Search Results (127)

The shift toward autonomous driving is steadily reducing the need for human intervention in vehicular operations, but sustaining this shift depends on 6G networks that can reliably multiplex diverse, delay-critical services under deterministic latency, throughput, and reliability constraints. Static over-provisioning can meet the stringent Quality of Service (QoS) requirements of critical traffic, but at the cost of resource starvation for co-existing non-critical services, and it degrades further under adverse channel conditions or network congestion where spectrum must be used efficiently. This motivates dynamic, service-aware scheduling as a core requirement for multi-service 6G vehicular architectures. This paper presents SOVANET+, an extended scheduling technique that jointly accounts for service criticality (critical vs. non-critical), network load, and wireless link quality to allocate resources adaptively across coexisting Vehicle-to-Everything (V2X) services. We evaluate SOVANET+ through extensive simulations of a congested single-cell urban-grid deployment supporting delay-critical automated driving services. Results show that SOVANET+ achieves lower latency and jitter, higher throughput, and improved uplink and downlink reliability compared to existing scheduling approaches, while scaling effectively to large numbers of connected autonomous vehicles, supporting its viability for next-generation intelligent transportation systems. Full article

Vehicle vibration and noise are predominantly driven by road-surface excitation, making robust prediction of these phenomena based on pavement roughness a central challenge in automotive development. This paper presents a fully automated pipeline that begins with high-resolution laser-triangulation scans of actual test tracks to produce centerline elevation profiles. These profiles are processed and classified by a MATLAB routine using ISO 8608-based power-spectral-density analysis to extract the Gh₀ roughness coefficient. Concurrently, in-service acoustic and chassis-vibration data, collected at two representative speeds, are transformed into feature vectors comprising statistical PSD descriptors. A regression model then learns the mapping from these features to Gh₀, evaluating the feasibility of mapping vehicle-borne signatures to roughness metrics. Predicted Gh₀ values drive a profile-synthesis algorithm to generate two-dimensional height grids, which are exported as CRG files and imported into a multibody simulation software (MSC ADAMS) as well as driver-in-the-loop platforms. Simulation results closely reproduce the primary excitation characteristics of the physical tracks, demonstrating a preliminary proof-of-concept pipeline for virtual road surface generation. While the cross-validated regression model indicates limited generalization on the current small dataset ($R^2=-0.2783$), the end-to-end workflow establishes the baseline integration required for future data-driven NVH simulation. To extend applicability beyond a single test vehicle, a set of Vehicle Calibration Transforms is proposed to adapt power-spectral-density features from arbitrary vehicles into the calibrated feature domain. The complete workflow promises to streamline virtual NVH validation, reduce prototype testing, and support full NVH simulator engineering in future research. Full article

This paper presents a validation framework for automated aircraft mass and balance operations, combining statistical process control with integrated risk scoring and addressing the critical gap between traditional centralised load control and emerging automated operations. A dual-validation system integrates univariate control limits, a weighted multivariate T²-type statistic, and composite scoring with operational risk assessment; risk scoring quantifies centre of gravity proximity and maximum mass margins. The framework was validated using 48 flights operated by Airbus A350-900/1000 aircraft. The framework was evaluated using a two-phase design—flights 1–30 for calibration (Phase I) and flights 31–48 for prospective evaluation (Phase II). Across all 48 flights it yields an 81.25% automation-eligibility rate (39 GREEN), with 4.17% YELLOW (non-blocking supervisor notification within a defined service window) and 14.58% RED (mandatory review). RED cases result from cargo offloads, dangerous goods changes, or significant baggage/ZFW variations, while cargo operations show exceptional stability (the exclusively cargo-loaded Compartment 2 has σ = 9 kg, versus 219 kg for total cargo). The framework enables transition from centralised load controller roles to exception-based supervision, with risk scoring outputs driving targeted, proportional safety measures. To the best of the authors’ knowledge, it provides the first systematic method for determining when automated load control can be trusted without human intervention—a critical requirement as the industry transitions towards full automation. Full article

Smart objects in the Social Internet of Things (SIoT), such as cameras, drones, and vehicles, exchange images that act as visual evidence and drive automated decisions. These images can be altered in their pixels or their metadata, replayed, or injected by unauthorized publishers. Central verification services can check them, but such services must be reachable at verification time, form a bottleneck, and observe who produced which image. This paper presents a decentralized scheme that makes an SIoT image object self-verifiable, so that an intermittently connected verifier can check it offline. Each object binds its image hash, its name, its provenance record, and its source identity in one signature, and it carries its own key documents and authorization chain. Trust anchors are Decentralized Identifiers computed as key thumbprints, so no registry, ledger, or certificate authority is queried during verification. Our central technical point is that a signed hash alone gives only name-bound replay prevention. We therefore add temporal layers that such schemes usually omit: a freshness mechanism with interactive, beacon, and transparency log variants; signed status lists with a proven bounded staleness revocation guarantee; and monotone epochs that resist rollback of rotated keys and documents. We prove the base goals by reduction to signature unforgeability and hash collision resistance under a Dolev–Yao adversary, and we prove the temporal properties as unbounded inductive invariants discharged in Z3. An Ed25519 and SHA-256 implementation verifies a typical image in under two milliseconds with about 1.6 kB of metadata. The evidentiary levels are stated separately and are not interchangeable. The base object is proven, implemented, and measured; the freshness, revocation, and rollback layers are proven but not measured; the pseudonymous mode is design-only. “Self-verifiable” means that provenance is checked cryptographically from the object and one anchor. It does not mean the scheme proves that the captured scene is real, and it is conditional on a preconfigured root identifier and, for the temporal layers, on a status list or time source. Full article

Low-visibility weather poses a significant threat to aviation safety and operational efficiency. Lhasa Airport is situated in a high-altitude river valley region prone to dust events. Clarifying the characteristics and dominant factors of low-visibility weather is crucial for enhancing aviation meteorological support capabilities. This study systematically analyzed the weather types, spatiotemporal distribution characteristics, and key meteorological influencing factors of low-visibility events using ground-based automated meteorological observation data, monthly summary records, and wind lidar data from Lhasa Airport from January 2020 to July 2024. The results indicate that dust weather (blowing sand and floating dust) is the primary cause of low visibility, accounting for over 90% of low-visibility days. Low-visibility events exhibit significant monthly variations, with the longest cumulative duration occurring in January and February, while being extremely rare from July to November. Under low-visibility conditions, visibility levels are predominantly concentrated in the 3–4 km range (72.5%), and most events last less than one hour. Factor analysis reveals that north, easterly, and west winds are commonly associated with dust-induced low visibility. Wind speed demonstrates a significant negative correlation with visibility and serves as the primary driving factor. Moderate relative humidity is most conducive to maintaining visibility between 3 and 5 km. Case studies further confirm that sustained strong low-level winds (≥10 m/s), high surface wind speeds (≥7.5 m/s), and intense upward motion are the key dynamic conditions for triggering and maintaining dust-related low-visibility processes. This study presents, for the first time, quantitative thresholds for key meteorological parameters and their season-dependent characteristics for dust-induced low-visibility events at Lhasa Airport, and proposes actionable forecasting indicators. These findings offer clear practical implications for aviation meteorological services at high-altitude valley airports. Full article

Autonomous robotic systems are being deployed in commercial, healthcare, logistics, and mixed-use built environments at a rate that significantly outpaces the adaptive capacity of existing building design and management paradigms. Buildings have historically been conceived exclusively for human occupants, and the resulting absence of a structured, scalable framework for evaluating or designing robot-ready facilities constitutes a critical gap in both research and professional practice. This article introduces the Robot-Friendly Buildings Level of Service (RFB-LOS) framework: a five-tier hierarchical classification system that characterises the degree to which a built environment supports autonomous robotic operations across six evaluative dimensions—building intelligence, active infrastructure, architectural planning, accessibility, observability, and safety. The framework spans a continuum from Robot Excluded (RFB-LOS-1), in which a building has no awareness of its robotic occupants, to Physical AI Robot Optimised (RFB-LOS-5), in which a Physical AI middleware layer assumes the highest command authority within a coordinated human–robot–building ecosystem. Drawing structural inspiration from the SAE J3016 Levels of Driving Automation, the EU Smart Readiness Indicator, HIMSS EMRAM, and BREEAM/LEED sustainability certification, the RFB-LOS framework is positioned as a foundational standard for the built environment and systems engineering community. Five real-world case studies spanning retail, hospitality, healthcare, and corporate sectors across four countries validate the framework’s tier assignments against observed operational outcomes. Full article

This study investigates the spatiotemporal dynamics and driving mechanisms of Water Supply Ecosystem Service Value (ESV) in the North China Plain from 2002 to 2022. Addressing the critical challenges of water scarcity and ecological degradation in this densely populated and agriculturally intensive region, the research develops an integrated framework to quantify the relative contributions of multi-dimensional drivers to the water supply service (quantified by biophysical supply, W). A Particle Swarm Optimization (PSO) algorithm was employed to automate hyperparameter tuning for XGBoost and Random Forest models, with model interpretability enhanced via SHAP (SHapley Additive exPlanations) to elucidate non-linear feature importance and directional impacts. Results demonstrate that the PSO-XGBoost model outperforms PSO-Random Forest in predictive performance (R² = 0.8013 vs. 0.7443). The total water supply exhibited a significant annual decline of 1.98 billion m³ (p < 0.05), with 53.4% of the study area showing significant pixel-level temporal trends. The supply structure is dominated by soil moisture (80–90%), while externally transferred water, despite increasing rapidly, exhibits high interannual variability. SHAP analysis identifies vegetation cover (NDVI), clay content, GDP, and population density as the predominant drivers. Notably, GDP shows a strong negative correlation with water supply, reflecting a trade-off where intensive socio-economic expansion increases water consumption at the expense of ecosystem supply capacity. Methodologically, the PSO-XGBoost-SHAP framework enables both high predictive accuracy and detailed attribution of driving factors. These findings highlight the strategic importance of soil water (“Green Water”) conservation and offer actionable insights for adaptive water resource management, providing a replicable analytical approach for other regions facing similar hydrological challenges. Full article

Low-speed automated driving (LSAD) shuttles operate in complex urban environments where abrupt braking can affect safety, service quality, and operational interpretability. This study proposes a telemetry-based workflow for the detection and characterization of hard braking (HB) events in autonomous shuttle operations. The workflow includes preprocessing of autonomous in-service telemetry data, deterministic HB detection under irregular sampling, evidence-based attribution using diagnostic and obstacle-related signals, and driving-context characterization through K-means clustering, applied to a 62-day dataset from an autonomous on-demand shuttle operating on a fixed 2.8 km urban loop in Turin. After preprocessing, 71% of the 16,670,518 observations are retained. The analysis identified 734 HB events, of which 89% are linked to specific contextual conditions, highlighting environmental and operational influences on safety-critical situations. Driving-context analysis relies on 316,280 observations collected at 1 Hz and yields a nine-cluster solution. When projected back onto the route through waypoint-level modal regimes, HB events are found to be over-represented in high-speed segments. These results show that HB events can be interpreted not only as a threshold exceedance, but as an operational indicator linked to route-level driving regimes. The proposed framework supports data-driven safety assessment and operational decision-making in autonomous shuttle systems by researchers and practitioners. Full article

This study examines public acceptance of autonomous shuttle services in a real-world urban context by integrating expectation–experience dynamics, system characteristics, and configurational analysis. Based on survey data collected from users of Seoul’s self-driving shuttle operating along the Cheonggyecheon corridor (n = 566), a mixed-method approach combining structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) is employed. The results confirm that pre-use expectations significantly shape post-use experiences, supporting the expectation–confirmation framework. Notably, perceived autonomy exhibits a significant negative effect on user attitudes, suggesting that users may prefer partial automation rather than full autonomy during early deployment stages. In contrast to prior research, trust and satisfaction do not significantly influence attitudes, suggesting a context-specific pattern in which user evaluations may be shaped more by system-related considerations than by psychological responses in this early-stage pilot setting. Furthermore, perceived human backup plays a dual role by enhancing experienced safety while simultaneously reducing perceived autonomy, highlighting a human backup paradox in early-stage deployment. Contextual factors, including integration value and fare acceptability, significantly influence continuation intention, highlighting the importance of system-level integration in public transport. The fsQCA results further uncover multiple configurational pathways leading to high acceptance, demonstrating causal complexity and equifinality. These findings advance understanding of user acceptance in early-stage autonomous mobility systems and provide both practical and policy-relevant insights for designing safe, trustworthy, and system-integrated AI-enabled transport services, thereby supporting the sustainable deployment of autonomous transport systems in smart cities. Full article

Pavement rutting is caused by grooves formed by vehicle traffic, affecting driving comfort, safety, and service life. Rutting detection methods have evolved from manual and automated approaches to intelligent detection for smart cities and maintenance. However, lightweight intelligent detection still faces challenges such as insufficient accuracy and technical complexity, and a mature system has yet to be established. This study aims to develop a portable intelligent terminal for pavement rut detection, which can address the challenges associated with traditional pavement rut detection while providing accuracy and reliability. In this study, rutting detection experiments were performed on a full-scale accelerated loading track to collect data on vibration acceleration, angular velocity, and attitude angles. Comparative experiments were carried out between traditional and lightweight detection methods. Subsequently, GRU-CNN, LSTM–Transformer, GRU, and LSTM models were developed to analyze and compare their performance in predicting rutting depth. The results show that the terminal operates stably, offering convenient usability and reliable data acquisition. Furthermore, vehicle angular velocity and roll angle emerge as critical indicators reflecting rutting impacts on driving states and prove suitable for pavement rut depth detection. The proposed GRU-CNN model achieves superior accuracy and overall performance relative to widely used models. Under synchronous detection conditions, the lightweight method yields a mean absolute error (MAE) of 1.22 mm, achieving performance improvements of 17.32%, 8.74%, and 10.08% over the LSTM–Transformer, GRU, and LSTM models, respectively. Additionally, the method yields a mean absolute percentage error of approximately 10.6%, representing error reductions of 15.87%, 19.08%, and 23.74% compared to the aforementioned baseline models, which meets application requirements. Innovation lies in the development of a lightweight intelligent terminal and GRU-CNN hybrid model that integrates vehicle dynamic parameters for large-scale pavement rutting detection. This study presents a lightweight, real-time pavement rutting detection method based on vehicle operation data for the construction and maintenance of smart cities and intelligent transportation infrastructure, combining the features of high cost effectiveness, high accuracy, and ease of large-scale application. Full article

High-efficiency electric motors represent a core enabling technology for sustainable industrial systems, providing substantial opportunities to reduce electricity consumption, operating costs, and associated greenhouse gas emissions across motor-driven processes. This paper presents a structured synthesis of recent progress in high-efficiency motor technologies within the IE3–IE5 efficiency classes, with emphasis on design innovations in electromagnetic optimization, advanced materials, and thermal management that collectively improve efficiency retention, reliability, and service lifetime under practical duty cycle conditions. Beyond component-level advances, the review analyses how high-efficiency motor–drive systems are being embedded within Industry 5.0 manufacturing environments, where human-centric automation and data-driven intelligence extend motor functionality toward adaptive, condition-aware operation. In this context, the integration of IoT-enabled sensing, AI-based analytics, and digital twin models supports predictive maintenance, real-time condition assessment, fault diagnostics, adaptive control, and duty cycle-responsive energy optimization, thereby improving both energy management and operational resilience. The paper also discusses implementation considerations that commonly constrain industrial adoption, including interoperability with legacy infrastructure, control architecture compatibility, data quality and model robustness, cybersecurity concerns, and lifecycle-oriented sustainability requirements such as material criticality and end-of-life pathways. Representative industrial case studies are synthesized to illustrate typical deployment architectures, observed implementation effects, and recurring technical challenges, together with practical mitigation strategies. This article advances the viewpoint that, under the Industry 5.0 paradigm, the value of high-efficiency motors is evolving from a component-level efficiency upgrade to a cyber-physical enabling asset that shapes lifecycle carbon performance and manufacturing resilience; realizing this shift requires integrated co-design spanning electromagnetics, thermodynamics, information science, and control. Full article

Level 2 driving automation requires continuous driver supervision, yet common attention metrics often capture gaze allocation rather than the structure of supervisory scanning. This study proposes a quantitative approach for describing supervisory gaze organisation using first-order Markov chain analysis of gaze transitions. Forty-three licensed drivers ($N=43$) completed a simulator drive with Level 2 automation for either 5 or 15 min (between-subjects), representing typical Japanese expressway intervals between service areas. Supervisory behaviour was analysed at the scenario level, without introducing secondary tasks, allowing attentional drift to emerge naturally under automation. Eye-tracking data were manually annotated frame-by-frame at 60 Hz and modelled as transition probability matrices across key Areas of Interest (AOIs): road centre, mirrors, periphery, and the human–machine interface. Compared with the 5 min condition, the 15 min condition showed fewer mirror-to-road-centre recovery transitions and slower System-Recognised Reaction Time (SRRT) at the takeover request. These patterns suggest a gradual weakening of supervisory gaze organisation rather than a simple loss of attention. The proposed framework offers a reproducible way to calibrate driver monitoring and evaluate human–machine interfaces by linking gaze transition probabilities to takeover readiness. By quantifying how supervisory behaviour reorganises under extended automation in realistic driving scenarios, this study provides a practical basis for the development of safety-relevant driver monitoring indicators in Level 2 driver assistance systems. Full article

Research on Cooperative Driving Automation (CDA) has advanced to overcome the limited perception range of onboard sensors and the difficulty of inferring surrounding vehicles’ intentions by leveraging vehicle-to-everything (V2X) communications. This paper models how an autonomous vehicle receives cooperative sensing and cooperative maneuvering information generated at an edge roadside unit (edge RSU) that integrates roadside units (RSUs) with multi-access edge computing (MEC), and how the vehicle fuses this information with its onboard situational awareness and path-planning modules. We then analyze the performance gains of edge RSU-enabled services across diverse traffic environments. In a highway-merging scenario, simulations show that employing the edge RSU’s sensor sharing service (SSS) reduces collision risk relative to onboard-only baselines. For unsignalized intersections and roundabouts, we further propose a guidance-driven Hybrid Pairing Optimization (HPO) scheme in which the edge RSU aggregates CAV intents/trajectories, resolves spatiotemporal conflicts via lightweight pairing and time window allocation, and broadcasts maneuver guidance through MSCM. Unlike a first-come, first-served (FCFS) policy that serializes passage, HPO injects edge guidance as soft constraints while preserving arrival order fairness, enabling safe concurrent passage opportunities when feasible. Across intersections and roundabouts, HPO improves average speed by up to 192% and traffic throughput by up to 209% compared with FCFS under identical demand in our simulations. Full article

The automation of logistics processes in companies is an essential part of the modernization and advancement of companies around the world. This article deals with the issue of deploying a selected type of automated guided vehicle (AGV) in very specific conditions. AGV is suitable for optimizing the circular supply chain in specific conditions of a manufacturing company. The deployment of AGVs is governed by the production needs of the section in question. The selection criterion was therefore the quantity of products that needed to be transported on the selected route. The article uses a new calculation of AGV requirements based on the saturation of individual components from the picking location to the assembly line. The ratio indicator was considered: driving time per shift, depending on the length of working time. Based on this calculation, the most effective option was applied from the individual solutions. Based on our calculation, we arrived at a requirement for three AGVs, plus a reserve, i.e., four. Our selected calculations were applied to the FRONT and TOP positions, where a decision was made between the option of using under-run AGVs or a truck. The decision was made based on the saturation level, and the result is described at the end of the discussion. The AGV is one of the tools for sustainable supply chain management in a company. However, it is important to evaluate the total cost of ownership, including lower labour costs, less risk of damage to goods, higher productivity, and long service life of the trucks. Thanks to these factors, AGVs often prove to be economically advantageous. Full article

This study rigorously evaluated the integration of energy-harvesting systems within electric vehicles to prolong battery service life. A laboratory-scale system was configured utilizing a scale electric vehicle with a 12.6 V lithium-polymer (Li-Po) battery alongside an automated control platform to precisely estimate the real-time State of Charge (SoC) through monitoring of current, voltage, and temperature of the vehicle battery under three distinct driving conditions: (A) constant velocity at 30 km/h, (B) variable velocities exhibiting a sawtooth profile, and (C) random speed variations. Wind energy was harvested employing Savonius rotor microturbines, with assessments conducted on efficiency losses and drag coefficients to determine the net power yield for each operational profile, which was found to be marginally positive. Considering the energy consumption of electric vehicles based on 2017 U.S. EPA fuel economy data, the maximal recovered energy corresponded to 0.0833% of auxiliary system demand, while the minimal recovery was 0.0398%. These results substantiated the necessity for continued research into sustainable energy management frameworks for electric vehicles. They emphasized the critical importance of optimizing the incorporation of renewable energy technologies to mitigate the environmental ramifications of the transportation sector. Full article