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.

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.

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 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.

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.