Search Results (1,239)

Rapid global urbanization is increasing the need for accurate, large-scale traffic monitoring to support sustainable transportation and city governance. Satellite video remote sensing offers a unique way to continuously observe urban road networks over large areas. It provides high-resolution spatio-temporal data that is essential for traffic flow analysis, infrastructure assessment, and dynamic urban planning. Moving vehicle detection in satellite video sequences is a basic task that turns raw imagery into useful traffic-state information, supporting these applications. Despite the advantages of satellite video data, detecting moving vehicles in practice remains a tough problem. Objects are extremely small and lack clear appearance details, while low local contrast makes them hard to separate from complex backgrounds. Satellite platform motion also introduces background misalignment and intensity fluctuations, resulting in missed detections and false alarms that hurt monitoring reliability. Furthermore, current methods do not fully exploit temporal motion cues or transform-domain priors, creating a performance bottleneck that restricts their practical use. To solve these problems, this paper proposes a Channel-Attentive Spatio-Temporal-Frequency Alignment (CASTFA) framework to effectively use and combine multi-dimensional features for moving vehicle detection in satellite videos, with the goal of providing high-quality traffic monitoring data to help smart city planning. Specifically, a State Space-Guided Temporal Compression (SSGTC) module first collects information along the time dimension with linear computational complexity, greatly reducing overhead while keeping motion cues that are critical for traffic-state estimation. The compressed temporal features are then processed with a multi-scale Haar wavelet transform to get hierarchical time-frequency representations that capture subtle motion dynamics across different frequency bands. At the same time, a pre-trained backbone network extracts multi-scale spatial features. To allow these different domains to work together, a Cross-Domain Feature Alignment (CDFA) mechanism aligns and combines spatial and time-frequency features through channel-attentive operations. Experimental results on the publicly available satellite video moving vehicle detection dataset show that the proposed CASTFA method consistently outperforms existing approaches, with better precision, recall, and F1-scores across diverse urban scenarios. These results show that CASTFA can provide reliable moving vehicle detection performance under difficult real-world conditions, supporting accurate traffic-flow monitoring and providing valuable geospatial intelligence for smart urban planning, transportation management, and sustainable city development. Full article

Continuous shared control provides an effective approach for intelligent vehicles to balance driving autonomy and system safety boundaries in complex human–machine interaction scenarios. However, existing shared control methods fail to dynamically adapt to the complex and time-varying states of the driver. To address this limitation, a hierarchical shared steering control strategy based on driver states is proposed in this paper. First, an in-vehicle eye tracker is utilized to collect data, and recognition features are extracted based on real-world datasets. Subsequently, a CNN-TCN deep learning algorithm is employed to train a model for identifying five-dimensional driver states. To mitigate excessive intervention and driving experience degradation caused by model misclassifications, a total probability weighting mechanism is developed. This mechanism integrates the real-time confidence distribution output by the neural network with the established baseline safety weights for each driving state, enabling the dynamic and continuous computation of the initial machine control authority. Furthermore, to eliminate high-frequency confidence spikes at the state perception end, a weight-smoothing strategy is designed using an adaptive nonlinear tracking differentiator based on Active Disturbance Rejection Control (ADRC). An autonomous driving controller is then constructed using the Linear Quadratic Regulator (LQR) method to ensure vehicle stability. Finally, Hardware-in-the-Loop (HIL) experiments conducted on a human–machine driving platform with hardware feedback verify the feasibility and superiority of the proposed method. Full article

Hydrotreated vegetable oil (HVO) is considered a promising drop-in alternative to conventional diesel fuel for reducing greenhouse gas emissions in road freight transport. However, empirical evidence on its performance under real-world operating conditions remains limited. This is particularly true for complex logistics systems such as agricultural transport. This study assesses the effect of neat HVO (HVO100) on fuel consumption using high-resolution vehicle operational data collected from three heavy-duty trucks during a full-scale sugar beet logistics campaign in Germany. Vehicle operation was recorded via a manufacturer-independent fleet management system interface and combined with satellite-based positioning data for route reconstruction. After data preprocessing and quality filtering, a total of 3353 valid transport tours were analyzed. Fuel consumption values during HVO100 operation were corrected for density-related measurement bias. The effect of fuel type was evaluated using a linear mixed-effects model. The model accounted for load status, route topography, driving speed, and their interactions. In addition, stratified pairwise comparisons were conducted across operational conditions. The results show that, in the full three-vehicle model, HVO100 was associated with a statistically significant increase in fuel consumption of 0.51 L/100 km under baseline conditions with an empty vehicle, low topographic variability, and medium driving speed, corresponding to approximately 2.3%. In a sensitivity analysis excluding the diesel-only truck, the estimated difference decreased to 0.34 L/100 km and was no longer statistically significant. Load status and topography were identified as the dominant drivers of fuel consumption with substantially larger effects than fuel choice. Overall, the findings indicate that the effect of HVO100 on fuel consumption is small relative to operational variability. Under many real-world operating conditions, operational factors outweighed the differences attributable to fuel type. These findings indicate that switching to HVO100 did not result in a substantial volumetric fuel-consumption penalty in the investigated agricultural logistics system. Full article

The paper addresses the problem of maritime navigation in coastal areas without access to satellite positioning data. The proposed approach relies on visual information from a monocular camera supported by map-derived coastal information. Although the operational concept assumes the use of publicly available cartographic data, the method’s preparation and evaluation require additional processing steps, including high-resolution aerial imagery, GIS-based extraction of coastal features, semantic segmentation, and trained convolutional neural network models. The problem discussed in the paper concerns, for example, Autonomous Underwater Vehicles that seek to reduce underwater dead-reckoning navigation error by surfacing and using information about what is visible around them, in a way similar to how a human would. To solve the above problem, a system was proposed that compares the camera’s representation of the observed coastline with the map representation of the area where the vehicle is most likely located. The system was validated using real-world data. The tests revealed that the information contained in a flat map is insufficient for accurate position estimation. Accuracy is also significantly affected by errors in the semantic segmentation used to extract land features from camera images, as well as by potential errors in the camera viewing angle. The achieved accuracies are sufficient for navigation away from land, but when operating close to land, the proposed system appears significantly insufficient. The paper specifies the system and reports the results. Full article

The rapid expansion of electric vehicle (EV) adoption has increased the demand for efficient charging infrastructure and data-driven approaches for understanding charging behavior. Analyzing charging patterns and identifying abnormal charging sessions are essential for improving charging network reliability, infrastructure utilization, and operational efficiency. This study proposes a comprehensive machine learning framework for EV charging behavior analysis and anomaly detection using real-world charging session data collected from six charging bays. Four charging behavior indicators, namely energy consumption (Usage), charging duration (Duration), average charging output power (Average Output), and Energy Consumption Ratio (ECR), were extracted through a feature engineering process. K-Means clustering was employed to identify distinct user behavior groups, while Principal Component Analysis (PCA) was utilized to visualize cluster separability. Isolation Forest was subsequently applied to detect anomalous charging sessions and investigate abnormal charging behavior patterns. Statistical validation was conducted using Analysis of Variance (ANOVA), and Pearson correlation analysis was performed to examine relationships among charging features and anomaly occurrence. The results identified four distinct charging behavior clusters representing moderate users, regular users, inefficient users, and high-power users. Clustering validation achieved a silhouette score of 0.6086, while PCA retained 89.8% of the total variance using two principal components. An anomaly detection analysis revealed that inefficient charging behavior exhibited the highest anomaly occurrence, whereas regular users demonstrated highly consistent charging patterns. Analysis indicated that average charging output power and ECR were the most influential variables contributing to anomaly identification. ANOVA results confirmed statistically significant differences among all identified clusters (p < 0.001), while correlation analysis demonstrated a strong positive relationship between charging power and charging efficiency (r = 0.95). The anomaly detection framework achieved accuracy, precision, recall, and F1-score of 80.0%. The proposed framework provides a comprehensive approach for EV charging behavior characterization, anomaly detection, and charging infrastructure assessment. The findings can support charging network operators in improving charging efficiency, identifying abnormal charging activities, and enabling data-driven management of EV charging systems. Full article

Arterial corridors present complex safety challenges because signalized intersections, access points, mixed traffic movements, and speed variation can increase the likelihood of severe crash outcomes. Although Advanced Driver Assistance Systems (ADAS) are becoming increasingly common in modern vehicles, limited evidence exists regarding their safety performance in real-world arterial environments. This study investigates crash injury outcomes involving ADAS-equipped vehicles along the US-98 corridor, in Panama City, Florida. Police-reported crash records from 2022–2024 were integrated with vehicle-level ADAS data derived from the National Highway Traffic Safety Administration (NHTSA) Vehicle Product Information Catalog (vPIC) VIN decoding. Multinomial Logistic Regression (MNL) and Random Forest (RF) models were used to examine factors associated with injury severity. MNL results indicate that crashes involving ADAS-equipped vehicles were associated with an approximately 59% lower relative risk of severe injury, while no statistically significant association was observed for moderate injury outcomes. Speeding, alcohol involvement, intersection-related crashes, dark–not-lighted conditions, and rural roadway context were associated with elevated severe injury risk. These findings suggest that ADAS technologies may contribute to severe injury mitigation, but their safety relevance depends on broader behavioral, environmental, and roadway conditions. Full article

The rapid growth of electric vehicle (EV) adoption has increased the importance of understanding charging behavior and improving the operational sustainability of charging infrastructure. Although existing studies have extensively investigated charging demand forecasting, charging load prediction, and charging behavior analysis, limited attention has been given to evaluating the sustainability of individual charging sessions. To address this gap, this study proposes a Sustainable Charging Session Index (SCSI) framework for assessing and classifying real-world EV charging behaviors based on operational charging characteristics. The proposed framework integrates the Entropy Weight Method (EWM), K-Means clustering, Principal Component Analysis (PCA), Random Forest feature importance analysis, and statistical validation techniques. A real-world dataset comprising 1929 EV charging sessions was analyzed, from which 1795 valid charging records were retained after preprocessing. Charging energy usage, average output power, and charging duration were selected as complementary indicators representing energy delivery effectiveness, charging efficiency, and temporal efficiency, respectively. The EWM assigned the highest weights to charging energy usage (0.5119) and average output power (0.4340), reflecting their greater discriminatory capability within the analyzed dataset. Clustering analysis identified three charging behavior archetypes, namely High-Sustainability Charging Sessions, Low-Sustainability Charging Sessions, and Efficient Charging Sessions. PCA demonstrated clear cluster separation, with the first two principal components explaining 97.9% of the total variance. Statistical analyses confirmed significant differences among the identified charging behavior groups (p < 0.001), while one-way ANOVA demonstrated strong internal consistency between the charging behavior clusters and SCSI scores (η² = 0.730). Furthermore, Random Forest analysis identified charging power as the most influential factor in differentiating charging behaviors. The proposed SCSI framework provides an objective and data-driven approach for charging session sustainability assessment, charging behavior characterization, and sustainable charging infrastructure management. Full article

The travel problem of visually impaired people is a worldwide issue that needs urgent attention. Although intelligent guide sticks provide obstacle detection and early warning through multi-sensor fusion and embedded algorithms, existing systems generally cannot model the temporal movement patterns of dynamic obstacles, such as pedestrians and vehicles, thereby hindering intention prediction and active obstacle avoidance. Long short-term memory (LSTM), with its gating mechanism, effectively captures long-term dependencies in trajectories and offers a promising solution. This review compares and analyzes LSTM against other mainstream temporal models under the resource constraints of intelligent guide sticks and finds that LSTM demonstrates a favorable combination in temporal modeling capability, lightweight maturity, and edge deployment feasibility. We categorize five lightweight techniques—architecture simplification, low-rank decomposition, structured pruning, quantization, and knowledge distillation—and examine their compression effectiveness, accuracy preservation, and hardware applicability across typical platforms. Furthermore, this review surveys application cases in speech guidance, trajectory prediction-based obstacle avoidance, positioning and navigation, edge computing, and Internet collaboration, exploring the diverse potential of LSTM in intelligent guide stick scenarios. The findings indicate that, after lightweight processing, LSTM models can meet the deployment requirements of resource-constrained edge devices, suggesting their potential feasibility on resource-constrained hardware platforms. However, existing applications still face challenges in balancing real-time performance and accuracy, meeting stringent resource constraints, and the absence of end-to-end validation on real intelligent guide stick prototypes. The reviewed evidence suggests that LSTM-based prediction represents a promising and practically valuable pathway for transitioning intelligent guide sticks from passive response to active prediction. Future research should prioritize real-world deployment validation, domain-specific data collection, and hardware-software co-design to realize its potential fully. Full article

Microscopic traffic simulation represents one of the most popular tools for analysing and comparing traffic performance in different scenarios of urban mobility. The reproduction of real-world traffic dynamics is its primary requirement for providing time-dependent estimates. This study presents a process to build a microscopic traffic model for an urban area of Athens, developed using high-resolution vehicle trajectories obtained from the pNEUMA dataset. Based on drone-recorded trajectories, a simulation scenario was built, combining a realistic road network model, including traffic light regulation, an estimated traffic demand, and a set of parameters to replicate the observed vehicle behaviour. The modelling process relies on an iterative comparison between simulated outputs and observed vehicle-level trajectory data. The proposed approach evaluates travel time distributions and helps develop an improved model, enhancing its ability to replicate the vehicle’s behaviour. The final calibrated configuration reduced the Wasserstein distance by approximately 55.8% compared with the default SUMO configuration. The results also show that the calibration of microscopic behavioural parameters can substantially affect secondary simulation outputs, including emission estimates relevant for sustainability-oriented traffic analyses. Full article

State estimation is a fundamental component of model-based Fault Detection and Diagnosis (FDD) in dynamic systems, underpinning real-time monitoring, predictive maintenance, and safety-critical operations across industries such as aerospace, power systems, robotics, and autonomous vehicles. Traditional estimators, including the Kalman Filter (KF) and its variants, provide physically interpretable residuals for fault detection but often fail to deliver reliable performance under nonlinear dynamics, modeling uncertainties, sensor faults, and non-Gaussian noise. This paper presents a comprehensive review of state estimation-based FDD approaches, with a particular focus on Artificial Intelligence (AI)-augmented Kalman filtering and hybrid frameworks that integrate Machine Learning (ML) models, including Neural Networks (NNs), Support Vector Machines (SVMs), and Gaussian Processes (GPs), with classical estimation theory. The review systematically evaluates model-based, data-driven, and hybrid methods, comparing their robustness, accuracy, computational efficiency, scalability, and interpretability in complex Cyber-Physical Systems (CPSs). Furthermore, emerging trends and open research challenges are identified, including online adaptation, fault-tolerant estimation, sensor fusion, explainable artificial intelligence (XAI), and deployment in Industry 4.0 and Internet of Things (IoT)-enabled environments. By bridging classical estimation theory with modern AI techniques, this review provides a roadmap for designing intelligent, adaptive, and resilient FDD systems capable of enhancing reliability, operational safety, and real-world applicability. Full article

Accurate fleet electrification assessment requires microscopic traffic simulation grounded in real-world demand, physics-based vehicle models, and routing that respects the lane-connection topology of urban networks. We present ISTVEL (Istanbul Simulation Tool for Vehicle Electrification), an open-source framework that ingests hourly Istanbul Metropolitan Municipality (IMM) loop-detector data, snaps detectors to OpenStreetMap edges, synthesises SUMO demand via a connection-graph Breadth-First Search (BFS) algorithm eliminating teleportation artifacts, and post-processes tripinfo.xml output to compute per-trip energy, use-phase CO₂, and energy operating cost (ECO₁₀₀), correctly distinguishing gross battery draw, regenerative recovery, and net grid consumption. Applied to the Kadıköy district of Istanbul ($3.2{\mathrm{km}}^2$, 08:00–09:00, January 2025, $\approx 2950$ vehicles), ISTVEL demonstrates that a full battery-electric vehicle (BEV) fleet reduces use-phase (operational) CO₂ by 80.1% and energy operating cost by 66.5% versus the internal-combustion-engine vehicle (ICEV) baseline at current Turkish grid intensity ($\gamma =0.45\mathrm{kg}{\mathrm{CO}}_2/\mathrm{kWh}$). However, these figures reflect use-phase emissions only (tailpipe combustion for ICEV; upstream grid emissions $\gamma \times E_{\mathrm{net}}$ for BEV) and exclude vehicle manufacturing, battery production, and upstream fuel extraction. Opportunistic in-transit dynamic wireless power transfer (DWPT) charging at 0.5 km spacing reduces post-trip battery replenishment demand by a further 67.1%, shifting grid supply from post-trip charging to in-transit delivery; total system electricity demand (including DWPT supply) is 895.7 kWh, marginally above the plain-BEV baseline of 848.1 kWh due to charging losses at ${\eta }_{\mathrm{cs}}=0.95$. Framework transferability is further demonstrated on the Fatih district under an identical protocol. Full article

Accurately predicting the strain time series of orthotropic steel bridge decks (OSBDs) is highly challenging due to their strong stochasticity and nonlinear characteristics. This paper proposes a hybrid prediction framework integrating wavelet decomposition with a cascaded Convolutional Neural Network and Long Short-Term Memory architecture. Initially, the raw strain signals are decoupled into temperature-dominated low-frequency trends and vehicle-induced high-frequency dynamic components using the 6-level Daubechies 10 wavelet transform. Subsequently, a deep architecture comprising three CNN layers and two LSTM layers is constructed to precisely extract and learn the local spatial features and long-term temporal dependencies of the decoupled signals. Based on real-world monitoring data, the proposed model is comparatively evaluated against baseline models, including CNN-GRU, LSTM, and Gated Recurrent Unit (GRU), across three time horizons: 24 h, 1 h, and 10 min. The results demonstrate that the proposed method consistently exhibits superior predictive performance across multiple scales. Specifically, the mean absolute percentage error (MAPE) is strictly maintained below 0.6% across all tested horizons, with an R² reaching 0.961. Furthermore, the single-step inference latency is merely 0.63 milliseconds, which is significantly lower than conventional sensor acquisition intervals. This decouple-then-predict analytical framework effectively avoids the feature interference typically encountered when a single network directly processes complex mixed signals. Moreover, while strictly satisfying real-time computational constraints, it provides an undistorted, high-fidelity data foundation for future online fatigue evaluations and continuous state tracking of bridge structures. Full article

Autonomous vehicle (AV) technologies have advanced rapidly in recent years, driving an increasing demand for large-scale, high-quality annotated data. However, collecting and annotating real-world pedestrian video datasets is time-consuming, costly, and often insufficient to cover rare and safety-critical scenarios. Recent world foundation models have demonstrated impressive capabilities in generating realistic videos, yet their suitability for safety-critical autonomous driving applications remains largely unexplored. In this work, we investigate whether current world foundation models can generate driving scenarios that are sufficiently realistic and behaviourally consistent for autonomous driving research. We conduct a case study centred on pedestrian–vehicle interactions captured from ego-vehicle dashcam viewpoints, where subtle behavioural and geometric errors can have significant safety implications. To support this investigation, we develop SynPeDAS, an open research framework comprising a collection of synthetic pedestrian-interaction videos, a reusable generation pipeline for transforming real-world driving footage into synthetic scenarios, an automated evaluation suite, and downstream demonstration code. Through quantitative evaluation and structured human assessment, we identify several recurring failure modes, including dynamic misalignment, depth drift, and object persistence inconsistencies. More importantly, we find that commonly used evaluation metrics frequently exhibit ceiling effects and weak alignment with human judgement, limiting their ability to detect safety-critical behavioural errors. These findings indicate that, despite high perceptual realism at the frame level, current generative world models and existing evaluation methodologies remain insufficient for capturing physically grounded motion and task-critical semantics. Consequently, significant challenges remain before world model-generated videos can be considered reliable for safety-critical autonomous driving applications. SynPeDAS provides an open platform for systematically studying these challenges and developing improved generation and evaluation methods. Full article

Existing publicly available datasets for insulator recognition primarily focus on overhead transmission lines and are commonly acquired using unmanned aerial vehicles. As a result, they often do not reflect the visual complexity of high-voltage substation environments, which are characterized by dense equipment arrangements, structured industrial backgrounds, frequent occlusions, and substantial variation in object scale. To address this gap, we present UVInsDet, a real-world dataset for insulator-string detection and instance segmentation collected during ground-based robotic inspections of an operational 220 kV substation. The dataset comprises 591 visible-spectrum RGB images acquired using a narrow-angle diagnostic inspection camera and contains 1415 manually annotated insulator-string instances represented by pixel-wise segmentation masks. The images cover daytime and nighttime conditions, varying weather scenarios, different viewing angles, and both target-object and negative samples corresponding to realistic inspection workflows. The dataset includes annotations for glass and porcelain insulator strings and provides data in both LabelMe and COCO formats. UVInsDet is intended as a specialized resource for computer vision research in industrial inspection environments. The dataset can support the development and evaluation of object detection and instance segmentation methods, studies of small-object recognition in complex scenes, robustness assessment under varying observation conditions, domain adaptation research, and the development of intelligent monitoring and inspection systems for power infrastructure. Full article

The survival probability of persons requiring rescue after large-scale earthquakes or landslides decreases rapidly with time, and nighttime search operations are constrained by limited visibility. Satellite remote sensing enables wide-area observation at night, but its limited spatial resolution restricts the identification of individual persons. This study develops an automated method for detecting persons in thermal infrared imagery captured by Unmanned Aerial Vehicles (UAVs) using the deep learning model Grounding DINO. To address the limited availability of Unmanned Aerial Vehicle (UAV)-based training data, thermal infrared imagery captured by fixed-point ground cameras was used for training. Spatial resizing and padding were applied to emulate UAV viewpoints and mitigate the ground-aerial domain gap. The proposed model outperformed the baseline on real-world datasets, with substantial Recall improvements in environments with low thermal contrast. An ablation study confirmed that spatial resizing, padding, and position-based augmentation each contribute progressively to detection performance. A comparison with the lightweight YOLOv8n and DETR-based RT-DETR detectors indicated that the Transformer architecture alone does not account for reliable detection in scenes with complex thermal noise. This robustness instead derives from the language-grounded semantic priors of Grounding DINO. An extended evaluation on nighttime imagery indicated that the proposed approach generalizes to nighttime acquisition. Full article