Search Results (1,345)

Contemporary buildings and urban spaces are increasingly expected to support psychological well-being—a quality often termed “restorativeness.” Conventional approaches to quantifying restorativeness rely on subjective surveys or coarse green metrics, failing to capture how specific building morphologies and street-level visual configurations shape restorative experiences, particularly for stress-prone groups such as young adults. This study develops a deep-learning-driven framework linking building visual elements to youth-specific perceived restorativeness, using Changsha, China, as a testbed. The framework comprises three AI-powered modules: the TrueSkill algorithm trains a deep learning model to predict six dimensions of youth perception (e.g., beautiful, clean, safe) from pairwise comparisons of street view images; the Mask2Former architecture segments street-level imagery into 18 building and street attributes; and the XGBoost-SHAP pipeline uncovers nonlinear associations and threshold-like patterns between these attributes and the composite Built Environment Restorativeness Index (BERI). Results reveal three key insights: tree coverage shows a sustained positive association without saturation; building density exhibits a weakening association at high levels, suggesting possible saturation; and road proportion follows a bidirectional pattern, shifting from negative to positive beyond a certain range. Spatially, high BERI zones concentrate where ecological assets and diverse building functions co-occur, while youth perception exhibits systematic mismatches (e.g., “beautiful but not clean,” “safe but not lively”), traceable to imbalances in building form, street furniture, and commercial mix. These findings advance AI-assisted evaluation of built environments by shifting from one-dimensional metrics to interpretable, design-relevant diagnostics, offering a replicable evidence base for crafting youth-responsive buildings and streets. Full article

This article presents a method for calibration of dynamic vehicle weighing systems (WIM—Weigh-In-Motion) involving the calibration of all WIM stations operating within a given road network segment as a single process. A key assumption of the method is the presence of at least one scale with significantly higher accuracy than the calibrated systems in this part of road network. This reference scale function may be played by a static scale, slow-pass scale (LS-WIM—Low-Speed WIM) for measurement of vehicle axle load or by a selected WIM system with heightened accuracy. Both the reference scale and all systems undergoing calibration must be equipped with a system for the automatic recognition of vehicle registration number plates. The reference scale makes it possible to determine axle load values considered as benchmark values. Then, for each vehicle weighed on the reference scale and subsequently on any WIM system operating within the analysed area, the relative difference between the reference result and the WIM system measurement is calculated with respect to the reference value. This difference forms the basis for the operation of the algorithm estimating the coefficients of the static characteristic of the calibrated WIM system (so-called calibration coefficients), which are then used to determine corrected weighing results. The estimation of the coefficients is updated after each identified vehicle that has previously been weighed on the reference scale is considered. The article presents both the results of simulations and experimental studies concerning the proposed spatial method of calibration. The results obtained allow for an assessment of the effectiveness of the proposed solution. As can be seen from the analyses conducted, this method leads to a significant reduction in systematic error of vehicle weight measurement. Unfortunately, it does not eliminate random errors. The spatial calibration approach described in this paper has certain limitations. The main ones include the impact of ANPR system errors on calibration effectiveness, cases where a vehicle is unloaded or loaded between WIM stations, and the propagation of systematic errors from the reference systems to the other WIM systems. A significant advantage of the proposed spatial calibration method is that it can operate effectively using weighing data from a single reference WIM system and does not require heavy traffic volumes. Full article

To address the safety concerns of incapacitated patients caused by changes in vehicle pose during the operation of an autonomous rescue vehicle on an unstructured road surface, this paper proposes an active leveling control scheme based on the Stewart platform. First, a complete kinematic and dynamic model of the Stewart platform and a double-layer platform leveling control model were established. Subsequently, a non-singular terminal sliding-mode control (NTSMC) algorithm based on a radial basis function (RBF) neural network was designed. By using the neural network to approximate aggregate uncertainties online, high-precision control of the Stewart platform was achieved. Additionally, to enhance perception capabilities in dynamic environments, an ORB-SLAM3 algorithm was proposed that integrates the YOLO11n-Seg instance segmentation algorithm. This approach effectively filters out dynamic feature points, enabling robust vehicle pose estimation. Finally, a physical double-layer Stewart platform experimental system was constructed to comprehensively validate the proposed control and vision algorithms. Full article

Recent advances in satellite technology have significantly progressed, yet acquiring high-quality images with meaningful labels for Earth observation missions remains a costly and time-intensive process. Furthermore, captured scenes frequently exhibit defects such as misaligned color channels, extensive cloud cover, or repetitive patterns in similar environments. Fortunately, the evolution of generative artificial intelligence offers a solution by enabling the creation of realistic synthetic scenes, simulating the characteristics of any targeted imager, and thereby mitigating the scarcity of authentic data. This paper demonstrates the feasibility of transferring knowledge from specialized AI-generated datasets to Earth observation missions. Leveraging a novel dataset of Spanish map tiles, Pix2Pix, CUT, and ControlNet models were implemented to synthesize satellite imagery. To analyze structural and topological generalizability, identical U-Net instances were trained on the resulting collections for building, road, and water segmentation tasks, and subsequently tested on independent authentic imagery. The results reveal a clear decoupling between visual realism and functional utility. Incorporating synthetic samples into hybridized training datasets successfully surpassed the limitations of using real data alone, increasing maximum Dice scores by 0.9% (to 54.1% for buildings), 2.3% (to 38.6% for roads), and 4.1% (to 46.5% for waterbodies). This systematic validation establishes structural-guided synthetic data augmentation as a robust, adaptable strategy for Earth observation applications across diverse sensors and geometric objectives. Full article

This study presents an energy-based assessment of a battery electric sport utility vehicle (SUV) tested under high-altitude and high-gradient real-world conditions in Ambato, Ecuador, at approximately 2500 m above sea level. A low-cost instrumentation setup composed of a Global Navigation Satellite System (GNSS) device, a Fluke 393 FC clamp meter, and an On-Board Diagnostics II (OBD-II) interface was used to evaluate zero, positive, and negative road-gradient conditions in Normal and Sport driving modes. The results show that positive gradients increased the acceleration energy from 0.0454 to 0.0658 kWh in Normal mode and from 0.0351 to 0.0535 kWh in Sport mode. In contrast, negative gradients favored regenerative braking, with Normal mode reaching a net energy balance of $-0.0249$ kWh and a segment-level recovery ratio of 194.38%. This value reflects the contribution of gravitational potential energy. Sport mode showed lower regenerative performance, particularly during uphill operation, where the recovery ratio decreased to 8.96%. These findings demonstrate that low-cost instrumentation can capture representative route-level energy trends and support real-world electric vehicle (EV) energy assessment in topographically complex high-altitude environments. Full article

Accurate recognition of fractures on tunnel faces is essential for evaluating surrounding-rock integrity and ensuring excavation safety, yet it remains difficult because fracture traces are slender, irregular, discontinuous, and easily obscured by complex rock textures and illumination variability. This study proposes MF-DeepLabv3+, an enhanced DeepLabv3+-based semantic segmentation framework for tunnel-face fracture identification and geometric characterization. Unlike existing attention-based DeepLab variants that mainly enhance global feature representation, MF-DeepLabv3+ is specifically designed for thin and discontinuous tunnel-face fracture segmentation by integrating a Multi-Scale Cross Attention module for multi-receptive-field feature interaction, a Feature Smoothing Module for noise suppression and fracture-continuity enhancement, and a lightweight MobileNetV2 backbone for improved computational efficiency. A dataset of 2153 annotated images collected from the Qingdao Jiaozhou Bay Second Subsea Tunnel and the Yantai Urban Rapid Road Tunnel was established for training and evaluation. Considering the strong class imbalance between fracture and background pixels, Accuracy is reported only as an auxiliary metric, while mAP, mIoU, per-class IoU, and fracture-specific Precision, Recall, and F1-score are emphasized to provide a more reliable assessment of segmentation performance. Comparative and ablation experiments show that MF-DeepLabv3+ achieved 82.56% mAP and 62.99% mIoU, with an auxiliary Accuracy of 92.47%. Compared with the original DeepLabv3+ baseline, the proposed model achieved a substantial improvement in mAP and a modest improvement in mIoU, indicating enhanced fracture recognition capability and slightly improved region-level overlap and a moderate increase in computational cost in exchange for improved segmentation performance. Fracture grouping and post-processing were further performed using edge detection, Hough transform, connected-component analysis, and fitted-line geometry to estimate fracture length and width. The proposed method therefore enables more reliable tunnel-face fracture recognition and provides quantitative geometric information for engineering assessment and geological interpretation. Full article

Cameras and IMUs on heavy mining trucks supply the visual signal that Advanced Driver Assistance Systems (ADASs) use in open-pit operations. Haul roads in a surface mine are unstructured and unmarked, so a perception model must be both accurate and fast. We address this with a video-based multitask pipeline for a mining Driver Support System (DSS): a single BiSeNetV1 network produces drivable-area segmentation and steering-direction classification in one forward pass. Training used only 100 frames sampled non-sequentially from in-cab recordings of a real open-pit mine; evaluation used two full onboard sequences. To exploit temporal redundancy without annotating video, we propose an Adaptive Clockwork (A-CW) inference scheme: the spatial path runs on every frame, while the context path is refreshed only on keyframes whose cadence is set by the classification output, the same signal shown to the driver as a steering hint. This classification-guided policy increases context updates on curved segments, where the scene changes more rapidly, and reduces them on straight sections, where semantic redundancy is higher. The selected A-CW configuration was evaluated on full temporal test sequences, including one route kept entirely outside the training source. On this unseen route, A-CW achieved 94.70% road-class IoU and 73.68% Top-1 Accuracy. GPU-only throughput increased from about 55 FPS with frame-by-frame inference to 168.01 FPS, and display-excluded end-to-end processing in the simulated ADAS pipeline remained at approximately 37.5 FPS. Full article

As autonomous driving technology expands from structured urban roads to unstructured outdoor environments, precise understanding of complex terrain has become a critical requirement for ensuring safe vehicle navigation. However, outdoor environments are characterized by high dynamics, drastic illumination variations, ambiguous category boundaries, and prohibitive annotation costs, making traditional supervised learning methods that rely on large amounts of pixel-level annotations difficult to generalize. In this paper, we propose a novel dual-path bidirectional interactive encoder, termed LapDINO, that effectively combines the strong semantic generalization capability of the self-supervised foundation model DINOv3 with the multi-scale frequency analysis capacity of the Laplacian pyramid. Specifically, we leverage DINOv3 to extract global semantic features as a “semantic map”, while simultaneously obtaining multi-scale high-frequency details through Laplacian pyramid decomposition as “structural contours”. Building upon this, we design a bidirectional cross-attention fusion mechanism that enables dynamic interaction and mutual refinement between semantic information and geometric details. Furthermore, we introduce a multi-branch attention enhancement module that extracts pyramid features from three complementary perspectives. To address domain shift, we design lightweight visual adapters that enable efficient fine-tuning of the frozen DINOv3 backbone. Finally, we construct two off-road terrain segmentation datasets, VOTD and VOCD, to facilitate research in this domain. Experimental results demonstrate that the proposed method achieves state-of-the-art performance, striking an optimal balance between accuracy and computational efficiency, thereby providing a robust and efficient engineering solution for terrain perception in off-road environments. Full article

Decarbonizing heavy-duty road transport requires comparing zero-emission options to guide infrastructure investments along strategic corridors. This study develops a scenario-based techno-economic model to evaluate hydrogen fuel cell trucks (HFCTs) and battery electric trucks supported by dynamic wireless power transfer (DWPT) on a 100 km segment of Italy’s A4 motorway in 2030 and 2050 scenarios. The framework integrates traffic flows, vehicle archetypes, infrastructure sizing, and end-to-end energy chains (power-to-hydrogen-to-wheel for hydrogen and grid-to-wheel for WPT) to estimate capital and operating costs, efficiencies, and energy demand. Results show that hydrogen refueling infrastructure requires lower initial investment (approximately €60 million CAPEX and €20 million annual OPEX) than wireless charging systems (€80 million CAPEX and €15 million OPEX). However, WPT achieves significantly higher grid-to-wheel efficiency (96% vs. 62%) and lower per-vehicle energy demand (18 MWh/year vs. 25 MWh/year). These findings highlight a fundamental trade-off: hydrogen solutions offer operational flexibility and are better suited to long-haul or low-density contexts, while WPT systems are more efficient and become increasingly competitive in high-traffic corridors with high infrastructure utilization. Overall, the results suggest that no single technology universally dominates and that optimal deployment depends on traffic density, infrastructure usage, and system integration. A combined implementation of hydrogen and wireless charging technologies may provide the most effective pathway to balance efficiency, flexibility, and cost in future heavy-duty transport systems. Full article

As the global population continues to age, mental health issues such as depression, anxiety, stress, loneliness, and social isolation among older adults are receiving increasing attention. The built environment is closely associated with older adults’ daily mobility, environmental perception, social participation, and mental health and well-being, but the evidence remains heterogeneous across spatial contexts, environmental indicators, and study designs. Previous umbrella reviews have summarized broad links between the built environment and healthy aging, but less attention has been paid to recent original empirical studies published after the COVID-19 pandemic, the distinction between objective environmental exposure and subjective environmental perception, and the role of social participation as a pathway linking environmental conditions to mental health and well-being. This study employs a systematic literature review approach, searching and screening peer-reviewed empirical studies published between 2015 and January 2026 that focus on the associations between the built environment and older adults’ mental health and well-being. PubMed, Scopus, and Web of Science databases were used for searching, supplemented by manual searching. After title and abstract screening and full-text evaluation, a total of 60 studies were included. Subsequently, a comprehensive analysis was conducted on aspects such as research design, spatial scale, environmental indicators, types of mental health outcomes, and potential pathways of action. In this review, core mental health and well-being outcomes included negative outcomes, such as depression, anxiety, stress, psychological distress, loneliness, and social isolation, and positive outcomes, such as life satisfaction, subjective well-being, psychological well-being, and mental well-being. Social participation was examined as a behavioral and psychosocial pathway rather than as a core outcome. Emerging methods, including street-view image analysis, FCN-based semantic segmentation, and XGBoost-SHAP, were examined because they can refine environmental exposure measurement and support variable-importance interpretation, rather than because they provide causal evidence. The main synthesis suggests that several built environment factors are associated with older adults’ mental health and well-being, although the strength and consistency of evidence vary across outcome types, spatial contexts, and study designs. (1) Exposure to green and blue spaces, quality of public open spaces, walkability and accessibility, accessibility of neighborhood facilities and services, housing and living conditions, and positive environmental perception are mostly associated with lower levels of depression, anxiety, stress, and loneliness, as well as higher levels of life satisfaction, subjective well-being, and psychological well-being. (2) Conversely, adverse environmental exposures such as proximity to roads, pollution, non-vegetated spaces, and high-intensity urbanization are more likely to exacerbate negative psychological outcomes. Existing evidence also suggests that social participation is one of the important behavioral pathways through which the built environment is linked to the mental health of older adults, but it is not the only mechanism. (3) In addition, the direction and intensity of environmental associations remain heterogeneous under different spatial scales, indicator types, and research methods. Overall, this review contributes by organizing recent empirical evidence into a built environment–social participation–mental health and well-being framework, while emphasizing that most findings should be interpreted primarily as evidence of association rather than as stable or uniform causal effects. Full article

Trajectory data is valuable for applications such as urban planning, but its public availability is often limited by privacy concerns and data collection costs. While recent diffusion models have shown promise in generative tasks, existing methods rarely integrate personalized conditioning with road network constraints. As a result, they struggle to simultaneously achieve personalized mobility modeling and high road-network spatial validity, resulting in limited trajectory quality. In this paper, we propose Sem-RoadDiff, a symmetry-aware dual-guided diffusion model for personalized and road network-constrained trajectory generation. Specifically, our model incorporates two key components. First, we design a semantic preference guidance mechanism to encode user history into a preference-weighted user embedding using a temperature-scaled softmax. This enables the model to capture user-level mobility patterns without directly using raw trip-level records as generation conditions. Second, we introduce a road-aware mechanism to improve overall spatial validity, employing a spatial validity loss derived from the User Mobility Transition Graph. From a symmetry perspective, Sem-RoadDiff aims to preserve distributional symmetry between real and generated trajectories while respecting the inherent asymmetry of directed road-network transitions. Extensive experiments on the Geolife and Porto datasets demonstrate that our approach improves trajectory distributional fidelity compared with the evaluated baselines and improves road-segment connectivity over the diffusion-based baseline. Full article

Traffic sign segmentation is a fundamental component of intelligent transportation systems and autonomous driving, where reliable pixel-level perception is required under challenging real-world conditions such as illumination variations, occlusion, scale diversity, and complex urban backgrounds. In this work, we propose Residual–Recurrent Kolmogorov–Arnold Network U-Net (R2KAN-U-Net), where “R2” denotes the integration of residual convolutional learning and recurrent KAN-based feature refinement. The proposed architecture combines residual U-Net feature extraction, multi-scale KAN fusion, and recurrent KAN refinement to improve pixel-level traffic sign segmentation under challenging road-scene conditions. The proposed framework integrates three complementary components: (1) residual convolutional blocks for stable feature propagation; (2) a multi-scale KAN fusion bottleneck for capturing contextual information at different receptive fields; and (3) recurrent KAN refinement modules for iterative enhancement of discriminative features. Unlike conventional convolutional architectures, the proposed KAN-based formulation replaces linear transformations with learnable univariate functions, enabling adaptive nonlinear feature modeling. We conduct extensive experiments on a custom dataset containing 9300 annotated urban traffic scene images, as well as on the ADE20K and Cityscapes benchmarks. On the custom dataset, the proposed R2KAN-U-Net achieved a Dice coefficient of 0.92 and an IoU score of 0.89, providing a strong accuracy–efficiency trade-off for traffic-sign foreground segmentation. It achieves competitive segmentation accuracy compared with recent CNN-, transformer-, and state-space-based segmentation models while using fewer parameters and lower computational cost. Additional low-light experiments demonstrate improved segmentation stability, with R2KAN-U-Net achieving the highest low-light Dice score of 0.88 and a competitive low-light IoU of 0.79. Furthermore, the proposed architecture maintains competitive computational efficiency with only 24 M parameters, 44.8 G FLOPs, and near-real-time inference at 13 ms per image. The experimental results demonstrate that integrating KAN-based function-space learning with residual and multi-scale feature refinement provides an effective and computationally efficient solution for robust traffic sign segmentation in complex driving environments. Full article

Road traffic accidents represent a critical road safety issue, the severity of which depends on the complex interplay of multiple factors. This issue directly impacts Target 3.6 of Sustainable Development Goal (SDG) 3, which aims to halve global deaths and injuries by 2030, and SDG 11, which focuses on safe and sustainable transport systems. The study of these factors and their interrelationships is important in the scientific literature. The objective of this study is to analyze the factors that determine the severity of road traffic accidents, identifying the most important ones and their correlations. A dataset containing variables such as infrastructure, location, time, and vehicle type, among others, was used to predict severity, applying Association Rules to identify latent correlations and the Classification and Regression Tree for hierarchical risk classification. The results reveal that the type of collision is the primary predictor of severity; the highest severity is associated with heavy traffic and head-on or side-impact collisions, involving critical scenarios, in the early morning hours and in rural areas, linked to trucks. The combined use of both tools provides a scientific basis for designing interventions on highly vulnerable road segments, contributing to the fulfillment of the 2030 Agenda for safe mobility. Full article

Autonomous navigation in unstructured off-road environments presents fundamental challenges due to terrain heterogeneity, the absence of structured road markings, and the necessity for real-time traversability reasoning from raw sensory observations. We present TerrainFormer, a hierarchical framework that integrates a world model for terrain dynamics prediction with a temporal decision transformer for action selection. Our methodology employs a two-phase training paradigm: (1) self-supervised world model pretraining on LiDAR point clouds to learn terrain representations encompassing traversability, elevation, and semantic segmentation; (2) behavioral cloning of the decision transformer conditioned on frozen world model features with temporally derived goal directions. The world model processes raw 3D LiDAR point clouds through a PointPillars encoder for real-time bird’s-eye-view (BEV) projection, followed by a Vision Transformer backbone that produces latent terrain representations. A principal contribution is our cross-dataset generalization paradigm: the world model is trained on separate datasets while the decision transformer is trained on separate sequences, ensuring zero data overlap between training phases. We introduce automatic goal direction computation from vehicle pose trajectories, enabling the model to learn directionally conditioned navigation policies. To address the class imbalance inherent in off-road driving data, we employ focal loss with inverse-frequency class weighting and action-chunk supervision. Experimental evaluation on the RELLIS-3D dataset achieves 87.31% test accuracy with 0.7948 macro F1 across all 12 action classes. The world model’s predicted future frames produce only a 0.79% accuracy drop versus ground-truth observations, with 98.82% action agreement, demonstrating effective cross-dataset generalization for real-time off-road navigation. Full article

Road freight logistics is one of the most difficult transport segments to decarbonize. In recent years, battery electric trucks and hydrogen fuel cell electric trucks have emerged as the most promising alternatives to conventional heavy-duty vehicles. However, their integration cannot be reduced to a question of vehicle substitution, as it depends on a broader system of conditions. This paper aims to identify and structure the system-determining parameters that influence the use of battery electric trucks and hydrogen fuel cell electric trucks in road freight logistics. To this end, the study applies a systematic literature review, yielding a final sample of 42 publications. The review shows that drive type suitability depends on parameters across four categories: economic, ecological, performance-related, and external. Accordingly, no single factor determines suitability; rather, outcomes emerge from the interaction of multiple conditions. The reviewed literature does not support a universally superior drive technology. Instead, the suitability of battery electric trucks and hydrogen fuel cell electric trucks depends on the specific configuration of the surrounding system. The paper thus provides a structured framework for future comparative assessments in sustainable road freight logistics. The study is embedded in the Research Campus Mobility2Grid, which provides a practice-oriented context for assessing alternative drive technologies in relation to fleet, depot, energy, and logistics-system requirements. Full article