A comprehensive bibliometric analysis was conducted to evaluate the evolution, thematic structure, and emerging trends in autonomous vehicle (AV) research. Scientific literature published up to 3 January 2025 was retrieved from the Web of Science (WoS), resulting in a corpus of 11,069 publications spanning 60 countries. Using VOSviewer software, a detailed examination was performed to map the intellectual structure of the field, including co-authorship patterns, citation networks, keyword co-occurrence, and institutional contributions. The findings revealed a marked increase in the volume of AV-related publications over time, indicating growing scholarly interest and investment in the domain. A total of 157 distinct scientific disciplines were identified, underscoring the inherently multidisciplinary nature of AV research, which encompasses fields such as computer science, robotics, transportation engineering, artificial intelligence, and socio-economic policy. The most prolific countries, institutions, and authors were visualised through citation and collaboration networks, revealing key contributors and international linkages. Particular emphasis was placed on the use of reinforcement learning and other machine learning methodologies in AV development, as reflected by keyword trends and thematic clustering. Additionally, attention was given to the broader socio-economic and managerial dimensions of AV adoption, including market dynamics, regulatory frameworks, and public acceptance. This analysis provides a rigorous and systematic overview of the current state of AV research and highlights potential avenues for future exploration. By synthesising large-scale bibliometric data, this study offers valuable insights for academics, policymakers, and industry stakeholders engaged in the evolving landscape of autonomous transportation systems.

Accurate detection of road surface potholes remains a persistent challenge due to environmental variability, inconsistent illumination, noise interference, and the complexity of road textures. Conventional detection methods frequently suffer from reduced performance when exposed to low-quality or noisy imagery, resulting in unreliable or delayed identification. To address these limitations, a robust and optimized image processing framework has been developed for real-time pothole detection under uncertain environmental conditions. The proposed approach employs a combination of advanced contrast enhancement techniques and adaptive convolutional processing to strengthen feature discrimination across heterogeneous road surfaces. To further improve detection reliability, a self-adaptive fuzzy refinement mechanism has been introduced, effectively delineating ambiguous or degraded regions often overlooked by deterministic methods. An energy-based functional is applied to model spatial and intensity gradients, enabling more precise localization of structural discontinuities indicative of pothole boundaries. The framework also incorporates computational optimization strategies to enhance processing speed without compromising accuracy, rendering it suitable for deployment in real-time autonomous or semi-autonomous road inspection systems. Thresholding and mask extraction operations have been systematically integrated to achieve accurate segmentation of pothole regions, even in the presence of substantial visual noise or occlusions. Experimental validations on benchmark datasets and real-world road imagery have demonstrated that the proposed method consistently outperforms existing state-of-the-art techniques with regard to detection accuracy, robustness to environmental disturbances, and computational efficiency. This approach presents a scalable and practical solution for intelligent transportation systems and automated infrastructure monitoring, contributing to improved road safety, timely maintenance, and cost-effective asset management.

Accurate detection of road surface anomalies remains a fundamental challenge in ensuring vehicular safety, particularly within the domain of intelligent transportation systems and autonomous driving technologies. Among such anomalies, crash stones—defined as irregular, protruding, and often unstructured fragments on the road—pose considerable risks due to their heterogeneous morphologies and unpredictable spatial distributions. In this study, a novel mathematical model is proposed, formulated through a functional energy minimization framework tailored specifically for the detection and segmentation of crash stones. The model incorporates three principal components: geometric edge energy to emphasize structural discontinuities, local variance descriptors to capture micro-textural heterogeneity, and fuzzy texture irregularity measures designed to quantify non-uniform surface patterns. These components are integrated into a unified total energy functional, which, when minimized, facilitates the precise localization of obstacle regions under diverse illumination, weather, and pavement conditions. Final detection is achieved through adaptive thresholding informed by fuzzy logic-based classification, enabling robust performance in scenarios with high noise or low contrast. Unlike deep learning-based methods, the proposed approach is fully interpretable, non-reliant on extensive annotated datasets, and computationally efficient, making it well-suited for real-time applications in resource-constrained environments. Experimental validations demonstrate high detection accuracy across varied real-world datasets, substantiating the model's generalizability and resilience. The framework contributes a mathematically rigorous, scalable, and explainable solution to the enduring problem of small obstacle detection, with direct implications for the enhancement of road safety in next-generation transportation systems.

Accurate identification of concrete surfaces on roadways is critical for the advancement of autonomous navigation systems and the effective monitoring of transportation infrastructure. Nevertheless, the inherently heterogeneous texture of concrete, in conjunction with environmental variables such as lighting fluctuations and surface degradation, continues to impede precise surface segmentation. To address these challenges, a novel framework has been developed that integrates Fuzzy Topological Entropy (FTE) with Multiscale Laplacian Structural Dissimilarity (MLSD) for the robust delineation of concrete regions in road imagery. Within this framework, FTE is employed to model uncertainty and spatial ambiguity through a continuous fuzzy membership function, thereby capturing the nuanced transitions between concrete and non-concrete domains. Concurrently, MLSD is utilised to quantify multiscale structural irregularities by leveraging Laplacian-based texture dissimilarity, enhancing sensitivity to surface roughness and material inconsistencies. These complementary components are embedded within a unified energy functional, the minimisation of which is conducted via an iterative optimisation strategy that avoids the need for extensive training datasets or prior scene annotations. The proposed methodology demonstrates strong resilience across a variety of environmental conditions, including shadows, glare, occlusions, and physical wear. Superior performance is observed particularly in complex or degraded urban settings, where conventional segmentation models often fail. Owing to its non-parametric nature and computational efficiency, the approach is well-suited for real-time deployment in autonomous vehicle systems, smart city infrastructure, and road condition assessment platforms. By facilitating reliable and scalable surface segmentation without reliance on deep learning architectures or exhaustive manual labelling, this work offers a significant advancement toward generalisable and interpretable road surface analysis technologies.

The efficient classification of transport vehicles is critical to the optimization of modern transportation systems, yet significant challenges persist, particularly in distinguishing Heavy Transport Vehicles (HTVs) from Light Transport Vehicles (LTVs). These challenges arise due to considerable variations in vehicle size, shape, orientation, and external factors such as camera perspective, lighting conditions, and occlusions. In this study, a novel classification framework is proposed, integrating geometric feature extraction with a soft computing approach based on fuzzy logic. Key geometric attributes, including bounding box length, width, area, and aspect ratio, are extracted through image processing techniques. Initial classification is performed via threshold-based rules to eliminate non-HTV instances using predefined feature thresholds. To address uncertainties inherent in real-world surveillance conditions, fuzzy logic inference is subsequently applied, enabling flexible and robust decision-making in the presence of imprecise or noisy data. This hybrid methodology, combining deterministic thresholding and soft computing principles, enhances classification reliability and adaptability under diverse environmental and operational conditions. Extensive real-world experiments have been conducted to validate the proposed framework, demonstrating superior performance in terms of accuracy, robustness, and computational efficiency when compared with conventional classification methods. The results underscore the potential of the framework for deployment in intelligent traffic monitoring systems where precise vehicle categorization is essential for traffic management, infrastructure planning, and safety enforcement.

Automated detection of vehicle dents remains a challenging task due to variability in lighting conditions, surface textures, and the presence of minor deformations that may mimic actual dents. This paper presents a novel hybrid framework that integrates color deviation analysis, fuzzy classification, and the Structural Similarity Index (SSI) to enhance detection robustness and accuracy. The proposed model employs an adaptive bounding box generation technique, optimized via morphological operations, for precise dent localization. A newly introduced Color Difference Metric (CDM), computed in the Hue, Saturation, and Value (HSV) color space, quantifies subtle color deviations induced by dents, improving the system’s sensitivity to minor deformations. Furthermore, a hybrid classification mechanism—merging step-function classification with fuzzy membership functions—ensures smoother transitions between dent severity levels, mitigating the risks of hard thresholding. SSI serves as a structural integrity validator, helping to differentiate true dents from surface irregularities and lighting artifacts. A Dent Confidence Score is computed as a weighted aggregation of the step-function output, fuzzy confidence levels, and SSI response, effectively balancing sensitivity and specificity. Dents are categorized into three interpretable classes: No Dent, Possible Dent, and Confirmed Dent. Evaluation on real-world datasets—encompassing diverse lighting conditions, vehicle colors, and camera angles—demonstrates the model’s superior performance. Compared to traditional approaches, our method significantly improves key metrics such as Intersection over Union (IoU), Dice Coefficient, Precision, Recall, and F1-Score, underscoring its applicability in real-world automated dent detection systems.

The mitigation of road traffic accidents remains a critical global challenge, particularly in regions where cultural norms and behavioral risk factors significantly influence driving practices. This study employs a hybrid Multi-Criteria Decision-Making (MCDM) approach, integrating Grey Theory, the Full Consistency Method (FUCOM), and the Evaluation based on Distance from Average Solution (EDAS), to systematically assess four strategic interventions: Infrastructure Improvements, Educational Programs, Policy Amendments, and Technology Integration. These strategies are evaluated based on a set of criteria that encompass attitudes toward speeding, perceptions of traffic laws, the use of safety equipment, and the prevalence of high-risk driving behaviors. The findings indicate that while Infrastructure Improvements and Technology Integration enhance the physical and technological dimensions of road safety, Educational Programs and Policy Amendments play an indispensable role in shaping driver behavior and reinforcing compliance with traffic regulations. The necessity of a comprehensive and integrated strategy that leverages both technological advancements and behavioral interventions is underscored, ensuring a holistic and sustainable reduction in traffic-related fatalities and injuries. The outcomes of this study provide valuable insights for policymakers and road safety authorities, offering a structured framework for the prioritization and implementation of road safety measures tailored to the socio-cultural and behavioral dynamics of Libya.

Accurate traffic prediction is essential for optimizing urban mobility and mitigating congestion. Traditional deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), struggle to capture complex spatiotemporal dependencies and dynamic traffic variations across urban networks. To address these challenges, this study introduces DSTGN-ExpertNet, a novel Deep Spatio-Temporal Graph Neural Network (DSTGNN) framework that integrates Graph Neural Networks (GNNs) for spatial modeling and advanced deep learning techniques for temporal dynamics. The framework employs a Mixture of Experts (MoE) approach, where specialized expert models are dynamically assigned to distinct traffic patterns through a gating network, optimizing both prediction accuracy and interpretability. The proposed model is evaluated on large-scale real-world traffic datasets from Beijing and New York, demonstrating superior performance over conventional methods, including Spatio-Temporal Graph Convolutional Networks (ST-GCN) and attention-based models. With a mean absolute error (MAE) of 1.97 on the BikeNYC dataset and 9.70 on the TaxiBJ dataset, DSTGN-ExpertNet achieves state-of-the-art accuracy. These findings highlight the potential of GNN-based frameworks in revolutionizing traffic forecasting and intelligent transportation systems (ITS).

Foggy road conditions present significant challenges for road monitoring systems and autonomous driving, as conventional defogging techniques often fail to accurately recover fine details of road structures, particularly under dense fog conditions, and may introduce undesirable artifacts. Furthermore, these methods typically lack the ability to dynamically adjust transmission maps, leading to imprecise differentiation between foggy and clear areas. To address these limitations, a novel approach to image dehazing is proposed, which combines an entropy-weighted Gaussian Mixture Model (EW-GMM) with Pythagorean fuzzy aggregation (PFA) and a level set refinement technique. The method enhances the performance of existing models by adaptively adjusting the influence of each Gaussian component based on entropy, with greater emphasis placed on regions exhibiting higher uncertainty, thereby enabling more accurate restoration of foggy images. The EW-GMM is further refined using PFA, which integrates fuzzy membership functions with entropy-based weights to improve the distinction between foggy and clear regions. A level set method is subsequently applied to smooth the transmission map, reducing noise and preserving critical image details. This process is guided by an energy functional that accounts for spatial smoothness, entropy-weighted components, and observed pixel intensities, ensuring a more robust and accurate dehazing effect. Experimental results demonstrate that the proposed model outperforms conventional methods in terms of feature similarity, image quality, and cross-correlation, while significantly reducing execution time. The results highlight the efficiency and robustness of the proposed approach, making it a promising solution for real-time image processing applications, particularly in the context of road monitoring and autonomous driving systems.

The effective allocation of emergency supplies is crucial in the aftermath of flood disasters, as it directly impacts response times and mitigates casualties and property losses. Traditional methods of material distribution predominantly rely on ground-based transportation, which often proves inefficient and inflexible under the dynamic conditions of a disaster. This study explores the potential of unmanned aerial vehicles (UAVs) as a transformative solution to the challenges associated with emergency material dispatch. Factors influencing UAV scheduling, including environmental constraints, payload capacity, and flight dynamics, are analyzed in depth. Optimization measures for improving UAV collaborative operations are proposed, with a focus on enhancing the efficiency and adaptability of disaster response systems. The integration of reinforcement learning (RL) is examined as a theoretical framework for optimizing UAV collaborative scheduling, facilitating autonomous decision-making in real-time scenarios. An empirical analysis is presented based on the “7-20” rainstorm and flooding disaster in Zhengzhou, illustrating the practical application of collaborative UAVs in disaster relief. The results demonstrate the significant optimization potential of UAV technology, with a notable reduction in response times and improved logistical coordination. Furthermore, the role of UAVs in future disaster relief operations is discussed, with emphasis on the integration of blockchain and smart dispatch systems to enable decentralized, autonomous coordination. These advancements are expected to enhance the overall efficiency of emergency material distribution and better address the complex challenges posed by post-disaster environments. The findings underscore the potential for UAV systems to revolutionize disaster management and contribute to more resilient, responsive strategies in future flood events.

Foggy road conditions present substantial challenges to road monitoring and autonomous driving systems, as existing defogging techniques often fail to accurately recover structural details, manage dense fog, and mitigate artifacts. In response, a novel defogging model is proposed, incorporating Pythagorean fuzzy aggregation, Gaussian Mixture Models (GMM), and the level-set method, aimed at overcoming these limitations. Unlike conventional methods that depend on fixed priors or oversimplified haze models, the proposed framework leverages the advantages of Pythagorean fuzzy aggregation to enhance contrast and detail restoration, GMM to estimate fog density robustly, and the level-set method for precise edge preservation. The performance of the model is quantitatively assessed, revealing a Peak Signal-to-Noise Ratio (PSNR) of up to 37.1 dB and a Structural Similarity Index (SSIM) of 0.96, which significantly outperforms existing defogging techniques. Statistical analyses further confirm the robustness of the approach, with a p-value of less than 0.001 for key performance metrics. Additionally, the model demonstrates an execution time of 0.07 seconds, indicating its suitability for real-time road monitoring applications. Qualitative assessments highlight the model's ability to restore natural road colours and maintain high structural fidelity, even under conditions of dense fog. This work provides a promising advancement over current methods, with potential applications in autonomous driving, traffic surveillance, and smart transportation systems.

Crowd logistics (CL) represents an innovative model within the logistics sector, leveraging the participation of individuals to enhance service provision, optimize resource utilization, and reduce operational costs. Among the various applications of CL, crowd distribution has emerged as one of the most prevalent methods. This study introduces a Multi-Criteria Decision-Making (MCDM) framework for the selection of CL platforms, examining key factors that contribute to their success. A comprehensive review of relevant literature and an in-depth analysis of both domestic and global platforms were conducted, revealing critical performance indicators for successful platform implementation. The Step-wise Weight Assessment Ratio Analysis (SWARA) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) methods were employed to evaluate essential criteria, including cost efficiency, delivery speed, reliability, environmental sustainability, flexibility, and customer support quality. The results of this analysis demonstrate that platforms such as Company 1, Company 2, and Company 3 have achieved market dominance in Serbia, attributed to their optimal balance across these performance criteria. This study’s proposed model serves as a practical tool for businesses and consumers seeking to select the most suitable CL platforms, while also providing actionable insights for further enhancement of logistics systems. The findings contribute to the growing body of knowledge on CL, highlighting the importance of comprehensive evaluation in the selection process.

Advanced ship detection technologies play a critical role in improving maritime safety by enabling the rapid identification of vessels and other maritime targets, thereby mitigating the risk of collisions and optimizing traffic efficiency. Traditional detection methods often demonstrate high sensitivity to minor variations in target appearance but face significant limitations in generalization, making them ill-suited to the complex and dynamic nature of maritime environments. To address these challenges, an enhanced ship detection method, referred to as YOLOv5-SE, has been proposed, which builds upon the YOLOv5 framework. This approach incorporates attention mechanisms within the backbone network to improve the model's focus on key features of small targets, dynamically adjusting the importance of each channel to boost representational capacity and detection accuracy. In addition, a refined version of the Complete Intersection over Union (CIoU) loss function has been introduced to optimize the loss associated with target bounding box prediction, thereby improving localization accuracy and ensuring more precise alignment between predicted and ground-truth boxes. Furthermore, the conventional coupled detection head in YOLOv5 is replaced by a Decoupled Head, facilitating better adaptability to various target shapes and accelerating model convergence. Experimental results demonstrate that these modifications significantly enhance ship detection performance, with mean Average Precision (mAP) at IoU 0.5 reaching 94.9% and 95.1%, representing improvements of 3.1% and 1.2% over the baseline YOLOv5 model, respectively. These advancements underscore the efficacy of the proposed methodology in improving detection accuracy and robustness in challenging maritime settings.

An innovative context-aware fuzzy logic transmission map adjustment method is proposed for road image defogging, aimed at improving visibility and clarity under varying fog conditions. Unlike conventional defogging techniques that rely on a uniform transmission map, the presented approach introduces a fuzzy logic framework that dynamically adjusts the transmission map based on local fog density and contextual factors. Fuzzy membership functions are employed to classify fog density into low, medium, and high categories, enabling an adaptive and context-sensitive adjustment process. Road images are segmented into distinct regions using edge detection and texture analysis, with each region treated independently to preserve critical details such as road markings, lane boundaries, and traffic signs. A key contribution is the integration of proximity-based adjustments for areas near high-intensity light sources, such as streetlights, to maintain brightness and enhance visibility in illuminated zones. The final transmission map is generated through the combination of fuzzy density-based adjustments and an iterative Gaussian filter, which smooths transitions and minimizes potential artifacts. This approach prevents over-darkening while enhancing contrast, even in dense fog conditions. Experimental results demonstrate that the proposed method significantly outperforms traditional defogging techniques in terms of brightness, contrast, and detail retention. The results underscore the utility of fuzzy logic in road image defogging, offering a robust solution for applications in autonomous driving, surveillance, and remote sensing. This method sets a new benchmark for visibility enhancement in challenging environments, providing a high-quality, adaptive solution for real-world applications.

Automated Guided Vehicles (AGVs) represent a transformative advancement in the automation of transport operations, facilitating unmanned mobility within a wide array of environments, including production lines, warehouses, freight hubs, and terminal operations. In container terminals, where AGVs are increasingly deployed, the routing of these vehicles is a critical task aimed at minimising operational inefficiencies such as travel time, fuel consumption, and overall transportation costs. Routing in this context refers to the determination of optimal paths for a fleet of AGVs, which must satisfy a variety of operational constraints while also adhering to predefined user requirements. Given the high complexity of these problems, characterised by a large solution space, finding exact solutions is computationally intractable for most scenarios. As a result, heuristic methods are commonly employed to approximate optimal solutions. Among the various heuristic techniques, the nearest neighbor algorithm and Tabu search have been identified as promising approaches for determining efficient AGV routes in container terminal environments. These methods are applied to identify paths that minimise travel distance and time, enhancing resource utilisation and improving the overall reliability of goods delivery. The application of these algorithms is expected to lead to a significant reduction in the number of kilometres travelled by AGVs, thereby lowering operational costs and improving service efficiency. This paper examines the efficacy of the "nearest neighbor" and Tabu search algorithms in the context of AGV routing at container terminals, highlighting their potential to optimise fleet operations in the face of complex logistical challenges. Emphasis is placed on the comparative analysis of both algorithms, with a focus on their ability to approximate optimal solutions in dynamic and highly constrained environments.