Search Results (194)

Driving behavior recognition plays a crucial role in intelligent driving systems and road traffic safety. Due to insufficient driving experience and limited ability to allocate visual attention, novice drivers are considered a high-risk group for traffic accidents. Existing approaches primarily focus on experienced drivers and rely on single-modal eye-tracking data, making it difficult to model spatial attention distributions and long-term temporal dependencies simultaneously. Moreover, these methods are often affected by modality asynchrony during multimodal fusion, further limiting performance gains. To address these challenges, this study proposes a novice driver behavior recognition method based on bimodal eye-tracking features and a gated cross-modal attention fusion (GCMAF) mechanism. The model adopts a spatial–temporal dual-branch architecture. The spatial branch employs ResNet34 to extract eye-tracking heatmap features to represent the visual attention distribution. In contrast, the temporal branch integrates a 1D-CNN with the Mamba model to capture local dynamic patterns and long-range temporal dependencies. In the fusion stage, the GCMAF module is introduced to enhance cross-modal interactions, and a gating mechanism is further used to adaptively adjust modality weights, thereby mitigating the adverse effects of modality asynchrony. To validate the effectiveness and generalization ability of the proposed method, repeated experiments and five-fold cross-validation are conducted. The results demonstrate that the model achieves an average classification accuracy of 93.86% across four driving behavior categories, with standard deviations below 0.3%. Compared with baseline methods, paired t-test results show that the performance improvement is statistically significant (p < 0.01). Ablation studies further confirm the independent contribution of each component. Overall, the proposed method outperforms existing approaches in terms of accuracy and stability, providing effective support for driving behavior assessment and proactive safety warning systems. Full article

Extracting rural road networks from remote sensing images is crucial for data-driven precision agriculture planning. However, traditional semantic segmentation methods often struggle to achieve both high-precision boundary delineation and topological integrity, especially in heterogeneous rural landscapes. To address these issues, this study proposes MACER-UNet, a novel connectivity-aware road extraction model that integrates multi-scale perception and edge enhancement capabilities. Specifically, MACER-UNet employs ResNet-50 as the backbone network to extract robust deep semantic features. Within the encoder–decoder framework, an atrous spatial pyramid pooling module (ASPP) is embedded to capture rich multi-scale context cues, thereby enhancing robustness to varying road widths and inconsistent imaging conditions. During the decoding process, the convolutional block attention module (CBAM) recalibrates features to reduce noise from the agricultural background. The edge enhancement module (EEM) extracts high-frequency gradient cues for geometric correction and boundary sharpening. This architecture combines spatial attention and edge constraints to balance recognition accuracy and topological connectivity. On the public WHU-CR dataset, MACER-UNet achieved an intersection over union (IoU) of 50.37% and an F1 score of 67.02%, outperforming U-Net (44.27%), DeepLabv3+ (49.43%), and D-LinkNet (49.54%), and its connectivity was comparable to recent state-of-the-art road extraction methods such as C²Net (49.37%) and CGCNet (50.34%). On a self-built dataset with a 3 m resolution in Suihua, the model achieved an IoU of 42.56% and an F1 score of 59.71%. The evaluation results confirm that MACER-UNet provides a road network with geometric consistency and topological integrity for spatial analysis in rural environments. Full article

The rapid and accurate extraction of landslide areas from remote sensing imagery is critical for post-disaster emergency response and rescue operations. However, landslides exhibit complex morphologies and irregular orientations and are easily confused with background features such as bare ground and roads. However, accurately modeling long-range spatial dependencies, effective multi-scale feature fusion, and precise boundary delineation for complex landslide scenarios remains challenging. To address these challenges, we propose NestedMambaUNet, a nested state-space network specifically designed for landslide extraction in remote sensing imagery. Building upon the dense skip-connection architecture of UNet++, the model first introduces a coordinate-enhanced convolution module called CoordConvBlock to explicitly encode spatial positional information during shallow feature extraction, thereby improving the modeling of spatial relationships between landslides and surrounding terrain. It further incorporates a 2D direction-adaptive selective scanning mechanism (DASS2D), which adaptively aggregates scanning results from four directions (horizontal, vertical, main diagonal, and anti-diagonal) to capture long-range spatial dependencies aligned with the irregular structures of landslides. Additionally, a direction-adaptive selective scanning fusion block (DASS Fusion Block) is designed to enhance multi-scale feature integration and improve boundary continuity by combining attention-based gating for skip connections with direction-adaptive state-space modeling. The experimental results on two public datasets, LMHLD and HR-GLDD, demonstrate that the proposed method outperforms competing approaches across multiple evaluation metrics. Specifically, the IoU reaches 71.23% and 58.84%, representing improvements of 1.17 and 5.65 percentage points over the second-best method, respectively, while the recall increases by 4.05 and 12.01 percentage points, respectively. Although the proposed method exhibits a slight reduction in Precision on certain datasets, it achieves the best overall F1-score, indicating a favorable balance between missed detection reduction and false positive control for landslide extraction tasks. These results indicate that NestedMambaUNet effectively improves the structural integrity of landslides and enhances boundary delineation, while exhibiting good robustness across different data distributions and geographic scenarios. In addition, the proposed method achieves a favorable balance between segmentation accuracy and computational efficiency, demonstrating its potential for time-sensitive large-scale landslide mapping applications. Full article

Managing surface subsidence caused by underground mining beneath critical infrastructure requires highly efficient and robust optimization models. This study presents a data-driven, multi-objective framework for the collaborative safety optimization of mine stopes and overlying roads, applied to a quartzite mine beneath a secondary highway in Yunnan Province. Based on 88 FLAC3D simulation samples, an XGBoost surrogate model was developed to predict geomechanical responses (Z-displacement and extraction volume), while a Bayesian-optimized Long Short-Term Memory (BO-LSTM) network was employed to forecast ore price signals. To ensure model generalizability and mitigate overfitting in the limited stope dataset, a 5-fold cross-validation (5-fold CV protocol was systematically incorporated into the model training process. Critically, the CV-supported predictive models underwent stress testing under varying training data sizes (40–90%), Gaussian noise intensities (1–13%), and outlier distributions (5–20% proportion; 10–50%amplitude) to define the boundaries of algorithmic reliability. The NSGA-II algorithm was used to map the Pareto-optimal frontier, which was deployed via a Tkinter graphical user interface for dynamic stope geometry adjustments driven by price fluctuations. Secondary FLAC3D validation of the recommended parameters (21.44 m pillar, 1.60 m sidewall) yielded minimal relative errors of 5.07% for displacement and 2.38% for extraction volume. This validated framework demonstrates numerical robustness while balancing geotechnical safety and dynamic market rewards. Full article

Road traffic noise in urban streets is shaped not only by traffic sources but also by sound propagation through the surrounding street geometry. Existing prediction methods are still largely source-oriented, and receptor-specific models that rely on street morphology alone remain uncommon. We developed and compared interpretable machine-learning models to predict a cyclist-side sound pressure level (SPL) under fixed source conditions, using 12 spatial parameters extracted from 5060 street sections on 195 streets in Harbin, China. Acoustic simulations were performed in ODEON under fixed source-power conditions, and four models—Linear Regression, support vector regression (SVR), extreme gradient boosting (XGBoost), and Random Forest (RF)—were evaluated through an illustrative 80/20 split, 20 repeated random 80/20 splits, and 20 road-name-based grouped holdout repetitions. The nonlinear models consistently outperformed the linear baseline. Under grouped holdout validation, XGBoost achieved the highest predictive accuracy (R² = 0.953 ± 0.018, RMSE = 0.583 ± 0.119 dB, MAE = 0.418 ± 0.082 dB). RF reached comparable accuracy (R² = 0.938 ± 0.041, RMSE = 0.662 ± 0.210 dB, MAE = 0.453 ± 0.128 dB) and was retained for the interpretation of feature importance and marginal response patterns. A computation-time comparison based on 93 representative ODEON simulations showed that ODEON required a median of 2 min 33 s per street section, whereas the trained models predicted all 5060 sections in 0.013 s with XGBoost and 0.143 s with RF. The RF-based interpretation identified vehicle-lane width, sidewalk width, and near-zone cross-sectional enclosure degree as the most influential variables. Width-related parameters dominated cyclist-side SPL prediction, while enclosure-related parameters became more relevant mainly under narrower width conditions. The framework is therefore intended as a comparative morphology-screening tool under fixed source conditions, not as a predictor of real-world traffic noise under varying traffic states. Full article

Autonomous ground vehicle navigation in dynamic real-world environments demands path planning systems that simultaneously accommodate real-time environmental hazards and diverse user-defined objectives requirements that classical algorithms, with their static, single-objective cost functions, cannot fulfil. This paper presents the Semantic Personalised Path Planning (SPPP) system, centred on a novel Semantic Personalised Cost (SPC) algorithm that augments the A* search framework with a dynamically computed personalised cost term. The SPC function integrates eight real-time semantic obstacle categories including traffic congestion, weather severity, road surface conditions, and construction activity with eight user-defined preference dimensions spanning safety, travel time, emergency response, comfort, and battery efficiency. An adaptive scaling mechanism amplifies obstacle penalties near the goal, and a gradient-based weight evolution rule refines preference weights iteratively over successive route segments. The user-defined preference activation directly personalises the routing objective to individual operational needs, with the gradient-based evolution further refining preference alignment over successive route segments. Experiments were conducted in two phases: 500 randomised obstacle configurations on a controlled $8\times 8$ grid, and a real 847-node road graph extracted from OpenStreetMap around SRM Institute of Science and Technology, Kattankulathur, representing a single 1.4 km urban corridor, with obstacle scores derived from live Mapbox Traffic and OpenWeatherMap application programming interface data. Under the full emergency preference scenario, SPPP achieves 94.3% obstacle avoidance versus 31.7% for the Euclidean distance threshold A* baseline, a difference statistically significant at $p < 0.001$ under the Wilcoxon signed-rank test with Cohen’s d ≈ 18.9. Real-world computation time of 1.91 ms on a standard laptop and 3.76 ms on a Raspberry Pi 4 confirms deployability on embedded autonomous vehicle hardware. Full article

This paper examines the effect of gas oil fraction extraction depth from fuel oil on the physicochemical and performance properties of road bitumen. The study’s novelty lies in establishing the relationship between the seven-component chemical group composition of heavy residues and their oxidation kinetics. It has been experimentally demonstrated that using feedstock with a nominal viscosity (VU80) in the range of 20–80 s (corresponding to fractions of 480–525 °C) enables the production of bitumen that simultaneously meets the requirements of ASTM D946, EN 12591, and ST RK 1373. The paper substantiates an optimal “viscosity range” for processing non-standard feedstock, ensuring increased resistance of the finished product to thermal-oxidative aging. Full article

The effective management of municipal road infrastructure requires up-to-date, standardized and reliable condition information to support sustainable maintenance. While visual road-condition assessment methods based on established standards are widely applied to municipal roads, they remain largely manual, time-consuming, costly and subjective. This study presents an end-to-end workflow for the automated visual inspection and condition assessment of municipal road infrastructure using high-resolution, 3D street-level imagery acquired by professional mobile mapping systems. The proposed approach integrates an efficient preprocessing pipeline for precise road-surface extraction with deep learning models trained for the specific task and an advanced postprocessing method for robust results aggregation. For this purpose, a large dataset covering approximately 352 km of municipal roads across eight municipalities was created by combining street-level imagery with expert-annotated road-condition index (RCI) values. Two neural network variants were implemented: a regression model predicting standardized RCI values and a binary classifier distinguishing between roads requiring maintenance and those in good condition. To ensure decision-oriented outputs at the infrastructure-asset level, frame-based predictions are aggregated into homogeneous road segments using outlier detection and change-point analysis along the road axis. The regression model achieved a mean absolute error of 0.48 RCI values at frame level and 0.40 RCI values at road-segment level, outperforming conventional inter-expert variability, while the binary classification model reached an F1-score of 0.85. These findings demonstrate that AI-based visual road-condition assessment using professional mobile mapping data can provide accurate, standardized and scalable condition information for municipal road infrastructure. The proposed workflow supports maintenance prioritization and infrastructure management decisions without requiring explicit detection of individual pavement defects, offering a practical pathway toward automated, cost-effective road-condition monitoring. Full article

Urban business-to-business distribution in Casablanca relies heavily on light commercial vehicles (LCVs) operating in a constrained street environment where loading/unloading access, intersection capacity, and recurring bottlenecks jointly shape performance and environmental impacts. However, high-resolution freight origin–destination (OD) observations and junction calibration data are limited, which complicates direct estimations of congestion and externalities attributable to commercial activity. This study develops a reproducible, large-scale modeling workflow that couples tour-based freight demand generation in order units with simulation-based traffic assignment (SBA) on a metropolitan network and translates network performance into emissions and monetary losses. Warehouses are modeled as primary producers and commercial activity zones as attractors via sector-tagged production and attraction functions; the resulting order distribution is converted to OD vehicle trips using the tour-based trip generation procedure with the mean targets-per-tour fixed to one to ensure numerical stability, yielding a direct-shipment approximation appropriate for stress–response analysis. Junction impedance is represented through turn-type volume–delay relationships and node-level impedance procedures, and congestion is evaluated using vehicle kilometers traveled/vehicle hours traveled (VKT/VHT)-based indicators, delay-intensity measures, and link/node bottleneck rankings. Across demand-scaling scenarios, VKT increases from 302,159 to 1,017,686 veh·km/day, while network delay rises nonlinearly from 392.5 to 2738.4 veh·h/day, indicating saturation-driven amplification of time losses. The Handbook of Emission Factors for Road Transport (HBEFA)-compatible emission estimates scale with activity: total carbon dioxide (CO₂) increases from 154.1 to 519.5 t/day, and nitrogen oxides (NOx) and particulate matter (PM_2.5) totals rise proportionally under fixed fleet assumptions. Monetizing delay with a purchasing-power-adjusted value-of-time range yields a congestion cost per trip that increases from approximately 0.20 to 0.41 Moroccan dirham, MAD/trip (at 60 MAD/veh·h), consistent with rising delay intensity. Bottleneck extraction shows welfare losses to be structurally concentrated on a small persistent corridor set, led by ‘Boulevard de la Résistance’, with recurrent hotspots including ‘Rue d’Arcachon’ and ‘Rue d’Ifni’. The framework supports policy-relevant reporting of congestion, emissions, and welfare impacts under data scarcity, with explicit sensitivity bounds. Full article

To investigate the dynamic changes in driver attention in complex road traffic scenarios, this paper proposes a driver attention prediction method based on cross-modal adaptive feature fusion (DAFNet). First, semantic segmentation is applied to the input image sequences, and a dual-branch encoder using a 3D residual network is designed to extract spatio-temporal features from both RGB images and semantic information in parallel. Next, a 3D deformable attention mechanism is introduced to enhance the traditional Transformer algorithm, which focuses on the key salient regions through spatio-temporal offset prediction and adaptive fusion of cross-modal features. Subsequently, a predictive recurrent neural network is employed to forecast the fused spatio-temporal features and improve the stability of long-term sequence prediction. Finally, the driver attention results are predicted by a lightweight decoder. Experimental results demonstrate that the proposed method outperforms the comparative methods in overall performance. The predictions not only capture salient regions in driving scenes in a bottom-up manner but also track the driver’s intent in a top-down manner. Thus, our method exhibits strong adaptability to various complex traffic scenarios. Additionally, the method achieves an inference speed of 53.73 frames per second, satisfying the real-time performance requirement of on-vehicle systems. Full article

Signal intersections are key nodes in urban road traffic networks, and real-time queue length information serves as a core performance indicator for formulating effective signal management schemes in modern adaptive traffic signal control systems, thereby enhancing traffic efficiency. In this study, a roadside Light Detection and Ranging (LiDAR) sensor is employed to acquire 3D point cloud data of vehicles in the road space, which acts as an important method for queue length detection. However, during queue-length detection, vehicles in different lanes are prone to occlusion because of the straight-line propagation of laser beams. This paper proposes a queue-length detection method based on variations in vehicle point cloud features to address the occlusion of queue-end vehicles during detection. This method first preprocesses LiDAR point cloud data (including region-of-interest extraction, ground-point filtering, point cloud clustering, object association, and lane recognition) to detect real-time queue lengths across multiple lanes. Subsequently, the occlusion problem is categorized into complete occulusion and partial occlusion, and corresponding processing is performed to correct the detection results. The performance of the proposed queue length detection method was validated through experiments that collected real-world data from three urban road intersections in Suzhou. The results indicate that this method’s average accuracy can reach 99.3%. Furthermore, the effectiveness of the proposed occlusion handling method has been validated through experiments. Full article

The predictive accuracy of the Bekker–Wong model for wheel traction is highly dependent on the precision of the wheel–soil contact angle parameters. These parameters are typically identified through extensive and costly single wheel–soil tests, which are limited by poor experimental repeatability and site-specific constraints. This study proposes a method for obtaining contact angle parameters through numerical simulation. Firstly, a finite element model of an off-road tire is established. The Drucker–Prager (D-P) constitutive model parameters of clay under different moisture were calibrated by soil mechanical tests. And then the moist clay was modeled through the SPH algorithm. An FEM–SPH interaction model was developed to define the tire–moist clay interaction. Meanwhile, the tire–moist clay interaction model was verified by a single wheel–soil test device. To identify the empirical parameters of tire–soil interaction, numerical simulations were conducted for multiple operating conditions involving different slip ratios, soil moisture contents, and vertical loads. By processing the simulated wheel–soil contact characteristic images, the contact angles for each condition were extracted. Finally, the contact angle parameters in the Bekker–Wong model were identified. The empirical parameters were integrated into the Bekker–Wong model to predict traction. The results indicate that the maximum relative error of traction force between the prediction and experiment did not exceed 13.6%, which validated the reliability of the proposed method. Full article

Point cloud semantic segmentation is a key technology for applications such as autonomous driving, robotics, and virtual reality. Current approaches are heavily reliant on local relative coordinates and simplistic attention mechanisms to aggregate neighborhood information. This often leads to an ineffective joint representation of geometric perturbations and feature variations, coupled with a lack of adaptive selection for salient features during context fusion. On this basis, we propose LSSCC-Net, a novel segmentation framework based on LACV-Net. First, the spatial-feature dynamic aggregation module is designed to fuse offset information by symmetric interaction between spatial positions and feature channels, thus supplementing local structural information. Second, a dual-dimensional attention mechanism (spatial and channel) is introduced to symmetrically deploy attention modules in both the encoder and decoder, prioritizing salient information extraction. Finally, Lovász-Softmax Loss is used as an auxiliary loss to optimize the training objective. The proposed method is evaluated on two public benchmark datasets. The mIoU on the Toronto3D and S3DIS datasets is 83.6% and 65.2%, respectively. Compared with the baseline LACV-Net, LSSCC-Net showed notable improvements in challenging categories: the IoU for “road mark” and “fence” on Toronto3D increased by 3.6% and 8.1%, respectively. These results indicate that LSSCC-Net more accurately characterizes complex boundaries and fine-grained structures, enhancing segmentation capabilities for small-scale targets and category boundaries. Full article

Point cloud data, with its rich information and high-precision geometric details, holds significant value for urban road infrastructure surveying and management. To overcome the limitations of manual classification, this study employs deep learning techniques for automated point cloud feature extraction and classification, achieving high-precision object recognition in road scenes. By integrating the Princeton ModelNet40, ShapeNet, and Sydney Urban Objects datasets, we extracted 3D spatial coordinates from the Sydney Urban Objects Dataset and organized labeled point cloud files to build a comprehensive dataset reflecting real-world road scenarios. To address noise and occlusion-induced data gaps, three augmentation strategies were implemented: (1) Farthest Point Sampling (FPS): Preserves critical features while mitigating overfitting. (2) Random Z-axis rotation, translation, and scaling: Enhances model generalization. (3) Gaussian noise injection: Improves training sample realism. The PointNet++ framework was enhanced by integrating a point-filling method into the preprocessing module. Model training and prediction were conducted using its Multi-Scale Grouping (MSG) and Single-Scale Grouping (SSG) schemes. The model achieved an average training accuracy of 86.26% (peak single-instance accuracy: 98.54%; best category accuracy: 93.15%) and a test set accuracy of 97.41% (category accuracy: 84.50%). This study demonstrates successful road scene point cloud classification, providing valuable insights for point cloud data processing and related research. Full article

Effective governance of informal commercial spaces is a common challenge faced by cities globally. To break through the superficial governance mindset of traditional spatial regulation, this study focuses on clarifying the spatial distribution characteristics and influencing factors of such spaces. By integrating the theory of “The right to the city” with the “7D” principles of New Urbanism, and focusing on the Jinxiang Street area in Harbin, a representative zone combining traditional industrial and modern residential communities, this study constructed a multidimensional indicator framework including population factors, functional diversity of facilities, accessibility of the built environment, spatial suitability, and intensity of community management, extracting 17 significant variables. Through spatial autocorrelation analysis (Moran’s I), multiscale geographically weighted regression (MGWR), and geographic detector analysis, the results show that informal commercial spaces exhibit clustered yet uneven characteristics between aging and upscale communities; the MGWR model reveals significant spatial heterogeneity in influencing factors; and geographic detector analysis shows that the interaction between public service facilities’ proximity to main roads and enhanced community management has the most significant explanatory power for heterogeneity (q = 0.85). These findings inform differentiated governance strategies and provide scientific support for sustainable governance of informal commercial spaces. Full article