Search Results (4,279)

The Internet of Vehicles (IoV) revolutionizes transportation by enabling real-time communication and data exchange among vehicles (V2V), infrastructure (V2I), and other entities (V2X). These capabilities are crucial for improving road safety and traffic efficiency. However, achieving reliable and secure consensus across network nodes remains a significant challenge. Consensus mechanisms are essential in IoV for ensuring agreement on the network’s state, enabling applications such as autonomous driving, traffic management, and emergency response. This paper presents a systematic review of IoV consensus mechanisms, examining 78 peer-reviewed publications from 2010 to June 2025 using the PRISMA framework. Our analysis highlights challenges, including scalability, latency, and energy efficiency and identifies trends such as the adoption of lightweight algorithms, edge computing, and AI-assisted techniques. Unlike previous reviews, this work introduces a structured comparative framework specifically designed for IoV environments, enabling a detailed evaluation of consensus mechanisms across key features such as latency, fault tolerance, communication overhead and scalability to identify their relative strengths and limitations. Full article

Accurate identification of rear light signals in preceding vehicles is pivotal for Advanced Driver Assistance Systems (ADAS), enabling early detection of driver intentions and thereby improving road safety. In this work, we present a novel approach that leverages a meta-learning-enhanced YOLOv8 model to detect left and right turn indicators, as well as brake signals. Traditional radar and LiDAR provide robust geometry, range, and motion cues that can indirectly suggest driver intent (e.g., deceleration or lane drift). However, they do not directly interpret color-coded rear signals, which limits early intent recognition from the taillights. We therefore focus on a camera-based approach that complements ranging sensors by decoding color and spatial patterns in rear lights. This approach to detecting vehicle signals poses additional challenges due to factors such as high reflectivity and the subtle visual differences between directional indicators. We address these by training a YOLOv8 model with a meta-learning strategy, thus enhancing its capability to learn from minimal data and rapidly adapt to new scenarios. Furthermore, we developed a post-processing layer that classifies signals by the geometric properties of detected objects, employing mathematical principles such as distance, area calculation, and Intersection over Union (IoU) metrics. Our approach increases adaptability and performance compared to traditional deep learning techniques, supporting the conclusion that integrating meta-learning into real-time object detection frameworks provides a scalable and robust solution for intelligent vehicle perception, significantly enhancing situational awareness and road safety through reliable prediction of vehicular behavior. Full article

Underwater drones such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) are revolutionizing underwater operations and are essential for advanced marine applications like environmental monitoring, deep-sea exploration, and marine surveillance. In this paper, we concentrate on the enabling technologies and wireless communication strategies for underwater drones. Specifically, we analyze acoustic, optical, and radio frequency (RF) approaches, along with their respective advantages and disadvantages. We investigate the potential of integrating underwater drone-enabled wireless communication systems for smart marine communications. The study highlights the benefits of combining acoustic, optical, and RF methods to improve connectivity and data reliability. A hybrid underwater communication system is ideal for underwater drones because it can reduce latency, increase data throughput, and improve adaptability under various underwater conditions, supporting smart marine communications. The future direction involves developing hybrid communication frameworks that incorporate the Internet of Underwater Things (IoUT), AI-driven data, virtual reality (VR), and digital twin (DT) technologies, enabling a next-generation smart marine ecosystem. Full article

Outlier detection is a critical task in the intelligent operation and maintenance (O&M) of transportation equipment, as it helps ensure the safety and reliability of systems like high-speed trains, aircraft, and intelligent vehicles. Nearest neighbor-based detectors generally offer good interpretability, but often struggle with complex data scenarios involving diverse data distributions and various types of outliers, including local, global, and cluster-based outliers. Moreover, these methods typically rely on predefined contamination, which is a critical parameter that directly determines detection accuracy and can significantly impact system reliability in O&M environments. In this paper, we propose a novel chain-based theory for outlier detection with the aim to provide an interpretable and transparent solution for fault detection. We introduce two methods based on this theory: Cascaded Chain Outlier Detection (CCOD) and Parallel Chain Outlier Detection (PCOD). Both methods identify outliers through sudden increases in chaining distances, with CCOD being more sensitive to local data distributions, while PCOD offers higher computational efficiency. Experimental results on synthetic and real-world datasets demonstrate the superior performance of our methods compared to existing state-of-the-art techniques, with average improvements of 11.3% for CCOD and 14.5% for PCOD. Full article

Bridges are critical components of transport networks, yet they inevitably deteriorate over time. Damage in a bridge reduces its stiffness, altering both its static and dynamic responses to loading. This change also affects the dynamic excitation of the loading source, such as a passing vehicle, meaning that vehicle vibrations can be used to infer bridge damage. Deflections under a moving instrumented axle are influenced by both bridge and vehicle properties, making them unique to each vehicle. To extract meaningful information about bridge conditions, these data must therefore be transformed into a form independent of vehicle characteristics. The bridge influence line, defined as the response to a unit moving load, provides a reliable descriptor of structural behaviour that is unaffected by vehicle properties. In this study, a fleet of vehicles is simulated to generate acceleration signals, from which bridge influence lines are derived using the inverse Newmark–beta method. Two damage indicators are proposed: D1, derived from the mid-span point of the mean Moving Reference Influence Line (MRIL), and D2, defined as the area under the mean MRIL curve. Results show that both indicators can effectively detect global bridge damage. D2 exhibits a clear linear relationship with stiffness loss and remains stable under varying speeds and noise levels, whereas D1 is more sensitive to factors such as measurement noise, vehicle speed, and variations in vehicle properties, but remains reliable at lower speeds and moderate noise levels. Measurement errors are simulated by adding random noise to the acceleration inputs, and the fleet monitoring approach is shown to mitigate these effects, enhancing the overall accuracy and robustness of bridge damage detection. Full article

This paper explores cost-effective solutions for automated guided vehicle (AGV) through the design and implementation of a low-cost, hoverboard-based line-following AGV tailored for textile manufacturing environments, specifically within sewing plants. The designed AGV leverages the capability of a commercial hoverboard as its mobility platform, significantly reducing development costs while maintaining effective operational performance. Utilizing affordable sensors such as infrared line detectors and ultrasonic sensors, the AGV autonomously navigates pre-defined pathways marked on the factory floor. Its primary function is transporting materials such as fabric bundles and partially or finished products between workstations, addressing common logistical challenges in dynamic and labor-intensive textile production settings. The system is designed for easy integration with both existing plant layouts and information and communication environment, requiring minimal infrastructural changes. Field testing demonstrated the AGV’s reliability, maneuverability, and responsiveness in real-world sewing plant conditions. The proposed solution underscores the potential of retrofitting existing consumer electronics for industrial automation, offering a scalable and economically viable alternative for small- to medium-sized textile enterprises seeking to enhance productivity and workflow efficiency. Full article

As space activities expand rapidly, especially the in-orbit population, concerns about their environmental consequences are growing. For in-space propulsion, this is particularly true under the increasing regulatory pressure on hydrazine-based legacy propellants. In response to that, this study presents a cradle-to-gate Life Cycle Assessment (LCA) of the four main current options for in-space liquid bipropellant systems—MON-3/MMH, 98%-HTP/Ethanol, 98%-HTP/RP-1 and N₂O/Ethane—each evaluated as a complete system including propellant-combination loading and sized propulsion-architecture manufacturing. The comparison is performed against a representative 2 kN Orbital Transfer Vehicle (OTV) mission scenario delivering a total $\Delta$v of 2300 m/s. Each solution’s environmental performance is quantified across 15 midpoint indicators, using ESA’s space-specific LCA database and combined through an Analytical Hierarchy Process (AHP) single-score for easier comparison. Results show that while HTP/Ethanol achieves the lowest impact at the propellant-loading level, the N₂O/Ethane system obtains the lowest overall footprint once the full propulsion system architecture, sized for the mission, is included, with a total environmental impact 63% lower than the legacy MON-3/MMH system. A key outcome of this study is that manufacturing propulsion components dominates the life-cycle footprint, bringing up to 95% of the total impact for HTP-based systems and approximately 64% for MON-3/MMH and self-pressurizing architectures, mainly due to the energy-intensive production of titanium and aluminum tanks. In light of these results, this paper proposes a mission-driven definition of “greener” propulsion, requiring at least a 50% reduction in the combined total and human-toxicity impacts, together with a lower Global Warming Potential (GWP) than legacy hydrazine-based systems, given that GWP was identified as the most critical environmental concern to address. However, the study also shows that considering only GWP would have led to an incorrect conclusion, and therefore advises against relying on single-impact environmental assessments. Additional replacement criteria for in-space propellants include cost-efficiency, reliability and global propulsive performance. This work implements a system-level environmental performance assessment and classification framework for in-space liquid propulsion options, providing a structured approach for selecting and qualifying more sustainable alternative candidates for future mission scenarios. Full article

To enhance the battery endurance of unmanned aerial vehicles (UAVs), this article addresses key issues in traditional wireless power transfer (WPT) systems. These issues occur during constant current/constant voltage (CC/CV) switching, such as poor stability, high payload, power loss, and charging instability. Accordingly, a WPT system based on topology switching is proposed. First, a lightweight compensation topology based on LCC-Series compensated topology (LCC-S) is designed. A tuning capacitor is incorporated, and two switches regulate the switching of the compensation capacitor to realize CC/CV mode transition. Meanwhile, the impedance matrix model is built to find optimal compensation component values, maximizing energy transfer. To reduce sensitivity to misalignment, a “+” shaped compensation coil is added to the basic 2 × 2 square coil array. It improves magnetic field uniformity and suppresses flux leakage. Experimental results show that the system achieves stable load-independent output. Within horizontal offset [−150, 150] mm and diagonal offset [−150√2, 150√2] mm, it keeps output power over 150 W and efficiency over 70%, with strong anti-misalignment ability. This system effectively solves key challenges such as endurance bottlenecks, complex CC/CV switching, and weak anti-misalignment. It offers a reliable technical solution for efficient charging of autonomous UAVs. Full article

Traditional multimodal public transportation recommenders often overlook in-vehicle crowding, a critical factor that causes passenger discomfort and leads to an inefficient distribution of people across the network that affects its reliability. To address this, we propose a proof of concept for a novel framework that directly integrates crowding into its optimization process, balancing it with user preferences such as travel habits, travel time, and line changes. Built on the Behavior-Enabled IoT (BeT) paradigm, our system is designed to manage the crucial QoE and QoS trade-off inherent in smart mobility. We validate our balanced strategy using real-world data from Lyon, comparing it against two baselines: a QoE-driven model that prioritizes user habits and a QoS-driven model that focuses solely on network efficiency. Our Wilcoxon-based statistical analysis demonstrates that a balanced strategy is the most effective approach for substantially mitigating public transit crowding. Our Wilcoxon-based statistical analysis demonstrates that a balanced strategy is the most effective approach for mitigating public transit crowding, since it leads to a substantial decrease in crowding. Despite a potential increase in travel times, our solution respects user habits and avoids excessive transfers, providing significant operational improvements without compromising passenger convenience. Full article

Despite continued efforts by the Korean government to improve road safety, truck-related accidents remain disproportionately fatal, with a rate approximately 2.6 times higher than that of passenger vehicles. Although legal regulations prohibit overloading and improper loading, existing enforcement practices—primarily dependent on low-speed weigh-in-motion (WIM) systems—are limited in coverage and responsiveness. This study develops and validates standardized test procedures for detecting overloading and improper loading in commercial freight vehicles using a high-speed weigh-in-motion (HS-WIM) system. The HS-WIM system offers advanced sensing capabilities, including vehicle speed, length, axle configuration, and weight measurement at highway speeds. However, Korean HS-WIM performance standards currently lack detailed guidance, especially concerning group axle load testing and asymmetric cargo detection. To address these regulatory and technical gaps, a comprehensive set of test scenarios was designed based on domestic and international standards. A dedicated testbed was constructed, and 12 commercial vehicle types were tested under varied speeds and loading conditions. The proposed procedures reliably detect violations, and the study introduces evaluation criteria that improve HS-WIM system accuracy and support future enforcement and policy development in Korea. Full article

Intelligent in-vehicle voice systems face critical challenges in robustness, security, and semantic flexibility under complex acoustic conditions. To address these issues holistically, this paper proposes a novel multimodal and secure voice-control framework. The system integrates a hybrid dual-channel wake-up mechanism, combining a commercial English engine (Picovoice) with a custom lightweight ResNet-Lite model for Chinese, to achieve robust cross-lingual activation. For reliable identity authentication, an optimized ECAPA-TDNN model is introduced, enhanced with spectral augmentation, sliding window feature fusion, and an adaptive threshold mechanism. Furthermore, a two-tier fuzzy command matching algorithm operating at character and pinyin levels is designed to significantly improve tolerance to speech variations and ASR errors. Comprehensive experiments on a test set encompassing various Chinese dialects, English accents, and noise environments demonstrate that the proposed system achieves high performance across all components: the wake-up mechanism maintains commercial-grade reliability for English and provides a functional baseline for Chinese; the improved ECAPA-TDNN attains low equal error rates of 2.37% (quiet), 5.59% (background music), and 3.12% (high-speed noise), outperforming standard baselines and showing strong noise robustness against the state of the art; and the fuzzy matcher boosts command recognition accuracy to over 95.67% in quiet environments and above 92.7% under noise, substantially outperforming hard matching by approximately 30%. End-to-end tests confirm an overall interaction success rate of 93.7%. This work offers a practical, integrated solution for developing secure, robust, and flexible voice interfaces in intelligent vehicles. Full article

Integrated circuits have become indispensable in modern society owing to their formidable computational power and high integration, finding extensive applications in critical fields such as artificial intelligence and new energy vehicles. However, continued increases in integration density and reductions in physical size lead to a significantly higher heat flux density, thereby posing major challenges for thermal management and overall chip reliability. To address these thermal challenges, this study introduces an optimized manifold microchannel design. A three-dimensional conjugate heat transfer model was developed, and computational fluid dynamics simulations were performed to analyze the thermal–hydraulic performance. To mitigate temperature non-uniformity, several strategies were implemented: adjusting channel widths, employing uneven inlet gaps, and incorporating micro-fins. Results demonstrate that the optimized configuration achieves a maximum temperature reduction of 7.7 K, with peak thermal stress decreasing from 55.29 MPa to 47 MPa, effectively improving temperature uniformity. This study confirms that the proposed optimized design significantly enhances overall thermal performance, thereby offering a reliable and effective strategy for advanced chip thermal management. Full article

Automated valet parking (AVP) is a key component of autonomous driving systems. Its functionality and reliability need to be thoroughly tested before road application. Current testing technologies are limited by insufficient scenario coverage and lack of comprehensive evaluation indices. This study proposes an AVP testing and evaluation framework using OnSite (Open Naturalistic Simulation and Testing Environment) and Unity3D platforms. Through scenario construction based on field-collected data and model reconstruction, a testing scenario library is established, complying with industry standards. A simplified kinematic model, balancing simulation accuracy and operational efficiency, is applied to describe vehicle motion. A multidimensional evaluation system is developed with completion rate as a primary index and operation performance as a secondary index, which considers both parking efficiency and accuracy. Over 500 AVP algorithms are tested on the OnSite platform, and the testing results are evaluated through the Unity3D platform. The performance of the top 10 algorithms is analyzed. The evaluation platform is compared with CARLA simulation platform and field vehicle testing. This study finds that the framework provides an effective tool for AVP testing and evaluation; a variety of high-level AVP algorithms are developed, but their flexibility in complex dynamic scenarios has limitations. Future research should focus on exploring more sophisticated learning-based algorithms to enhance AVP adaptability and performance in complex dynamic environment. Full article

The rapid use of renewables like wind power (WP) and photovoltaic (PV) power is essential for a sustainable energy future, yet their volatility poses a threat to grid stability. Electric vehicles (EVs) contribute to the solution by providing storage, while biomass energy (BE) ensures a reliable and sustainable power supply, solidifying its critical role in the stable operation and sustainable development of the power system. Therefore, a dynamic economic emission dispatch (DEED) model based on WP–PV–BE–EVs (DEED_WPBEV) is proposed. The DEED_WPBEV model is designed to simultaneously minimize operating costs and environmental emissions. The model formulation incorporates several practical constraints, such as those related to power balance, the travel needs of EV owners, and spinning reserve. To obtain a satisfactory dispatch solution, an adaptive improved multi-objective evolutionary algorithm based on decomposition with differential evolution (IMOEA/D-DE) is further proposed. In IMOEA/D-DE, the initialization of the population is achieved through an iterative chaotic map with infinite collapses, and the differential evolution mutation operator is adaptively adjusted. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified on the ten-units system. The experimental results show that the proposed model and algorithm can effectively mitigate renewable energy uncertainty, reduce system costs, and lessen environmental impact. Full article

Ginkgo biloba L. plays an important role in biodiversity conservation. Accurate identification of Ginkgo in forest environments remains challenging due to its visual similarity to other broad-leaved species during the green-leaf period and to species with yellow foliage during autumn. In this study, we propose a novel two-stage segment-then-classify (STC) strategy to improve the accuracy of Ginkgo identification from unmanned aerial vehicle (UAV) imagery. First, the Segment Anything Model (SAM) was fine-tuned for canopy segmentation across the green-leaf stage and the yellow-leaf stage. A post-processing pipeline was developed to optimize mask quality, ensuring independent and complete tree crown segmentation. Subsequently, a ResNet-101-based classification model was trained to distinguish Ginkgo from other tree species. The experimental results showed that the STC strategy achieved significant improvements compared to the YOLOv8 model. In the yellow-leaf stage, it reached an F1-score of 92.96%, improving by 24.50 percentage points over YOLOv8. In the more challenging green-leaf stage, the F1-score improved by 31.27 percentage points, surpassing YOLOv8’s best performance in the yellow-leaf stage. These findings demonstrate that the STC framework provides a reliable solution for high-precision identification of Ginkgo in forest ecosystems, offering valuable support for biodiversity monitoring and forest management. Full article