Pavement distress is one of the most serious and prevalent diseases in pavement road detection. However, traditional methods for crack detection often suffer from low efficiency and limited accuracy, necessitating improvements in the accuracy of existing crack detection algorithms. Consequently, we propose the shuffle attention for you only look once version eight (SA‐YOLOv8) model, which is based on an enhanced framework. Initially, we establish the required dataset and classify images proportionally based on their states. Subsequently, we conduct comparative testing against the results of the original model, analyzing issues such as the oversight of shallow and small cracks, truncation in the recognition of single‐instance long cracks, and imprecise detection. We devise an improved detection approach based on YOLOv8. This method incorporates a small target detection layer to optimize the receptive field range, aiming to focus on identifying shallow and small cracks. Simultaneously, the Shuffle Attention mechanism and the transplanted spatial pyramid pooling‐fast (SPP‐F) reuse structure are introduced in the feature extraction network to enhance the model’s attention to detection targets. This augmentation improves the fusion of features for shallow small targets and overall and partial features of long cracks, thereby alleviating the precision of the model in crack detection. The experimental results demonstrate a stepwise improvement in the model’s mean average precision (mAP) with each enhancement to the original network. Initially, adding a small object detection layer increased the mAP by 3.4 percentage points, raising it to 68.2%. Subsequently, incorporating the spatial attention (SA) module resulted in a more substantial improvement, boosting the mAP by 8.7 percentage points to 73.5%. Finally, the addition of the transplanted SPP‐F module further enhanced accuracy, increasing the mAP by 0.7 percentage points from the previous stage, thus achieving a final mAP of 74.2%. Overall, these modifications resulted in a total improvement of 9.4 percentage points in mAP compared to the original model. In conclusion, the proposed SA‐YOLOv8s model effectively supports the automated recognition of asphalt road surface cracks, demonstrating applicability in practical scenarios. The recognition performance is notably favorable, demonstrating robustness in complex environments.

This study predicts expressway asphalt pavement maintenance decisions using machine learning to overcome the information loss inherent in traditional composite indices like PQI and PCI. Using ten years of inspection data from the G3012 Expressway in Xinjiang, an interpretable Random Forest (RF) model was developed. The methodology integrates permutation-based feature selection, three imbalance mitigation strategies (Balanced Weighting, SMOTE, and Cost-Sensitive Learning), and a rigorous time-aware validation framework. Results indicate that raw distress features—specifically strip repairs, block cracking, transverse and longitudinal cracking—are the most influential predictors, significantly outperforming aggregated indices. The optimized model, using Balanced Weighting and mean imputation, achieved an accuracy of 0.826 and ROC-AUC of 0.853 under strict temporal validation, effectively identifying the minority “repair” class. This research demonstrates that leveraging raw distress data through an interpretable ensemble framework provides a robust, data-driven alternative to threshold-based planning, supporting the transition from reactive to preventive maintenance in complex infrastructure management.

Accurate characterization of the true tire–pavement contact state is essential for understanding pavement friction; yet conventional texture indicators and nominal contact assumptions cannot directly represent the actual interfacial interaction between rubber and pavement. This study proposes a rapid and non-destructive method for measuring three-dimensional tire–pavement true contact texture under different loads. A materials testing system was used to apply controlled loads to a rubber pad–carbon paper–pavement assembly, and the resulting imprints were combined with three-dimensional laser profilometer data and support-curve-based slicing to determine the real contact area ratio, penetration texture depth, and self-affine fractal dimension. Tests on nine asphalt pavement samples under loads from 5 to 20 kN showed that the real contact area ratio increased with load but remained below 40% at 20 kN. The predicted contact area from the reconstructed 3D texture agreed well with the imprint-based results, with an absolute error not exceeding 2.59%. Penetration texture depth showed a stronger relationship with skid resistance than fractal dimension. The proposed method provides a practical means of capturing effective tire–pavement contact parameters and offers useful inputs for laboratory-based skid resistance evaluation and texture-informed friction modeling.

Multi-target path planning for Automated Guided Vehicle (AGV) in complex logistics environments requires balancing planning efficiency, obstacle avoidance capability, and trajectory smoothness. To address these challenges, this paper proposes a bi-level collaborative framework integrating Particle Swarm Optimization (PSO) with the Bidirectional Rapidly Exploring Random Tree (Bi-RRT). The framework unifies adaptive sampling, online parameter optimization, and trajectory smoothing within a single planning architecture. Specifically, the framework constructs a five-dimensional particle encoding that includes the expansion step size and multi-level strategy switching thresholds. During the Bi-RRT expansion process, an expansion-failure-driven adaptive sampling mechanism is introduced to enhance search performance in cluttered environments, while local-density-based suppression and directional dispersion are employed to reduce redundant exploration. In addition, a lightweight PSO-based monitoring mechanism enables online adaptive parameter adjustment. For multi-target scheduling, a greedy heuristic based on a hybrid weighted graph determines the visitation sequence. Trajectory smoothness is further improved using cubic B-spline interpolation combined with bounded perturbation optimization. Experimental results demonstrate that the proposed framework improves planning efficiency while maintaining stable performance across environments with different obstacle densities. These results demonstrate the effectiveness of the proposed framework for multi-target AGV path planning in complex warehouse environments.

Continuous eye tracking is difficult to deploy in production vehicles, while eye-movement-derived proxy labels are susceptible to same-source circularity and road-type confounding. We propose a driver-independent, confounder-aware cross-source framework in which the eye-movement-derived visual load index (VLI) proxy is constructed within training folds, whereas held-out prediction uses only vehicle kinematics and an optional driving-style prior. Evaluation used naturalistic data from 33 drivers, comprising 24,437 windows along a 48.4 km route, under driver-grouped cross-validation with driver-level bootstrap confidence intervals and control and increment tests. The vehicle-only model achieved an AUC of 0.589 across held-out drivers, rising to 0.633 with the style prior. After controlling for road type, vehicle features improved AUC by 0.042 (95% CI: 0.006–0.078), indicating measurable information beyond the binary road-type proxy; the style increment remained exploratory because its confidence interval included zero. Future-state prediction converged to a persistence baseline at 20 s. The VLI was not externally validated against an independent subjective or physiological criterion; accordingly, the findings are limited to cross-source prediction of an eye-movement-derived proxy. Across five architectures, this cross-source pattern was directionally consistent, supporting prospective evaluation of vehicle kinematics as a complementary sensing channel in multisource driver-monitoring systems.

Pavement intersecting cracks expand outward under load, especially at intersections where stress leads to branching and a complex network. This study introduces Crack‐DL, a deep learning framework for crack segmentation and feature extraction. We propose the YOLO‐Segcrack model, which integrates the advanced FasterNet backbone with the SENet attention module. This combination leverages the computational efficiency of FasterNet for robust feature extraction and the discriminative ability of SENet to emphasize critical crack areas, and the model achieves significantly improved segmentation performance and precisely extracts pavement intersecting cracks. Additionally, a convolution kernel matching algorithm (CKMA) is developed based on morphological image processing for precise intersection point localization and to quantify crack lengths and intersection angles. Finally, the CrackX dataset containing pavement intersecting cracks is constructed to support this research. The proposed Crack‐DL framework was tested on CrackX and public datasets, CrackTree260, demonstrating its accuracy and reliability. Experimental results show that using the YOLO‐Segcrack model increases detection and segmentation precision by 11.1% and 4.8%, respectively. In addition, extensive experimental results on crack‐seg, package‐seg, and carparts‐seg datasets further show that the improved YOLOv8s‐seg model outperforms existing advanced methods in terms of performance. When applying the CKMA for detecting intersection points, the detection accuracy reached 73.19%. For the publicly available CrackTree260 dataset, the accuracy reached 91.5%. Furthermore, when the error is under 5 unit pixels (mm), the accuracy for calculating total crack length is 92.46% for ground truth images and 80.82% for the adaptively segmented binary images. These results demonstrate that the proposed model enhances the extraction of intersecting cracks area and the CKMA provides a reference value for the analysis of cracks propagation. The dataset and source code are available at https://github.com/Keeeram/Intersecting-Crack-Analysis .