi.org/10.3390/drones8100567.

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Unmanned aerial vehicles (UAVs) can be utilized as airborne base stations to deliver wireless communication and federated learning (FL) training services for ground vehicles. However, most existing studies assume that vehicles (clients) and UAVs (model owners) offer services voluntarily. In reality, participants (FL clients and model owners) are selfish and will not engage in training without compensation. Meanwhile, due to the heterogeneity of participants and the presence of free-riders and Byzantine behaviors, the quality of vehicles’ model updates can vary significantly. To incentivize par

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32

Gupta, Manik, Rakesh Kumar, Shashi Shekhar, et al. "Game Theory-Based Authentication Framework to Secure Internet of Vehicles with Blockchain." Sensors 22, no. 14 (2022): 5119. http://dx.doi.org/10.3390/s22145119.

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The Internet of Vehicles (IoV) is a new paradigm for vehicular networks. Using diverse access methods, IoV enables vehicles to connect with their surroundings. However, without data security, IoV settings might be hazardous. Because of the IoV’s openness and self-organization, they are prone to malevolent attack. To overcome this problem, this paper proposes a revolutionary blockchain-enabled game theory-based authentication mechanism for securing IoVs. Here, a three layer multi-trusted authorization solution is provided in which authentication of vehicles can be performed from initial entry t

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Dr. B. Maruthi Shankar. "Neural Network Based Hurdle Avoidance System for Smart Vehicles." International Journal of New Practices in Management and Engineering 8, no. 04 (2019): 01–07. http://dx.doi.org/10.17762/ijnpme.v8i04.79.

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The structure of a self-ruling vehicle dependent on neural sophisticated network for route in obscure condition is proposed. The vehicle is equipped with an IR sensor for obstacle separation estimation, a GPS collector for goal data and heading position, L298 H-connect for driving the engines which runs the wheels; all interfaced to a controller unit. The smaller scale controller forms the data gained from the sensor and GPS to produce robot movement through neural based network. The neural network running inside the small scale controller is a multi-layer feed-forward network with back-engend

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Pathak, Pawan, and Hyoung Jin Cho. "Self-Assembled 1-Octadecanethiol Membrane on Pd/ZnO for a Selective Room Temperature Flexible Hydrogen Sensor." Micromachines 13, no. 1 (2021): 26. http://dx.doi.org/10.3390/mi13010026.

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A layer of self-assembled 1-octadecanethiol was used to fabricate a palladium (Pd)/zinc oxide (ZnO) nanoparticle-based flexible hydrogen sensor with enhanced response and high selectivity at room temperature. A palladium film was first deposited using DC sputtering technique and later annealed to form palladium nanoparticles. The formation of uniform, surfactant-free palladium nanoparticles contributed to improved sensor response towards hydrogen gas at room temperature. The obtained sensor response was higher than for previously reported room temperature Pd/ZnO sensors. Furthermore, the use o

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Yu, Mei, Sha Ye, Yuelin Zheng, et al. "A Shallow Pooled Weighted Feature Enhancement Network for Small-Sized Pine Wilt Diseased Tree Detection." Electronics 12, no. 11 (2023): 2463. http://dx.doi.org/10.3390/electronics12112463.

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Pine wild disease poses a serious threat to the ecological environment of national forests. Combining the object detection algorithm with Unmanned Aerial Vehicles (UAV) to detect pine wild diseased trees (PWDT) is a significant step in preventing the spread of pine wild disease. To address the issue of shallow feature layers lacking the ability to fully extract features from small-sized diseased trees in existing detection algorithms, as well as the problem of a small number of small-sized diseased trees in a single image, a Shallow Pooled Weighted Feature Enhancement Network (SPW-FEN) based o

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Juyal, Amit, Sachin Sharma, and Shuchi Bhadula. "Modified-vehicle detection and localization model for autonomous vehicle traffic system." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 2 (2025): 1183. http://dx.doi.org/10.11591/ijeecs.v37.i2.pp1183-1200.

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<p>The modification of vehicles for financial gain is an evolving tendency observed in India. Recognizing and detecting of these modified illicit cars is an important but critical task in autonomous vehicles (AV). It is always possible for a cyclist or pedestrian to traverse obstacles or other fixed objects that appear in front of any moving vehicle. Vehicles that are autonomous or self-driving require a different system to quickly identify both stationary and moving objects. A deep learning model named you only look once version 5 (YOLOv5)-convolutional block attention module (CBAM) is

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Amit, Juyal Sachin Sharma Shuchi Bhadula. "Modified-vehicle detection and localization model for autonomous vehicle traffic system." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 2 (2025): 1183–200. https://doi.org/10.11591/ijeecs.v37.i2.pp1183-1200.

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The modification of vehicles for financial gain is an evolving tendency observed in India. Recognizing and detecting of these modified illicit cars is an important but critical task in autonomous vehicles (AV). It is always possible for a cyclist or pedestrian to traverse obstacles or other fixed objects that appear in front of any moving vehicle. Vehicles that are autonomous or self-driving require a different system to quickly identify both stationary and moving objects. A deep learning model named you only look once version 5 (YOLOv5)-convolutional block attention module (CBAM) is proposed

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Khidhir, Yahya Ghufran, and Ameer Hussein Morad. "Comparative Transfer Learning Models for End-to-End Self-Driving Car." Al-Khwarizmi Engineering Journal 18, no. 4 (2022): 45–59. http://dx.doi.org/10.22153/kej.2022.09.003.

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Self-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into contro

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Mukherjee, Moumita, Avijit Banerjee, Andreas Papadimitriou, Sina Sharif Mansouri, and George Nikolakopoulos. "A Decentralized Sensor Fusion Scheme for Multi Sensorial Fault Resilient Pose Estimation." Sensors 21, no. 24 (2021): 8259. http://dx.doi.org/10.3390/s21248259.

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This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is

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Zou, Yi, Weiwei Zhang, Wendi Weng, and Zhengyun Meng. "Multi-Vehicle Tracking via Real-Time Detection Probes and a Markov Decision Process Policy." Sensors 19, no. 6 (2019): 1309. http://dx.doi.org/10.3390/s19061309.

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Online multi-object tracking (MOT) has broad applications in time-critical video analysis scenarios such as advanced driver-assistance systems (ADASs) and autonomous driving. In this paper, the proposed system aims at tracking multiple vehicles in the front view of an onboard monocular camera. The vehicle detection probes are customized to generate high precision detection, which plays a basic role in the following tracking-by-detection method. A novel Siamese network with a spatial pyramid pooling (SPP) layer is applied to calculate pairwise appearance similarity. The motion model captured fr

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Zhu, Qingyong, Haixin Jie, Shun Lu, and Zhihui Li. "The Influence of Environmental Temperature on the Passive Oxidation Process in the C/SiC Composite." Fractal and Fractional 8, no. 4 (2024): 192. http://dx.doi.org/10.3390/fractalfract8040192.

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The C/SiC composite plays a crucial role in providing thermal protection for hypersonic vehicles. The SiO2 oxide layer formed via passive oxidation during ablation constitutes a typical porous medium with self-similarity. Given its significant impact on the thermal protection of the material, accurately predicting the variation in the SiO2 oxide layer thickness is of paramount importance. The growth of the oxide layer impedes the diffusion of oxygen within the material. This study considered microstructural parameters of the oxide layer based on high-temperature gas oxidation tests of the C/Si

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Alawaji, Khaldaa, Ramdane Hedjar, and Mansour Zuair. "Traffic Sign Recognition Using Multi-Task Deep Learning for Self-Driving Vehicles." Sensors 24, no. 11 (2024): 3282. http://dx.doi.org/10.3390/s24113282.

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Over the coming years, the advancement of driverless transport systems for people and goods that are designed to be used on fixed routes will revolutionize the transportation system. Therefore, for a safe transportation system, detecting and recognizing traffic signals based on computer vision has become increasingly important. Deep learning approaches, particularly convolutional neural networks, have shown exceptional performance in various computer vision applications. The goal of this research is to precisely detect and recognize traffic signs that are present on the streets using computer

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Koti, Ramesh B., and Mahabaleshwar S. Kakkasageri. "Delay and Energy Optimized Safety Information Dissemination Scheme in V2I Networks." International Journal of Information Technology and Computer Science 14, no. 3 (2022): 34–51. http://dx.doi.org/10.5815/ijitcs.2022.03.04.

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Intelligent Transport System (ITS) is a transport system that uses communicating technologies such as cellular network communication, digital video broadcasting and adhoc wireless communication to link people on the road, vehicles with aim of solving various traffic related issues. Vehicle to infrastructure (V2I) communication is an important research area to develop cooperative self-driving support system using DSRC technology. V2I develops an environment friendly system that also accelerates the fuel efficiency by establishing high quality links between vehicles to roadside infrastructure. I

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Lim, Yong-Chae, Jiheon Jun, Yi-Feng Su, John E. Wade, Jong Keum, and Feng Zhili. "Mitigating Galvanic Corrosion in Carbon Fiber Reinforced Polymer-AZ31B Dissimilar Joints through Oxide Layer-Coated Rivets." ECS Meeting Abstracts MA2024-02, no. 17 (2024): 1691. https://doi.org/10.1149/ma2024-02171691mtgabs.

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The major technical hurdles for lightweight multi-material vehicles lie in joining and corrosion, particularly galvanic corrosion of dissimilar material joints. In this study, the friction self-piercing rivet (F-SPR) process was utilized to spot join carbon fiber reinforced polymer (CFRP) to AZ31B Mg alloy at a coupon scale. After fabricating the dissimilar joint samples, a unique corrosion exposure test was performed to investigate galvanic corrosion of AZ31B at the joint in 0.1 M NaCl solution. A novel oxide self-formation technique was employed on alumina forming austenitic (AFA) alloy to e

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Ni'am, Muhammad Choirul, Reno Martin Fajar Budi Prasetyo, Ibnu Toto Husodo, and Slamet Budirahardjo. "Rancangan Perbandingan Pelebaran Perkerasan Jalan Lentur Dengan Metode Bina Marga 1987 dan Bina Marga 2017 Pada Ruas Jalan Semarang – Purwodadi di Kecamatan Tegowanu Pada STA 25+700 – STA 27+700." Jurnal Teknik Sipil Giratory UPGRIS 3, no. 2 (2023): 98–104. http://dx.doi.org/10.26877/giratory.v3i2.14515.

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ABSTRACTThe road is a means of transportation that then develops into a means of transportation in carrying out economic activities both accessibility and mobility of goods and services. Heavy traffic and violations on road users and owners of large vehicles passing through often make construction on road pavements damaged.Road widening is one of the main structures in road construction where the construction system is required to be able to provide safety and comfort for its users. Factors that affect the performance of road pavements are passing traffic, weather, pavement layer design, as we

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Chen, Hui, Naiyun Tang, and Zhipeng Zuo. "Improvement and Reduction of Self-Heating Effect in AlGaN/GaN HEMT Devices." Journal of Sensors 2022 (September 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/5378666.

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GaN is one of the third-generation broadband semiconductor materials developed rapidly in recent years, and Algan/GanHEMT has a broad application prospect in the fields of high temperature, high power, high frequency and radiation resistance, etc. In recent years, gallium nitride based high electron mobility transistors have been widely used in emerging industries, such as 5G technology, new energy vehicles, unmanned aircraft and other fields, due to their high power and high voltage resistance. However, due to the high power density of HEMT devices, the self-heating effect will lead to a sign

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Aloqaily, Ahmad, Emad E. Abdallah, Hiba AbuZaid, Alaa E. Abdallah, and Malak Al-hassan. "Supervised Machine Learning for Real-Time Intrusion Attack Detection in Connected and Autonomous Vehicles: A Security Paradigm Shift." Informatics 12, no. 1 (2025): 4. https://doi.org/10.3390/informatics12010004.

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Recent improvements in self-driving and connected cars promise to enhance traffic safety by reducing risks and accidents. However, security concerns limit their acceptance. These vehicles, interconnected with infrastructure and other cars, are vulnerable to cyberattacks, which could lead to severe costs, including physical injury or death. In this article, we propose a framework for an intrusion detection system to protect internal vehicle communications from potential attacks and ensure secure sent/transferred data. In the proposed system, real auto-network datasets with Spoofing, DoS, and Fu

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Moreira, Rian T. D., and Dianne S. V. Medeiros. "A Location-Aware and Greedy Cross-Layer Routing Protocol for Flying Ad-hoc Networks." Journal of the Brazilian Computer Society 30, no. 1 (2024): 688–701. https://doi.org/10.5753/jbcs.2024.4164.

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The Flying Ad-hoc Networks (FANETs) enhance the coverage capacity in cellular networks by forwarding data in multiple hops using Unmanned Aerial Vehicles (UAVs). Nevertheless, unlike classic ad-hoc networks, FANETs have specific characteristics, such as free movement in three dimensions and very high-speed nodes. These characteristics lead to a more complex and dynamic mobility pattern compared to other ad-hoc networks, generating more frequent topology changes. This paper proposes the Greedy Weighted Perimeter Routing Protocol (GWPRP), which aims to improve networking performance. GWPRP is a

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Hayashi, Masaki, and William L. Quinton. "A constant-head well permeameter method for measuring field-saturated hydraulic conductivity above an impermeable layer." Canadian Journal of Soil Science 84, no. 3 (2004): 255–64. http://dx.doi.org/10.4141/s03-064.

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Hydrologic understanding of mountainous and northern regions of Canada is poor owing to the lack of critical field data such as hydraulic conductivity. A portable field instrument, the Guelph permeameter (GP), is a promising tool for measuring field-saturated hydraulic conductivity in remote watersheds inaccessible by motorized vehicles. In order to extend the applicability of the GP method to relatively thin soils underlain by impermeable bedrock or permafrost, a new set of shape factors was determined by numerical simulation. The new shape factors gave accurate values of field-saturated hydr

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Zhao, Wentao, Ping Dong, Min Guo, Yuyang Zhang, and Xuehong Chen. "BSS: A Burst Error-Correction Scheme of Multipath Transmission for Mobile Fog Computing." Wireless Communications and Mobile Computing 2020 (June 30, 2020): 1–10. http://dx.doi.org/10.1155/2020/8846545.

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In the scenario of mobile fog computing (MFC), communication between vehicles and fog layer, which is called vehicle-to-fog (V2F) communication, needs to use bandwidth resources as much as possible with low delay and high tolerance for errors. In order to adapt to these harsh scenarios, there are important technical challenges concerning the combination of network coding (NC) and multipath transmission to construct high-quality V2F communication for cloud-aware MFC. Most NC schemes exhibit poor reliability in burst errors that often occur in high-speed movement scenarios. These can be improved

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