Mechatronics and Intelligent Transportation Systems

Mechatronics and Intelligent Transportation Systems, 2026, Volume 5, Issue 2, Pages undefined: A Bio-Inspired Multi-Modal State Evaluation and Game-Theoretic Coordination Approach for Active Safety in Intelligent Public Transport Systems

li wang — 04-16-2026

Mechatronics and Intelligent Transportation Systems, 2026, Volume 5, Issue 2, Pages undefined: Cycle-Aware Adaptive Horizon Model Predictive Control for Vehicle Trajectory Optimization at Signalized Intersections

magzhan atykhan — 04-14-2026

The increasing complexity of modern urban traffic networks demands intelligent control strategies that can anticipate and adapt to dynamic traffic conditions. Model Predictive Control (MPC) is a framework that optimizes vehicle control by predicting future states and respecting real-time constraints, such as traffic signals at intersections. However, the computational complexity of MPC increases significantly with the number of decision variables and constraints, which is directly proportional to the length of the prediction horizon, creating a critical trade-off between control performance and computational efficiency. To address this challenge, this paper proposes an adaptive-horizon optimal driving (AHOD) bi-level optimization framework that incorporates a novel time-step discretization for real-time trajectory optimization and integrates it into a full traffic signal cycle. Unlike conventional MPC, which employs uniform time discretization leading to exponential growth in decision variables with horizon length, the proposed AHOD framework assigns finer time steps near signal phase transitions and coarser steps in the distant horizon, maintaining a fixed number of optimization nodes regardless of cycle length. The proposed framework comprises two controllers: the upper and lower controllers. The Upper controller employs finer resolution at critical times of signal change and coarser resolution in distant horizons, thereby reducing computational cost while maintaining prediction accuracy. The lower controller applies a practical MPC scheme to generate realtime control actions that are consistent with the long-term constraints of the upper controller. Simulation results demonstrate that the proposed framework achieves up to 17.6% fuel savings compared to traditional human driving and reduces computation time by approximately 61% compared to long-horizon MPC, while maintaining comparable control performance. The proposed framework enables real-time, cycle-aware predictive control for connected and automated vehicles (CAVs), and establishes a practical basis for embedding long-horizon prediction within an MPC-based trajectory-planning framework.

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Mechatronics and Intelligent Transportation Systems, 2026, Volume 5, Issue 2, Pages undefined: Genetic Algorithm-Optimized Mamdani Fuzzy Logic Control for Robust Quadrotor Trajectory Tracking

mohammed mansour — 04-02-2026

This paper presents a genetic algorithm (GA) tuned Mamdani type fuzzy logic control (FLC) framework for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) using a nonlinear rigid body model. The proposed architecture adopts a cascaded structure in which an outer loop position controller generates attitude and thrust references $(\phi_{\mathrm{ref}},\theta_{\mathrm{ref}},T_{\mathrm{ref}})$, while an inner loop attitude controller generates body torques $(\tau_\phi,\tau_\theta,\tau_\psi)$. Both loops employ a shared Mamdani fuzzy inference system with normalized inputs (tracking error and error-rate) and a normalized control output. The GA automatically tunes scaling gains $(K_e,K_d,K_u)$ across all axes to minimize a robust objective that averages tracking error, control effort, and constraint violations over multiple scenarios with mass uncertainty and wind disturbances. Simulation results on a three dimensional figure eight trajectory indicate that GA tuning can reduce position and attitude errors while respecting actuator saturation and tilt safety limits, demonstrating a practical route to performance enhancement without requiring a high fidelity aerodynamic model. The methodology leverages the interpretability of fuzzy rules and the global search capabilities of evolutionary optimization within a UAV modeling framework consistent with established quadrotor dynamics literature.

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Mechatronics and Intelligent Transportation Systems, 2026, Volume 5, Issue 2, Pages undefined: An Intelligent PSO-Optimized Informer Framework for Predictive Maintenance of Marine Shafting Systems Based on Bearing Vibration Analysis

xiao ma — 04-02-2026

Rolling bearings are critical components of marine shafting power transmission systems, and accurate prediction of their vibration signal trends is essential for predictive maintenance. To address the limited adaptability of conventional time-series forecasting models under varying operating conditions and their insufficient ability to capture strong noise and abrupt changes, this study proposes a vibration signal prediction method that integrates particle swarm optimization (PSO) with an improved Informer model. PSO is used to adaptively optimize key Informer hyperparameters for different operating conditions, while a rolling time-window mechanism is introduced to enhance the capture of abrupt signal variations. In addition, a mixture of sparse attention (MoSA) encoder with a collaborative dense-head/sparse-head structure is designed to balance global temporal dependency modeling and local fault feature extraction. Experimental results on the Case Western Reserve University (CWRU) bearing fault dataset show that the proposed model outperforms Long Short-Term Memory (LSTM), Transformer, Informer, iTransformer, and Flowformer in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Erro (RMSE). The model achieves an MSE of 0.2015, which is 25.5% lower than that of the second-best iTransformer model. It also demonstrates robust performance under four different bearing operating states, confirming its adaptability to complex operating conditions. The proposed method provides a promising technical route for the predictive maintenance of rolling bearings in marine shafting systems.

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Mechatronics and Intelligent Transportation Systems, 2026, Volume 5, Issue 1, Pages undefined: Management Analysis and Control Strategies for the Road Incident Dynamics Model

mehboob ali — 03-23-2026

Mechatronics and Intelligent Transportation Systems, 2026, Volume 5, Issue 1, Pages undefined: Cooperative Scheduling of Aerial and Ground Service Vehicles for Urban Public Service Tasks: A Multi-Objective Optimization Approach

xin liao — 03-15-2026

Efficient coordination of heterogeneous mobile resources is essential for delivering large-scale urban services, such as sanitation, infrastructure inspection, or last-mile delivery. This study addresses the problem of scheduling aerial and ground service vehicles—unmanned aerial vehicles (UAVs) and mobile ground crews—to cover spatially distributed demand points under operational constraints. We formulate the task as a multi‑objective optimization problem that simultaneously maximizes service coverage, minimizes total completion time, and optimizes resource utilization while respecting safety, capacity, and time‑window restrictions. A hierarchical solution framework is proposed: global task allocation first assigns demand zones to vehicle types according to their capabilities, and local path planning then generates efficient routes for each agent. A dynamic re‑optimization mechanism adjusts schedules in real time when disturbances occur, such as resource depletion or environmental changes. The method is evaluated on scenarios of increasing scale (51, 113, and 212 demand points) that emulate urban public spaces. Results from ten repeated experiments show that the cooperative strategy achieves coverage rates (CRs) above 97% across all scales, reduces total operation time (TOT) by up to 33% compared with single‑mode operations, and improves resource efficiency by 21.10% and 47.40% Statistical analysis confirms the robustness of the improvements. The framework offers a scalable, resource‑aware solution for coordinating heterogeneous service fleets, with direct applicability to intelligent transportation systems, particularly in demand‑responsive urban services and multimodal fleet management.

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Mechatronics and Intelligent Transportation Systems, 2026, Volume 5, Issue 1, Pages undefined: Calendar- and Weather-Sensitive Short-Term Forecasting of Urban Bus Ridership: Evidence from the Transjakarta Bus Rapid Transit System

hanifah khairunnisa — 03-11-2026

Urban public transport systems are required to respond to pronounced temporal variations in passenger demand driven by calendar effects, weather conditions, and evolving mobility patterns. Reliable short-term demand forecasts have therefore become an important role in supporting operational planning and service management in large-scale transit systems. This study examines the daily ridership dynamics of the Transjakarta bus rapid transit system and evaluates the forecasting performance of three modeling approaches: seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), multilayer perceptron (MLP), and a dynamic moving-window model. The analysis is based on 851 daily observations from January 1, 2023 to April 30, 2025, with rainfall, working days, and national holidays included as exogenous variables. Each model is estimated using a training dataset and evaluated on a hold-out test set over a 30-day forecasting horizon. Forecast accuracy is assessed using the mean absolute percentage error (MAPE). The results indicate that the MLP model achieves the highest forecasting accuracy, with a MAPE of 8.547%, while SARIMAX and the dynamic model yield higher error levels of 33.345% and 37.754%, respectively. The findings suggest that non-linear modeling approaches are better suited to capturing the complex and irregular demand patterns observed in daily urban bus ridership data. The study provides empirical evidence that can support short-term planning and demand-aware operational decision-making in urban public transportation systems.

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Mechatronics and Intelligent Transportation Systems, 2026, Volume 5, Issue 1, Pages undefined: Cybersecurity in Intelligent Transportation Systems: A Comparative Study on AI-Based Anomaly Detection and Threat Analysis

recep arslan — 03-04-2026

The rapid integration of technology, with increasing speeds, has transformed vehicles into cyber-physical systems by connecting them to each other Vehicle-to-Everything (V2X), significantly expanding the attack surface and leaving them vulnerable to network-based threats. Current cyber intrusion detection systems (CIDS) exhibit performance degradation due to significant class imbalance, limited resilience against adversarial attacks, and insufficient interpretability for security-critical environments. To overcome the identified issues in this study, we propose Hierarchical Classifier-Agnostic Boosted Stacking for Network Intrusion Detection (HCABS-NID), a hierarchical classifier-agnostic boosted stacking architecture for network intrusion detection in connected device ecosystems. The proposed framework adds the Synthetic Minority Over-sampling Technique for Nominal and Continuous features (SMOTENC)-based adaptive class balancing to increase minority attack detection and TreeSHAP to make it multi-level interpretable. As a hierarchical stacking strategy, a two-layer structure includes heterogeneous learners together with meta-learning, calibrated with LightGBM, XGBoost, CatBoost, and TabNet to take advantage of the complementary decision boundaries. Extensive experiments performed on the benchmark dataset from University of New South Wales Network-Based 15 (UNSW-NB15) should enhance generalization performance. HCABS-NID achieved 98.20% accuracy, 97.10% macro F1 score, and 0.989 macro Receiver Operating Characteristic Area Under the Curve (ROC-AUC), in contrast to the latest community-based methods found in the literature. The proposed model achieves 3.40 ms average inference latency, satisfying the real-time processing requirement of the V2X safety systems. Indeed, other analysis architectures show the same 96.8% accuracy at 5% corruption, which underscores their practicality. The results validate that hierarchical ensemble learning, with adaptive imbalance management artificial intelligence (AI) mechanisms, provides a sound, interpretable, and ready-to-use intelligent transportation security package.

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Mechatronics and Intelligent Transportation Systems, 2026, Volume 5, Issue 1, Pages undefined: A Lightweight Real-Time Vision Framework for Road Infrastructure Monitoring in Intelligent Transportation Systems

quanliang chen — 01-29-2026

Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 4, Pages undefined: Life-Cycle Analysis of Energy Efficiency in Battery Electric Vehicles

luca piancastelli — 12-21-2025

This study examines the energy efficiency of battery electric vehicles (BEVs) from a system-level and lifecycle perspective. The analysis highlights that the high mass of automotive battery packs, often an order of magnitude greater than conventional fuel tanks for equivalent energy storage, contributes to increased vehicle weight and may necessitate higher installed power to maintain performance. Consequently, larger battery capacities and associated increases in vehicle dimensions are commonly required, which in turn influences subsystems such as tires, resulting in higher rolling resistance. Operational advantages of BEVs are primarily observed in low-speed acceleration, while overall efficiency can be limited by frictional losses and auxiliary energy demands. Battery production is particularly energy-intensive, accounting for a substantial portion of the embodied energy in BEVs. Charge and discharge efficiencies vary with current rates and usage conditions. Slow charging, which takes approximately 10–12 hours, can reach an efficiency of around 95%, while fast charging from 25 to 75% of capacity over one hour typically achieves an efficiency of 85–90%. Discharge efficiency decreases from near 95% at low rates to roughly 70% at high C-rates (10–15 C). Despite the high efficiency of modern electric motors, including permanent magnet machines, system-level efficiency is further impacted by battery losses, power electronics, and auxiliary components. The effective energy delivery from the grid depends on the generation and distribution infrastructure; in contexts dominated by thermal power and limited renewable penetration, overall electricity efficiency may be around 40%, comparable to modern internal combustion engine vehicles, which operate at 40–50% thermal efficiency. Finally, current battery recycling technologies recover only 40–50% of materials and require additional energy input, highlighting limitations in end-of-life management. These findings suggest that BEVs may not always offer a clear energy efficiency advantage over conventional vehicles when evaluated on a comprehensive, life-cycle basis.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 4, Pages undefined: MCSSA-CNN-BiLSTM-Attention Model-Based Prediction and Compensation Control for Rebound in Ship Hull Outer Plate Processing

dongxu liu — 11-17-2025

This study proposed a novel pin-level dynamic compensation strategy to combat the critical challenge of springback in the three-dimensional numerically controlled bending of ship hull plates. A collaborative prediction model combining convolutional and bidirectional recurrent networks (CNN-BiLSTM) was optimized using an improved metaheuristic algorithm, the Modified Sparrow Search Algorithm (MCSSA), to achieve millimeter-level precision in springback compensation. Based on the 225-pin independent control architecture, the system enabled real-time compensation with millisecond-level response ($\leq$ 50 ms) on standard industrial computing hardware, to overcome the limitations of conventionally fixed compensation methods. The optimized algorithm enhanced global search capability, population diversity, and convergence efficiency, hence yielding a prediction accuracy of RMSE = 4.41 $\times$ $10^{-5}$ mm. The integrated spatiotemporal learning framework effectively combined feature extraction, sequential modeling, and critical region emphasis, to achieve a test-set $R^2$ of 0.969. Industrial validation of the SKWB-1600 system demonstrated significant improvements in traditional stepwise approximation methods: (i) Post-compensation forming errors were reduced to 0.13–0.26 mm with a 47–62% improvement; and (ii) Curvature errors in high-stress zones were maintained within $\pm$ 0.02 mm, thus forming iterations decreased by 42% and energy consumption reduced by 35%. This certified pin-level dynamic compensation solution provides a new methodology for forming precision of complex curved ship hull plates under industrial conditions and establishes a technical paradigm for manufacturing related components requiring high precision and efficiency.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 4, Pages undefined: Prediction of Road Safety Risks through Crack Detection and Structural Deterioration Assessment

sie long kek — 11-11-2025

Road surface cracks are a major contributor to vehicular accidents, particularly in high-speed and high-traffic environments. Conventional crack detection techniques that rely on grayscale imaging often fail to maintain accuracy under varying lighting conditions and in the presence of noise. To address these challenges, a robust detection methodology is proposed, based on a Gradient-based Crack Enhancement, Color Consistency, and Smoothness Regularization Model (GCSM). This model leverages Gaussian smoothing to reduce noise, gradient-based enhancement to accentuate crack features, and color consistency to effectively differentiate cracks from surrounding textures. Smoothness regularization ensures the continuity of crack patterns and minimizes false positives, enhancing the accuracy of detection. The resulting crack maps form the foundation for advanced risk analysis, directly linking crack detection to safety evaluation. The integration of crack detection with accident prediction is achieved by a hybrid model that estimates the likelihood of accidents induced by road surface deterioration. This hybrid model combines logistic regression to assess variables such as crack density, width, traffic volume, vehicle speed, and pavement condition, with a fuzzy inference system (FIS) to handle the imprecision inherent in road condition assessments. The final accident risk score is computed as a weighted combination of these components, offering enhanced prediction accuracy. Experimental results on datasets from Peshawar, Khyber Pakhtunkhwa, demonstrate that GCSM outperforms existing methods in terms of Intersection over Union (IoU), Precision, Recall, and Structural Similarity Index Measure (SSIM), with statistical significance (p < 0.01) confirmed via ANOVA. The hybrid prediction model achieves an accuracy of 88.23% and a mean squared error (MSE) of 0.042, highlighting its efficiency and robustness. This framework facilitates automated crack visualization and accident risk classification, providing valuable insights for engineers and urban planners. Future work will focus on real-time deployment and system adaptability to various road conditions, supporting intelligent transportation systems and proactive road safety management.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 4, Pages undefined: Safety Risk Factors of Urban Logistics Drone Delivery in the Context of the Low-Altitude Economy

yuanyuan zhang — 10-27-2025

This study analyzes the safety risk transmission mechanism in urban logistics drone last-mile delivery within the policy-driven low-altitude economy. To address the limitations of traditional risk identification methods, which rely heavily on accident data, this research integrates the Fuzzy Decision Analysis Laboratory Method (Fuzzy-DEMATEL),Interpretive Structural Modeling (ISM), and the Matrix of Cross-Impact Multiplication (MICMAC) to construct a three-dimensional analytical framework based on causal relationships, structural hierarchy, and attribute classification.First, Fuzzy-DEMATEL is employed to quantify the strength of causal relationships among risk factors. Next, ISM is used to deconstruct the multi-level hierarchical network and identify fundamental causes within the risk system. Finally, MICMAC is applied to calculate the dependencies and driving forces of each influencing factor, helping prioritize risk governance measures. The research findings indicate that: (1) The safety risk system of urban logistics drones for last-mile delivery exhibits a “dual-core driven – multi-loop coupled” characteristic. Equipment failures act as the physical carriers of systemic failures, while the root-cause risks stem from institutional factors such as inadequate pre-service training and violations of laws and regulations. (2) The risk hierarchy follows a pyramid-shaped transmission path, with risks propagating from the root layer to the surface in successive layers. Open airspace serves as an accelerator, transforming environmental disturbances into institutional defects, which in turn lead to technical failures. (3) The dependency attributes of each factor indicate the priority order for risk prevention and control: management leverage points serve as the strategic control core, the environment-technology interaction network is central to joint prevention, standardized processes solidify basic operations, and systemic risk levels are reduced.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 4, Pages undefined: Influential Factors on Braking Coefficients and Variability in Braking Force of Vehicle Service Brakes: A Neural Network Approach

sreten simović — 10-12-2025

Vehicles comprise several critical systems, including the braking, steering, transmission, and suspension systems, which operate in concert to ensure safe and efficient movement. Research has established that vehicle malfunctions, particularly in the braking system, contribute significantly to road accidents, with technical failures accounting for approximately 15% of crashes and brake system failures responsible for 17.4% of these incidents. In light of this, an investigation was conducted to identify the factors that influence the braking coefficient and the variability of braking force in vehicle service brakes. A total of 1,018 vehicles were involved in the study, with results indicating that variables such as vehicle production year, category, place of registration, engine power and displacement, gross and curb weight, and payload significantly affect the braking coefficient. Furthermore, the analysis revealed that factors such as vehicle production year, category, registration location, gross and curb weight, and payload are prominent in determining the braking force variability. Neural network analysis was employed to further assess these influential factors, highlighting that the year of manufacture, place of registration, and vehicle payload are particularly influential in predicting both compliance with minimum braking coefficient requirements and variations in braking force. The findings underscore the importance of these factors in the development of more precise models for vehicle brake performance, with potential implications for safety standards and regulatory frameworks.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 3, Pages undefined: Enhancing Performance and Reducing Latency in Autonomous Systems Through Edge Computing for Real-Time Data Processing

anil kumar pallikonda — 09-18-2025

The integration of edge computing for real-time data processing in autonomous systems has been identified as a promising solution to mitigate the performance bottlenecks and latency associated with traditional cloud-based models. Autonomous systems, including vehicles, drones, and robotics, rely heavily on quick data analysis to make timely decisions. However, cloud computing, with its inherent data transmission delays, hinders the responsiveness and efficiency of these systems. To address these challenges, edge computing is proposed as a means to process data locally, at the point of use, thus enabling faster decision-making processes and reducing data transfer overhead. This approach leverages distributed machine learning for decision-making and dynamic resource allocation to balance computational tasks between edge and cloud resources. Through extensive experimentation, it has been demonstrated that the edge computing paradigm can reduce latency by up to 65%, offering substantial improvements in both energy efficiency and data processing speed when compared to traditional cloud-based methods. Furthermore, the proposed system outperforms existing alternatives in terms of computational speed, reliability, and energy consumption. The introduction of an Edge Computing Decision Model (ECDM) and a Dynamic Resource Allocation Algorithm (DRAA) is shown to optimize system performance by balancing computational demands between local edge nodes and remote cloud servers. These innovations enable autonomous systems to function more effectively and efficiently, even in resource-constrained environments. This study highlights the importance of integrating edge computing into autonomous system architectures to meet the growing demand for low-latency, high-performance applications. The potential of edge computing to significantly enhance the reliability and operational capacity of autonomous systems has been established, paving the way for more reliable and scalable solutions in real-time environments.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 3, Pages undefined: A Color and Shape-Aware TSR Model Enhanced by Morphological Filtering and Fuzzy Logic

shahrina ismail — 09-09-2025

Real-time traffic sign recognition (TSR) plays a crucial role in intelligent transportation systems (ITS) and autonomous driving technologies. It enhances road safety, ensures efficient traffic rule enforcement, and supports the seamless operation of both autonomous and driver-assist systems. This paper proposes a hybrid TSR model that integrates mathematical morphology, edge detection, and fuzzy logic to accurately identify and classify traffic signs across diverse environmental conditions. The preprocessing stage applies contrast enhancement and Gaussian filtering to improve the visibility of key features. Next, shape- and color-based segmentation using mathematical morphology extracts regions of interest that are likely to contain traffic signs. These regions are then analyzed using a fuzzy inference system (FIS) that evaluates features such as color intensity, geometric shape ratios, and edge sharpness. The fuzzy system handles the inherent ambiguity in visual patterns, enabling robust decision-making. The entire model is developed in MATLAB R2015a, ensuring both computational efficiency and real-time performance. The integration of classical mathematical techniques with fuzzy reasoning allows the system to maintain high accuracy and reliability across a wide variety of traffic scenes. The proposed approach demonstrates significant potential for practical deployment in ITS applications, including smart vehicles and automated road safety systems.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 3, Pages undefined: Adoption of Real-Time Data and Functional Modeling to Predict Urban Traffic Crashes

izhar ullah — 07-17-2025

Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 3, Pages undefined: Entropy-Based Visibility and Fuzzy Logic Integration for Robust Object Detection in Foggy Road Environments

shakeel ahmad — 07-14-2025

Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 3, Pages undefined: Multi-Channel Functional Gradient–Entropy Model for Robust Road Boundary Detection in Complex Visual Environments

zia ur rahman — 06-25-2025

Accurate and consistent road boundary detection remains a fundamental requirement in autonomous driving, traffic surveillance, and intelligent transportation systems, particularly under diverse lighting and environmental conditions. To address the limitations of classical edge detectors in complex outdoor scenarios, a novel multi-channel edge detection framework is proposed, termed the Multi-Channel Functional Gradient–Entropy (MC-FGE) model. This model has been specifically designed for colour road imagery and incorporates a mathematically principled architecture to enhance structural clarity and semantic relevance. The initial phase involves channel-wise normalization of RGB data, followed by the computation of a fused gradient magnitude that preserves edge information across heterogeneous spectral distributions. Two original mathematical constructs are introduced: the Spectral Curvature Function (SCF), which quantifies local geometric sharpness by leveraging first- and second-order differential operators while exhibiting resilience to noise; and the Colour Entropy Potential Function, which captures local texture complexity and intensity-driven irregularity through entropy analysis of chromatic distributions. These functions are combined into a unified Functional Edge Strength Map (FESM), designed to emphasize semantically meaningful road-related boundaries while suppressing irrelevant background textures. A central innovation is the Log-Root Adaptive Thresholding Function (LRATF), which adaptively modulates threshold sensitivity by integrating curvature and entropy cues in a logarithmic-root formulation, thereby improving robustness to illumination variability, occlusions, and shadow interference. The final binary edge map is derived through precision thresholding of the FESM and refined using morphological post-processing to ensure topological continuity and suppress artefactual edge fragments. Quantitative and qualitative evaluations conducted across varied outdoor datasets demonstrate that the MC-FGE model consistently outperforms conventional edge detectors such as Canny, Sobel, and Laplacian of Gaussian, particularly in scenarios involving texture-rich road surfaces, poor lighting, and partial occlusion. The model not only exhibits enhanced detection accuracy and edge coherence but also offers improved interpretability of road features, contributing both a rigorous theoretical foundation and a scalable computational framework for adaptive edge-based scene understanding in road environments.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 2, Pages undefined: A Comprehensive Bibliometric Review of Autonomous Vehicle Research: Trends, Disciplines, and Future Directions

eray can zorlu — 05-27-2025

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.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 2, Pages undefined: Robust Image Processing Framework for Real-Time Detection of Road Potholes under Environmental Variability

abdul samad — 05-26-2025

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.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 2, Pages undefined: A Functional Energy Minimization Framework for the Detection of Crash Stones on Road Surfaces in Intelligent Transportation Systems

dolat khan — 05-20-2025

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.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 2, Pages undefined: Robust Segmentation of Concrete Road Surfaces via Fuzzy Entropy Modelling and Multiscale Laplacian Texture Analysis

muhammad shahkar khan — 05-12-2025

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.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 2, Pages undefined: A Hybrid Soft Computing Framework for Robust Classification of Heavy Transport Vehicles in Visual Traffic Surveillance

ibrar hussain — 05-05-2025

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.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 1, Pages undefined: Automated Vehicle Dent Detection Using Hybrid Image Processing and Fuzzy Decision Making

ikram ullah — 03-30-2025

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.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 1, Pages undefined: Integrating Cultural Norms and Behavioral Risk Factors into Traffic Accident Mitigation: A Hybrid MCDM Approach for Libya

ibrahim badi — 03-16-2025

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.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 1, Pages undefined: DSTGN-ExpertNet: A Deep Spatio-Temporal Graph Neural Network for High-Precision Traffic Forecasting

seyyed ahmad edalatpanah — 02-26-2025

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).

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 1, Pages undefined: An Efficient Road Image Dehazing Model Based on Entropy-Weighted Gaussian Mixture Model and Level Set Refinement for Autonomous Driving Applications

muhammad zeeshan naeem — 02-19-2025

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.

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Mechatronics and Intelligent Transportation Systems, 2025, Volume 4, Issue 1, Pages undefined: Optimized Collaborative Scheduling of Unmanned Aerial Vehicles for Emergency Material Distribution in Flood Disaster Management

jing gao — 01-19-2025

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.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 4, Pages undefined: A Robust Road Image Defogging Framework Integrating Pythagorean Fuzzy Aggregation, Gaussian Mixture Models, and Level-Set Segmentation

luqman alam — 12-30-2024

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.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 4, Pages undefined: Strategic Selection of Crowd Logistics Platforms: A Multi-Criteria Decision-Making Approach

janja kozoderović — 12-24-2024

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.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 4, Pages undefined: Ship Detection Based on an Enhanced YOLOv5 Algorithm

xin liu — 11-09-2024

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.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 4, Pages undefined: A Region-Based Fuzzy Logic Approach for Enhancing Road Image Visibility in Foggy Conditions

muhammad shahkar khan — 10-16-2024

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.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 4, Pages undefined: Optimising AGV Routing in Container Terminals: Nearest Neighbor vs. Tabu Search

adis puška — 10-14-2024

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.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 3, Pages undefined: An Adaptive Multi-Stage Fuzzy Logic Framework for Accurate Detection and Structural Analysis of Road Cracks

ibrar hussain — 09-29-2024

The degradation of road infrastructure presents significant challenges to public safety and maintenance budgets, with cracks serving as critical indicators of structural instability. Despite extensive advancements, existing detection methodologies frequently fail to address complex surface textures, variable illumination, and diverse crack geometries, resulting in inconsistent performance. An adaptive, multi-stage framework has been developed to mitigate these limitations, integrating advanced image processing techniques with fuzzy logic-based analysis. The proposed approach utilises dynamic contrast enhancement and multi-scale feature extraction to ensure accurate detection of both fine and extensive cracks across heterogeneous surfaces. A fuzzy graph-based methodology is employed to evaluate crack connectivity, while an adapted algorithm is applied to assess continuity and severity. The framework incorporates fuzzy wavelet transforms to enhance feature segmentation and employs morphological techniques for precise crack boundary delineation. Dijkstra’s algorithm is integrated to optimise connectivity analysis, facilitating the identification of critical structural deficiencies. The performance of the model has been rigorously validated through extensive experimental testing, achieving an accuracy rate of 94.2%, with high precision and recall metrics. Comparative analysis with conventional techniques reveals a significant reduction in false detection rates and an improved capacity for capturing intricate crack features. The results underscore the practical utility of the proposed model, demonstrating its scalability and reliability across diverse roadway conditions. By enabling early and accurate identification of structural anomalies, the framework enhances roadway safety, minimises maintenance costs, and supports proactive infrastructure management. The findings highlight its potential as a transformative solution for addressing modern challenges in road maintenance, with implications for improved public safety and resource optimisation.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 3, Pages undefined: Optimizing Electric Vehicle Charging Infrastructure: A Site Selection Strategy for Ludhiana, India

harpreet kaur channi — 09-26-2024

This study investigates the spatial distribution and potential expansion of electric vehicle (EV) charging infrastructure in Ludhiana, India, with a focus on optimizing site selection to accommodate increasing demand. A multi-criteria framework was employed, incorporating traffic volume, demographic data, and usage patterns of existing charging stations to identify high-priority locations. Central commercial zones, including Ghumar Mandi, Feroze Gandhi Market, ISBT Ludhiana, and Ludhiana Railway Station, were found to exhibit significant traffic density and high EV ownership rates, making them prime candidates for the establishment of new charging stations. Spatial analysis, including heat maps, bar graphs, and pie charts, was used to visualize these key areas, revealing critical patterns in demand and facilitating the strategic targeting of infrastructure expansion. Community engagement was emphasized as an essential component in ensuring that infrastructure development aligns with user needs and preferences. The study further highlighted the importance of accessibility, economic viability, and sustainability as pivotal criteria for site selection. The findings offer valuable insights for urban planners and policymakers, supporting the development of a robust EV charging network that contributes to the advancement of sustainable urban mobility and the reduction of carbon emissions in Ludhiana. These results provide a basis for informed decision-making in the design of EV infrastructure, guiding the city's efforts towards an eco-friendly, future-ready transportation system.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 3, Pages undefined: Performance Evaluation of a Four-Legged Signalized Intersection with Variable Traffic Flow Dynamics

eren dağlı — 08-30-2024

The rapid growth of population and vehicular traffic has necessitated effective urban planning strategies to mitigate traffic congestion and enhance roadway efficiency. This study focuses on a critical signalized intersection in Konya, Turkiye’s largest metropolitan area, which is notable for its agricultural, industrial, and educational significance. Strategically positioned at the nexus of major transportation routes linking the Black Sea and Central Anatolia regions to the Mediterranean and Aegean areas, Konya exhibits considerable logistical potential. The Coşandere intersection, located in the Selçuklu district, was selected for analysis due to its four-legged configuration, featuring three lanes on both the south and north approaches and two lanes on the east and west approaches. Additionally, suitable turning islands and U-turn pockets are provided on the south and north approaches. Observational data indicate that the evening peak period poses significant operational challenges. A video surveillance system was employed to monitor vehicle movements, yielding a traffic volume of 1,874 vehicles per hour. The existing geometric design, traffic dynamics, and signalization were modelled using PTV Vissim software to assess the intersection's performance. The analysis revealed an average delay of 44.1 seconds per vehicle, an average of 0.9 stops per vehicle, and an average vehicle speed of 29.6 km/h, resulting in a Level of Service (LOS) classification of D. These findings indicate that the intersection currently accommodates traffic demand to a moderate degree. However, substantial improvements in operational efficiency could be achieved through enhancements to the signalization system, including the potential implementation of an adaptive traffic signal control system. This study provides valuable insights for traffic management authorities and urban planners aiming to optimise intersection performance in rapidly developing urban environments.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 3, Pages undefined: Exploring the Dynamics of Maglev Trains on Curved Bridges: A Case Study from the Fenghuang Maglev Sightseeing Express

xiao liang — 07-30-2024

Magnetic levitation (maglev) transportation represents an advanced rail technology that utilizes magnetic forces to lift and propel trains, eliminating direct contact with tracks. This system offers numerous advantages over conventional railways, including higher operational speeds, reduced maintenance requirements, enhanced energy efficiency, and reduced environmental impact. However, the dynamic interaction between maglev trains and railway bridges, particularly curved bridges, presents challenges in terms of potential instability during operation. To better understand the dynamic behavior of maglev trains on curved bridges, an experimental study was conducted on the Fenghuang Maglev Sightseeing Express Line (FMSEL), the world’s first “Maglev + Culture + Tourism” route. The FMSEL employs a unique ‘U’-shaped girder design, marking its first application in such a setting. Field test data were collected to analyze the dynamic characteristics of the vehicle, suspension bogie, curved rail, and ‘U’-shaped bridge across a range of train speeds. The responses of both the train and bridge were examined in both time and frequency domains, revealing that response amplitudes increased with train speed. Notably, the ride quality of the vehicle remained excellent, as indicated by Sperling index values consistently below 2.5. Furthermore, lateral acceleration of the train was observed to be lower than vertical acceleration, while for the track, vertical acceleration was consistently lower than lateral acceleration. These findings offer insights into the dynamic performance of maglev trains on curved infrastructure, highlighting key factors that must be considered to ensure operational stability and passenger comfort.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 3, Pages undefined: Optimising Dual Pantograph-Catenary System Performance in Curved Sections for Enhanced High-Speed Railway Operations

zhao xu — 07-15-2024

The spatial configuration of the pantograph-catenary system (PCS) is significantly altered by the superelevation present in curved railway tracks, leading to deviations in the system’s dynamic behaviour and imposing constraints on operational speeds. In this study, a detailed model of the PCS in curved sections has been developed to evaluate the dynamic performance of a dual PCS under these conditions. It was observed that the contact loss rate of the trailing pantograph increases markedly as train speed rises, with this effect being more pronounced in curved sections compared to straight tracks. This degradation in performance necessitates optimisation strategies to ensure operational efficiency at higher speeds. To address the issue, it is proposed that the static uplift force of the trailing pantograph be increased when trains traverse curved sections. Additionally, optimisation of the catenary system is recommended, involving both a reduction in the span length and an increase in the tension of the contact wire. By implementing these strategies, the dual PCS can sustain the necessary contact and satisfy dynamic performance criteria at speeds of up to 300 km/h in curved sections. These findings provide valuable insights for improving the reliability and safety of high-speed railway operations on complex track geometries.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 2, Pages undefined: Self-Tuning Parameters of a Maglev Control System Based on Q-Learning

yang wang — 06-29-2024

Maglev transportation, as an innovative mode of rail transit, is regarded as an ideal future transportation system due to its wide speed range, low noise, and strong climbing ability. However, the maglev control system faces challenges such as significant nonlinearity, open-loop instability, and multi-state coupling, leading to issues like insufficient tuning and susceptibility to environmental influences. This paper addresses these problems by investigating the self-tuning parameters of a maglev control system using Q-learning to achieve optimal parameter tuning and enhanced dynamic system performance. The study focuses on a basic levitation unit modeled after the simplified control system of an electromagnetic suspension (EMS) train. A Q-learning reinforcement learning environment and Q-learning agent were established for the levitation system, with a forward "anti-deadlock" reward function and discretization of the action space designed to facilitate reinforcement learning model training. Finally, a Q-learning-based method for self-tuning the parameters of the maglev control system is proposed. Simulation results in the Python environment demonstrate that this method outperforms the Linear Quadratic Regulator (LQR) control method, offering better control effects, improved robustness, and higher tracking accuracy under system parameter perturbations.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 2, Pages undefined: Optimizing Vehicle Collision Safety: A Two-Mass Model with Dual Springs and Dampers for Accurate Crash Dynamics Prediction

badr ait syad — 06-29-2024

A comprehensive analysis of vehicle collision dynamics is presented using a two-mass model that simulates the impact of a vehicle against a rigid barrier. The model incorporates dual springs and dampers to examine the influence of spring stiffness and damping on a mass attached to the vehicle. The equations of motion are solved utilizing state variables, while energy principles are employed to establish correlations between vehicle deformation, impact force, and acceleration. Validation is conducted through comparison with crash test data from a 2023 Honda Accord LX 4-Door Sedan. Average deformation values are used to calculate acceleration, followed by a Monte Carlo simulation to analyze acceleration data recorded by the engine sensor, enabling the determination of vehicle speed through integration. Parametric regression is applied to optimize model parameters, resulting in a high degree of concordance between experimental and theoretical values. The model's accuracy is further verified through the analysis of velocity and deceleration profiles and the integration of the deceleration curve. The findings underscore the model's capability to replicate real-world crash dynamics, highlighting its potential to enhance vehicle safety system design. The innovation of this research lies in its simplified yet effective approach to modeling collision dynamics, offering significant insights into the relationship between vehicle deformation and occupant forces. This work advances the understanding of vehicle collision mechanics and provides a robust tool for the development of advanced safety features. The integration of theoretical and empirical data reinforces the model's reliability, contributing substantively to the field of automotive safety engineering.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 2, Pages undefined: Analysis of Variables Influencing Towing Limits in Self-Propelled Rail Track Maintenance Equipment

dipak patil — 06-26-2024

The towing limits for self-propelled rail track maintenance equipment (SP-TME) are influenced by a multitude of factors, including the type and weight of the equipment, speed, braking capabilities, track and weather conditions, traction, engine power, driveline performance, coupler/towing link integrity, and safety regulations. This study investigates these variables to determine their impact on the towing limits of SP-TME. Unlike traditional rail vehicles, SP-TME possesses unique operational constraints and specifications, necessitating careful consideration of its independent mobility. An extensive analysis was conducted on the towing usage and overuse of SP-TME during travel mode, examining various scenarios that incorporate different combinations of trailing load, rail track grade, rail curvature, and weather conditions. These scenarios, ranging from normal to worst-case, aim to simulate demanding operational environments. The parameters evaluated include structural strength, traction, engine and driveline performance, wheel rolling and skidding, braking capabilities, trailing load, speed, and track and weather conditions. Results indicate that under normal and moderate conditions, the equipment can tow significantly higher loads than the defined base load. However, in special situations, such as negotiating tighter curves and steeper grades in adverse weather conditions, wheel skidding and locking emerge as limiting factors. Findings related to service and parking brake performance during steep grade descents, particularly when the trailer lacks independent braking capabilities, are also presented. Recommendations and cautions are provided to ensure safe and efficient operation of SP-TME under various conditions.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 2, Pages undefined: A Bibliometric Analysis of Trends and Collaborations in Autonomous Driving Research (2002-2024)

edi purwanto — 05-30-2024

Through the deployment of bibliometric techniques and network visualizations, this analysis synthesizes the evolution and trajectories of autonomous driving research from 2002 to May 2024, as captured in the Scopus database encompassing 342 scholarly documents. This study was conducted to delineate the developmental contours, thematic emphases, and the expansive growth trajectory within this field, particularly noting a surge in scholarly outputs since 2017. Such growth has been primarily facilitated by breakthroughs in artificial intelligence and sensor technologies, along with burgeoning interdisciplinary collaborations and escalating academic and industrial investments. A meticulous examination of publication trends, document types, subject areas, and geographic distributions elucidates the multidisciplinary and international nature of this burgeoning field. Key thematic clusters identified—comprising foundational technologies, environmental sustainability, safety measures, regulatory frameworks, user experience, connectivity, and technological innovations—underscore the prevailing research directions and emerging focal areas poised to shape future autonomous mobility solutions. Notably, increased emphasis on environmental sustainability and regulatory frameworks has been observed, highlighting their critical roles in advancing and integrating autonomous driving systems. This study provides pivotal insights for researchers, policymakers, and industry stakeholders, fostering a nuanced understanding of the field’s dynamics and facilitating strategic alignments and innovations in autonomous mobility. The emergent research domains and collaborative networks revealed herein not only map the current landscape but also guide future scholarly endeavors in autonomous driving systems globally.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 2, Pages undefined: Enhancing Occluded Pedestrian Re-Identification with the MotionBlur Data Augmentation Module

zhen xue — 04-29-2024

In the field of pedestrian re-identification (ReID), the challenge of matching occluded pedestrian images with holistic images across different camera views is significant. Traditional approaches have predominantly addressed non-pedestrian occlusions, neglecting other prevalent forms such as motion blur resulting from rapid pedestrian movement or camera focus discrepancies. This study introduces the MotionBlur module, a novel data augmentation strategy designed to enhance model performance under these specific conditions. Appropriate regions are selected on the original image for the application of convolutional blurring operations, which are characterized by predetermined lengths and frequencies of displacement. This method effectively simulates the common occurrence of motion blur observed in real-world scenarios. Moreover, the incorporation of multiple directional blurring accounts for a variety of potential situations within the dataset, thereby increasing the robustness of the data augmentation. Experimental evaluations conducted on datasets containing both occluded and holistic pedestrian images have demonstrated that models augmented with the MotionBlur module surpass existing methods in overall performance.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 1, Pages undefined: Design and Implementation of a Digital Twin System for Monitoring Automated Container Terminal Equipment

houjun lu — 03-30-2024

To address the lack of multi-perspective, real-time monitoring and management of operations and equipment in automated container terminals, a digital twin system targeted at monitoring automated container terminal equipment has been designed and developed. Based on the concept of a five-dimensional model of digital twins, a digital twin framework for monitoring automated container terminal equipment was constructed. The system's maintainability is enhanced through a layered design, which also reduces coupling between different functional modules. A multi-dimensional, multi-scale virtual scene was built and model consistency evaluations were conducted to verify the system. The system's operational efficiency was improved by optimizing model rendering with discrete level of detail (LOD) techniques. A multi-layered distributed solution for the digital twin system was proposed to achieve multi-perspective monitoring. Ultimately, using a specific automated container terminal as a case study, a system prototype was developed, realizing multi-perspective digital monitoring of terminal operations and equipment. This project offers a solution for the application of digital twin technology in the field of automated container terminals and promotes the development of intelligent digital terminals.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 1, Pages undefined: Enhancing Urban Traffic Management through YOLOv5 and DeepSORT Algorithms within Digital Twin Frameworks

haoyuan kan — 03-10-2024

The acceleration of urbanization and the consequent increase in population have exacerbated urban road traffic issues, such as congestion, frequent accidents, and vehicle violations, posing significant challenges to urban development. Traditional manual traffic management methods are proving inadequate in meeting the demands of rapidly evolving urban environments, necessitating an enhancement in the intelligence level of urban road traffic management systems. Recent advancements in computer vision and deep learning technologies have highlighted the potential of image processing and machine learning-based traffic management systems. In this context, the application of object detection and tracking technologies, particularly the YOLOv5 and Deep learning-based Simple Online and Realtime Tracking (DeepSORT) algorithms, has emerged as a pivotal approach for the intelligent management of urban traffic. This study employs these advanced object detection and tracking technologies to identify, classify, track, and measure vehicles on the road through video analysis, thereby providing robust support for urban traffic management decisions and planning. Utilizing digital twin technology, a virtual replica of traffic flow is constructed from camera data, serving as the dataset for training different YOLOv5 algorithm variants (YOLOv5s, YOLOv5m, and YOLOv5l). Upon comparison of training outcomes, the YOLOv5s model is selected for vehicle detection and recognition in video feeds. Subsequently, the DeepSORT algorithm is applied for vehicle tracking and matching, facilitating the calculation of vehicles' average speed based on tracking data and the temporal interval between adjacent frames. Results, stored in Comma-Separated Value (CSV) format for future analysis, indicate that the system is capable of accurately identifying, tracking, and computing the average speed of vehicles across various traffic scenarios, thereby significantly supporting urban traffic management and advancing the intelligent development of urban road traffic. This approach underscores the critical role of integrating cutting-edge object detection and tracking technologies with digital twin models in enhancing urban traffic management systems.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 1, Pages undefined: Evaluating the Road Environment Through the Lens of Professional Drivers: A Traffic Safety Perspective

aleksandar trifunović — 03-03-2024

In the context of traffic safety, the interplay between the road environment and the human factor emerges as a critical determinant of the severity of road crash consequences. This study was designed to explore the perceptions of professional drivers regarding the road environment, with a particular focus on the elements that either contribute to or mitigate safety risks. A comprehensive survey was conducted, wherein 118 professional drivers from the Republic of Serbia were asked to rate photographs depicting various road environments in terms of safety. The investigation aimed to elucidate the extent to which these drivers recognize and assess road hazards, as well as to examine potential variations in their evaluations based on demographic characteristics. The findings underscore the significant impact of the road environment on traffic safety, particularly highlighting the role of solid obstacles such as trees, pillars, and masonry objects. When vehicles veer off the road, collisions with these obstacles frequently result in exacerbated outcomes of road crashes. The methodology employed in this research involved a quantitative analysis of the survey responses, ensuring a systematic evaluation of the drivers' perceptions. The study contributes to the existing body of knowledge by offering insights into the evaluative processes of professional drivers concerning the road environment, thereby informing strategies aimed at enhancing driver safety.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 1, Pages undefined: Enhancing Cold Chain Logistics: A Framework for Advanced Temperature Monitoring in Transportation and Storage

vukašin pajić — 01-28-2024

In the face of the increasingly demanding of goods transportation and storage, the orchestration of cold chain logistics emerges as a critical and multifaceted endeavor. This study, addressing a notable gap in literature, establishes a comprehensive framework for temperature monitoring within cold chain logistics, focusing particularly on transportation and warehousing aspects. The complexity of managing temperature-sensitive goods is amplified by the burgeoning number of entities involved in this sector, underscoring the need for a robust monitoring approach. Recent global challenges have precipitated a series of disruptive events, further complicating the reliable transport of temperature-sensitive commodities. In light of these challenges, the necessity for meticulous temperature control during both transportation and warehousing phases is paramount; lapses in this regard could lead to grave consequences. A thorough analysis of existing cold chain delivery systems was conducted, alongside an examination of various temperature monitoring devices utilized in vehicle cargo compartments and storage facilities. The study not only scrutinizes current trends but also introduces novel solutions for effective monitoring. By exploring and evaluating these elements, the research contributes significantly to both theoretical and practical spheres, offering a solid foundation for future investigations and guidance for practitioners and decision-makers in the field. This exploration revealed the imperative for advanced sensor technologies and integrated data management systems, capable of providing real-time, accurate temperature readings throughout the entire cold chain process. The integration of smart transportation solutions, leveraging Internet of Things (IoT) technology, emerges as a pivotal factor in enhancing the reliability and efficiency of temperature monitoring. Additionally, the study underscores the importance of standardized protocols and practices across the industry to ensure consistency and reliability in temperature management. In conclusion, the framework proposed in this study not only addresses existing challenges in cold chain logistics but also paves the way for innovative approaches in temperature monitoring, fostering enhanced quality control and safety in the transportation and storage of temperature-sensitive goods.

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Mechatronics and Intelligent Transportation Systems, 2024, Volume 3, Issue 1, Pages undefined: Cause Analysis of Whole Vehicle NVH Performance Degradation under Idle Conditions

haiping lai — 01-16-2024

Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 4, Pages undefined: Simulation Analysis of Track Irregularity in High-Speed Maglev Systems Based on Universal Mechanism Software

xiangyang jia — 12-25-2023

Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 4, Pages undefined: Automated Development of Railway Signalling Control Tables: A Case Study from Serbia

ivan ristic — 12-07-2023

Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 4, Pages undefined: Machine Learning for Road Accident Severity Prediction

koteswararao kodepogu — 12-04-2023

Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 4, Pages undefined: Economic Feasibility of Solar-Powered Electric Vehicle Charging Stations: A Case Study in Ngawi, Indonesia

singgih dwi prasetyo — 11-27-2023

In the context of increasing electric vehicle (EV) prevalence, the integration of renewable energy sources, particularly solar energy, into EV charging infrastructure has gained significant attention. This study investigates the economic viability of grid-connected photovoltaic (PV) systems for EV charging stations in Ngawi City, Indonesia, selected due to its substantial solar energy potential and ongoing renewable energy initiatives. Key factors influencing the economic feasibility of these systems include load requirements, renewable energy potential, system capacity, levelized cost of electricity, payback period, net present cost (NPC), and cost of energy (COE). A comprehensive techno-economic assessment was conducted to estimate the capital recovery time, incorporating both utilization costs and payback periods. The analysis utilized the Hybrid Optimization Model for Electric Renewables (HOMER) software, focusing on the application of PV energy in EV charging stations within Ngawi Regency. Findings indicate that a PV system-based generation approach can adequately meet the power needs of EV charging stations. Notably, this system is capable of generating surplus energy, which presents an opportunity for additional revenue, thus enhancing its economic attractiveness. The analysis determined that to produce an annual output of 562,227 kWh, a total of 1245 PV modules, each with a 370-watt capacity, are necessary. This off-grid PLTS system, relying exclusively on PV modules for electrical energy generation, can sufficiently supply a daily load of 342.99 kWh for an EV charging station. The study underscores the potential of solar-powered EV charging stations in contributing to sustainable urban development, reinforcing the integration of renewable energy into urban infrastructure.

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Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 4, Pages undefined: Advanced Vehicle Detection and License Plate Recognition via the Kanade-Lucas-Tomasi Technique

egina nyati — 11-12-2023

The optimization of traffic flow, enhancement of safety measures, and minimization of emissions in intelligent transportation systems (ITS) pivotally depend on the Vehicle License Plate Recognition (VLPR) technology. Challenges predominantly arise in the precise localization and accurate identification of license plates, which are critical for the applicability of VLPR across various domains, including law enforcement, traffic management, and both governmental and private sectors. Utilization in electronic toll collection, personal security, visitor management, and smart parking systems is commercially significant. In this investigation, a novel methodology grounded in the Kanade-Lucas-Tomasi (KLT) algorithm is introduced, targeting the localization, segmentation, and recognition of characters within license plates. Implementation was conducted utilizing MATLAB software, with grayscale images derived from both still cameras and video footage serving as the input. An extensive evaluation of the results revealed an accuracy of 99.267%, a precision of 100%, a recall of 99.267%, and an F-Score of 99.632%, thereby surpassing the performance of existing methodologies. The contribution of this research is significant in addressing critical challenges inherent in VLPR systems and achieving an enhanced performance standard.

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Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 3, Pages undefined: A Comprehensive Exploration of Resource Allocation Strategies within Vehicle Ad-Hoc Networks

sadashiviah sheela — 09-29-2023

In recent years, a surge in the utilisation of vehicle-to-vehicle (V2V) communication has been observed, serving as a pivotal factor in facilitating automatic control of vehicles without human intervention. This advancement has notably curtailed accident rates, mitigated traffic congestions, and augmented vehicular security. Consequently, a meticulous survey has been orchestrated in the domain of Vehicle Ad-Hoc Networks (VANETs), particularly as autonomous vehicles pervade urban landscapes. The necessity for resources to assure secure and consistent operations of an escalating fleet commensurately intensifies with the enlargement of the fleet itself. Intelligent Transportation Systems (ITS) hinge upon VANETs to furnish travellers with secure and pleasant journeys, pertinent information and entertainment, traffic management, route optimisation, and accident prevention. Nevertheless, a plethora of challenges inhibits the delivery of an adequate Quality of Service (QoS) within vehicular networks, such as congested and interrupted wireless channels, a progressively saturated and sprawling spectrum, hardware inconsistencies, and the swift expansion of vehicular communication systems. Contemporary networks and energy grids are subject to strain from daily and recreational activities. As demand perpetually ascends, a necessity arises for more refined tools and methodologies for resource management and a more precise distribution system. This investigation offers an exploration of the most recent practices and trends in VANET resource allocation, with the objective of garnering insights into the existing research landscape and its impelling forces.

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Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 3, Pages undefined: Enhanced Vehicle Performance Through Nonlinear Finite Element Analysis of Tyre-Soil Interaction

fatemeh aliramezani — 09-29-2023

In this investigation, critical insights into the complex interactions between tyres and soil are explored through the utilization of nonlinear finite element analysis (FEA), bearing significant implications for vehicle dynamics, safety, and performance. Maximal shear stress values, identified through shear stress analyses, reveal a peak of 8.4 MPa in the tyre-road contact region and an approximately uniform shear stress of 1.703 MPa in alternative areas, laying the foundation for advancements in tyre design optimisation. It was demonstrated that tyre designs necessitate optimisation to specific ground materials to fulfil essential traction requirements and preclude sinking. For interfaces involving soil and neoprene rubber, the contact status at the mid-section zone was observed to be in a sticking condition, transitioning to sliding as the observation point moved away from the centre. The research highlighted that through nonlinear analysis, accurate predictions of tyre behaviour under fluctuating loads can be achieved, thereby aiding in the formulation of designs for more fuel-efficient tyres and enhanced wet-weather handling. However, the study recognises the constraints imposed by simplifications within the tyre model, omission of dynamic behavioural factors, and assumptions regarding unvarying friction coefficients. While the analysis was confined to particular material models and validation was executed primarily via numerical simulations, findings affirm that strategic application of nonlinear FEA elucidates pivotal factors in tyre-soil interaction, propelling the establishment of safer and more performance-oriented vehicle models.

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Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 3, Pages undefined: Regional Transformation via Rail: A Historical and Analytical Examination of Iran's Railway Network and its Socio-Economic Impacts

stabak roy — 09-26-2023

The role of transport infrastructure, especially railways, in shaping a nation's socio-economic and cultural dynamics is of paramount importance. The present research delves into the profound influence of the railway network on Iran's regional transformation, from its inception to present times. An in-depth historical evaluation uncovers the genesis and expansion of the Iranian railway system, linking it intricately with pivotal junctures in the nation's trajectory. Emphasis is placed on regions undergoing substantial developmental shifts, attributable to enhanced rail connectivity, offering distinct examples of varied growth paradigms. Economic repercussions manifest as interregional trade augmentation, resurgence of industries, and alterations in employment landscapes, thereby positing railways as an integral component of Iran's economic blueprint. Concurrently, an exhaustive scrutiny of socio-cultural realms underscores railways' pivotal role in fostering intercultural exchanges and expediting urbanisation trends. From an environmental perspective, the sustainability merits of rail transport are illuminated, accentuating the increasing pertinence of ecological considerations in railway's prospective expansion. Through meticulous case studies, a comparative narrative emerges between areas endowed with rail connectivity and those situated in relative isolation. The objective is to elucidate railways as instigators of transformative shifts. This study culminates with projections grounded in potential technological advancements poised to reshape Iran's railway infrastructure and the ensuing regional implications. Findings underscore railways' monumental impact on Iran's socio-economic fabric, illuminating their potential as change agents and offering invaluable insights for global infrastructure strategising.

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Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 3, Pages undefined: Stacked Extreme Learning Machine with Horse Herd Optimization: A Methodology for Traffic Sign Recognition in Advanced Driver Assistance Systems

praveen kumar jayapal — 08-20-2023

In the quest for autonomous vehicle safety and road infrastructure management, traffic sign recognition (TSR) remains paramount. Recent advancements in accuracy across various benchmarks have been identified in the literature concerning this essential task. Such technology might remain absent in older vehicles, while integration into Advanced Driver Assistance Systems (ADAS) is common in more recent models. Yet, the capability of these systems to function proficiently under diverse driving conditions has not been widely investigated. A framework has been devised to allow a moving vehicle to detect traffic signs, targeting the enhancement of driver safety and the diminishment of accidents. The present research introduces an innovative methodology, amalgamating the extreme learning machine (ELM) method with deep-learning paradigms, in response to experimental discoveries. As a pioneering computational approach in neural network-based learning, ELM facilitates rapid training and commendable generalization. An accuracy of 95.00% was achieved by the proposed model. By utilizing the Horse Herd Optimization method (HHOA), the memory consumption is minimized in the more sophisticated approach of stacked ELM (SELM) within the deep-learning framework. This study contributes to the understanding of potential challenges that may be encountered during TSR tasks, and lays the groundwork for future investigation by proffering a diverse set of evaluations for various road scenarios. Consistency in the utilization of professional terms is maintained throughout.

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Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 3, Pages undefined: Finite Element Analysis and Electromagnetic Field Optimization of Linear Synchronous Motor in High-Speed Maglev Systems

maozhenning yang — 08-20-2023

Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 2, Pages undefined: Incorporating Climate Change Resilience in India’s Railway Infrastructure: Challenges and Potential

stabak roy — 06-28-2023

This study delves into the crucial task of embedding climate change resilience within the sphere of railway infrastructure planning and design in India. As climate change continues to threaten global transportation systems, the creation of robust, sustainable infrastructure becomes indispensable for minimizing its impacts. Initial investigation entails assessing both existing and anticipated climate change scenarios in India, encompassing elements like temperature fluctuations, changes in precipitation, and severe weather phenomena. Following this, the study proceeds to pinpoint the specific risks and vulnerabilities that the Indian railway system stands to confront due to these climatic shifts. A thorough exploration of current adaptation policies and strategies provides a framework to merge these into railway infrastructure planning and design, using a mix of literary review, best practices, and international case studies as resources. The Indian railway network undergoes a meticulous analysis to evaluate its vulnerability, leading to the identification of key adaptation measures like devising new railway tracks, enhancing the existing infrastructure, adopting resilience-based technologies, and implementing nature-centric solutions. The research probes the economic, social, and environmental ramifications of these measures, underlining the long-term sustainability and beneficial impacts on the transportation industry. Expert interviews, stakeholder consultations, and policy analysis culminate in a set of recommendations for policymakers, urban planners, and transportation authorities. These recommendations aim to shape the progression of a climate-resilient railway infrastructure in the light of India’s distinct challenges. Such an integration of climate change adaptation strategies contributes towards a more robust and sustainable transportation system. This study enriches the existing body of knowledge on climate change adaptation in transportation, offering valuable perspectives for policymakers, practitioners, and researchers aiming for climate resilience in the railway sector.

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Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 2, Pages undefined: Deep Learning-Enhanced Hybrid Fruit Fly Optimization for Intelligent Traffic Control in Smart Urban Communities

ghamya kotapati — 06-18-2023

The rapid urbanization accompanying the evolution into “smart” communities presents numerous challenges, not least of which is the significant increase in road vehicles. This proliferation exacerbates congestion and accident rates, posing major barriers to the successful implementation of innovative technologies such as Wireless Sensor Networks (WSNs), surveillance cameras, and the Internet of Things (IoT). Accurate traffic flow prediction, a crucial component of these technological initiatives, requires a reliable and efficient methodology. This research explores the implementation of an intelligent traffic control system that employs a Transferable Texture Convolutional Neural Network (TTCNN). The design of this system eschews the traditional pooling layer, instead incorporating three convolutional layers and a single Energy Layer (EL). This configuration facilitates the provision of real-time traffic updates, which can enhance the utility and efficiency of the smart city infrastructure. A model inspired by the Hybrid Fruit Fly (HFFO) optimizes the system's hyperparameters. The application of HFFO to the TTCNN showcases the potential for improved accuracy in traffic flow prediction. Simulation results suggest that the HFFO provides superior organizational boundaries for the TTCNN, enhancing the overall accuracy of the model's predictions. The hybrid forecasting method discussed herein demonstrates its potential to outperform other established techniques. This investigation sheds light on the potential benefits of applying deep learning algorithms and hybrid models in the context of traffic flow prediction and control, contributing to the ongoing development of smart urban communities.

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Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 2, Pages undefined: China-Europe Container Multimodal Transport Path Selection Based on Multi-objective Optimization

jingmiao zhou — 06-02-2023

Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 2, Pages undefined: Assessing Automatic Dependent Surveillance-Broadcast Signal Quality for Airplane Departure Using Random Forest Algorithm

rani silvani yousnaidi — 05-28-2023

This study aims to assess the safety level of the Automatic Dependent Surveillance-Broadcast (ADS-B) signal quality during airplane departures at Sultan Mahmud Badaruddin II Airport. The Aero-track application was utilized to monitor commercial aircraft departures and collect observation data. The collected data underwent processing using data analysis algorithms and labeling processes, resulting in a comprehensive dataset for evaluating ADS-B signal quality. Signal quality was categorized into four levels, and a model was built using the Random Forest algorithm, achieving an accuracy of 99%. Comparative analysis with SVM and Naive Bayes algorithms showed accuracy values of 93% and 97% respectively. Consequently, the Random Forest Model was chosen for estimating ADS-B signal quality during commercial aircraft takeoff and landing.

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Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 2, Pages undefined: Preparation of the User Requirements Specification for ETCS Level 2 System in Serbia - Experiences and Challenges

ivan ristic — 05-15-2023

Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 1, Pages undefined: Stability Simulation and Analysis of Maglev Vehicle at Different Speed Based on UM

pengyu yang — 03-30-2023

Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 1, Pages undefined: Dynamic Performance of Low- and Medium-Speed Maglev Train Running on the Turnout

sumei wang — 03-27-2023

This study aimed to analyze dynamic performance of the low- and medium-speed maglev train on the turnout, by conducting a series of field tests on the Fenghuang maglev sightseeing express line. This study systematically analyzed and evaluated dynamic responses of vehicle body, suspension bogie, short rail joint, motor-driven long-span girder (LSG) and continuous beam of the turnout at different speeds and loads in time and frequency domains. Test results obtained in this study indicated that: 1) speed and load of the maglev train affected acceleration amplitude of the train and turnout; 2) vertical sperling indexes of the test train were less than 2.5, indicating that the train had good ride quality; and 3) resonance phenomenon of track occurred at 40 km/h. The findings of this study may serve as references for model validation and optimized design of low- and medium-speed maglev train.

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Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 1, Pages undefined: Objective-Subjective CRITIC-MARCOS Model for Selection Forklift in Internal Transport Technology Processes

eldina huskanović — 03-19-2023

In today transport technology processes, forklifts are one of the most important equipment for making handling operations in order to increase sustainability. They have a large influence in achieving the efficiency and sustainability of internal transport. According to the previous studies, and based on the current needs of the company, skills, and knowledge of managers, criteria, and alternatives for evaluating forklifts were created. The paper aims to create an integrated decision-making model to improve the company's technological processes. The objective CRITIC (Criteria Importance Through Intercriteria Correlation) approach was used to determine the criteria weights which are a combination of economic, technological, technical and environmental criteria. MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) approach was applied to select the most suitable forklift in transport technology processes. Results show that A4 forklift is the most suitable, and the A1 forklift is the worst variant. Apart from this, sensitivity and comparative analysis have been done in order to verify the initial results.

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Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 1, Pages undefined: Development and Verification of an Autonomous and Controllable Mobile Robot Platform

Tianyu Jiang — 03-19-2023

In this paper, we design a mobile robot platform, which employs a fully autonomous mechanical structure and electrical control system. Two driving wheels realize flexible steering movement with four universal wheels. A variety of sensors are built on the mobile robot platform, including the Inertial Measurement Unit (IMU) used to establish the inertial navigation coordinate system and the Velodyne’s Puck lidar sensor (VLP-16) used to obtain the three-dimensional (3D) point cloud information of the environment. Then, we build a software control architecture based on the Robot Operating System (ROS), using multi-node communication to perform positioning, environment perception, dynamic obstacle avoidance, path planning and motion control. Furthermore, a method of actively exploring the environment and constructing a map is proposed, using multi-path evaluation for real-time path planning and obstacle avoidance. In the end, we conduct autonomous exploration experiments to verify the performance of the designed mobile robot platform in indoor multi-obstacle scenes.

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Mechatronics and Intelligent Transportation Systems, 2023, Volume 2, Issue 1, Pages undefined: Regional Classification of Serbian Railway Transport System Through Efficient Synthetic Indicator

stabak roy — 02-28-2023

The railway transport system is one of the most important elements in the development of the economy and the social space of any area. The main objective of the study is to analyse the regional differentiation in railway development in Serbia with causal interference. The research has been conducted based on secondary data collected from multiple sources, and the existing synthetic Indicator was applied to classify eight states based on their railway infrastructural status. An alternative synthetic Indicator approach has been proposed and found to be more efficient than the existing synthetic Indicator. The causality of such unequal development has been analysed through a correlation test by defining the composite infrastructure index. The analysis revealed that railway infrastructure significantly influences Serbia's economic and social development. The service area of railway infrastructure indicates the potential zone for future growth.

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Mechatronics and Intelligent Transportation Systems, 2022, Volume 1, Issue 1, Pages undefined: Design Configuration and Technical Application of Rotary-Wing Unmanned Aerial Vehicles

tianao zhao — 11-04-2022

Due to their advantages in hovering, takeoff and landing adaptability, maneuverability, and other factors, rotary-wing unmanned aerial vehicles (UAVs) are widely applied across many different fields. The UAVs' design and configuration can be quite flexible to fit diverse operation conditions. The major goal of innovations in rotary-wing UAVs is to lower operating risk and expense by optimizing payload and structure layout. This study examines three aspects of rotary-wing UAV design and evolution: the number and arrangement of rotors, hybrid-wing-based UAVs, and configuration and loading structures. The most current advancements of UAV applications in crucial industries, including agriculture, fire rescue, inspection and monitoring, and aerial logistics, are then thoroughly examined. Finally, the authors discussed the prospective uses for rotary-wing UAV design in the future.

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Mechatronics and Intelligent Transportation Systems, 2022, Volume 1, Issue 1, Pages undefined: Pavement Condition Assessment Using Pavement Condition Index and Multi-Criteria Decision-Making Model

omar elmansouri — 11-04-2022

Road maintenance is essential to the growth of the transportation infrastructure and, thereby, has a big impact on a nation's overall economic stability and prosperity. It is impossible to simultaneously monitor and maintain the entire network. As a result, transportation authorities are eager to develop scientific foundations for assessing the importance of maintenance tasks within the network of roads. Hence, pavement assessment methods are needed to establish the priorities and achieving the most convenient level of service. In this study, a road stretch was assessed using the sixteen criteria in the Distress Identification Manual for pavement defects, using pavement condition index (PCI) and multi-criteria decision-making models (MCDM). The two methods were compared to determine the possibility of using MCDM. The study came to the conclusion that MCDM is reliable in assessing pavement performance because both methods indicated that the road pavement is deteriorating.

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Mechatronics and Intelligent Transportation Systems, 2022, Volume 1, Issue 1, Pages undefined: Curve Negotiation Characteristics of the Side-Suspended High-Temperature Superconducting Maglev System

zongpeng li — 11-04-2022

Thanks to its superb curve negotiation characteristics, the side-suspended high-temperature superconducting (SS-HTS) maglev system boasts a great potential for high-speed transportation. The SS-HTS maglev system, however, significantly differs in suspension features from the conventional maglev system because of its unique side-suspended structure. To improve suspension performance, the field-cooling technique of superconducting bulks in the SS-HTS system was investigated through a number of experiments. To fit the experimental data, the authors proposed the mathematical models of the levitation and guidance forces as well as the optimal field-cooling position. Furthermore, a dynamic model was developed for the SS-HTS maglev vehicle operating on a curve line, and the curve negotiation characteristics were simulated for the maglev vehicle. Finally, the stability of the curve negotiation for the SS-HTS system was assessed using the Sperling index. The results show that the SS-HTS maglev vehicle can pass over bends at a certain speed. The authors also recommended the suspension parameters the maglev vehicle.

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Mechatronics and Intelligent Transportation Systems, 2022, Volume 1, Issue 1, Pages undefined: Field Tests and Analyses on Running Stability of Fenghuang Medium and Low Speed Maglev Train

yuheng ai — 11-04-2022

Multiple field tests were carried out on the Fenghuang medium and low speed maglev train. During the tests, the authors collected the vibration data of train carriage and suspension frames under no-load (AW0). Next, the stability of the maglev train under corresponding conditions was investigated, using indices like weighted RMS acceleration (ISO 2631) and Sperling index. Through the in-depth analyses, it was concluded that the maglev train runs smoothly, and the passengers on the train generally feel comfortable.

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Mechatronics and Intelligent Transportation Systems, 2022, Volume 1, Issue 1, Pages undefined: Modeling of Operating Speeds as a Function of Longitudinal Gradient in Local Conditions on Two-Lane Roads

marko subotić — 11-04-2022

The operating speed is the average value of the speed of traffic flow under normal conditions, i.e., the conditions of mutual interference of traffic participants. The operating speed serves as a gauge for how well a given roadway is performing under the applicable traffic conditions. All key decisions in the management of the growth and utilization of a road network, including planning, designing, evaluating, and implementing road projects, depend on accurate measures of capacity and level of service. This paper aims to develop a recommended model for operating speed on two-lane roads under local conditions by analyzing the operating speeds of the traffic flow on representative sections of such roads. Through the modeling process, the values of the 85th percentile of the operating speed were determined, and compared with relevant studies. The results show that the authors have successfully modeled operating speeds as a function of longitudinal gradient in local conditions on two-lane roads.

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Mechatronics and Intelligent Transportation Systems, 2022, Volume 1, Issue 1, Pages undefined: Design and Testing of Cooperative Motion Controller for UAV-UGV System

yuxue li — 11-04-2022

Unmanned ground vehicles (UGVs) and quadrotor unmanned aerial vehicles (UAVs) can work together to solve challenges like intelligent transportation, thanks to their excellent performance complements in perception, loading, and endurance. This study presents a UAV-UGV system cooperative control mechanism. To achieve collaborative trajectory tracking, the leader-follower strategy based on a centralized control structure is firstly established in conjunction with the application scenario. The fuzzy robust controller is created to control the quadrotor UAV and improve attitude stability. Meanwhile, the UGV's controller uses the pure pursuit algorithm and a proportional integral derivative (PID) controller. In order to evaluate the cooperative control strategy and algorithm, the UAV-UGV experimental platform is set up based on the QDrone and QCar, and the experimental results show the viability of the suggested plan.

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Mechatronics and Intelligent Transportation Systems, 2022, Volume 1, Issue 1, Pages undefined: Influence of Electromagnet-Rail Coupling on Vertical Dynamics of EMS Maglev Trains

yougang sun — 11-04-2022

Active control is essential for EMS maglev trains to achieve stable suspension. Currently, the main line's suspension performs well, but in areas with low track stiffness, such as the garage, turnouts, and other lines, unexpected coupling vibration is more likely to occur. Control parameters, vehicle parameters, and rail parameters are all closely related to this phenomenon. In this study, the vehicle-rail coupling dynamic equation with secondary suspension system is first established, and used to disclose the effects of different parameters on the electromagnet-rail coupling vibration of the EMS maglev train. Next, the authors adopted the proportional-derivative (PD) controller, and proposed the concept of maglev train control frequency. Next, a general simulation model was established based on the MATLAB/Simulink, and numerical simulation was carried out to reveal how the secondary suspension frequency, the control frequency and the rail frequency affect the electromagnet-rail coupling vibration. The research results provide a reference for the design of maglev trains, controllers, and tracks, laying a theoretical basis for the maintenance of maglev commercial lines.

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Mechatronics and Intelligent Transportation Systems, 2022, Volume 1, Issue 1, Pages undefined: Editorial to the Inaugural Issue

yougang sun — 11-04-2022

11-04-2022