International Journal of Heavy Vehicle Systems (34 papers in press)

Regular Issues

• Deep learning-based forecasting of port cargo throughput using PCA and error correction multivariate LSTM  
  by Sihao Wei, Wei Deng 
  Abstract: As an emerging technology, deep learning has been well-used in many fields. This paper mainly studies the application of deep learning in smart cities about port cargo throughput forecasting. Firstly, the port cargo throughput is analysed by Principal Component Analysis (PCA). Correlation analysis was carried out on the impact factors, and the screened GDP and container throughput was put into the multivariate long-term short-term memory neural network (LSTM) as external input factors to improve the accuracy, and a multivariate LSTM prediction model based on PCA was built; then, using the errors generated by the prediction model in the prediction of cargo throughput in Ningbo Zhoushan Port as training data, training to generate error sequences, and the prediction data are subjected to error correction to increase the prediction accuracy. Lastly, the model's forecast outcomes are contrasted with the vector autoregressive model (VAR), Holt-Winters, grey prediction model, and univariate LSTM model prediction results were compared and analysed. The comparison results show that the multivariate LSTM prediction model based on PCA and error correction has higher prediction accuracy
  Keywords: smart cities; artificial intelligence; deep learning; principal component analysis; multivariate long short-term memory neural network; error correction; port cargo throughput.
  DOI: 10.1504/IJHVS.2023.10059804
   
  
• Study on cold chain logistics vehicle path optimisation method based on improved artificial bee colony algorithm  
  by Fengju Chen, Jingzhao Zhang 
  Abstract: Cold chain logistics describes the process of transporting and storing perishable items from their point of manufacture to the final customer transportation and storage facilities equipped with refrigeration systems. Using cold chain logistics, perishable foods such processed foods, meats, seafood, ice creams, poultry, dairy products, vegetables, and fruits can be safely transported from the producer to the consumer. Effective planning of the cold chain logistics vehicle is crucial for minimising travel time, distance, and overall logistics costs in order to get the product to the consumer. One such artificial swarm intelligence technique is the Artificial Bee Colony (ABC) algorithm , which is inspired by the activities of bees and their colonies. The fundamental aim of this research is to reduce the time, distance and cost associated in transportation. The results show that effective cold logistics transportation and optimal path selection have been achieved with a 98 83% success.
  Keywords: cold chain logistics; path optimisation; artificial intelligence; transportation; artificial bee colony.
  DOI: 10.1504/IJHVS.2023.10061595
   
  
• Research on active suspension-based anti-rollover control strategy for side-unloading dump trucks during lifting operations  
  by Tianmin Zhu, Mingmao Hu, Qinghe Guo, Min Liu, Renjun Liu, Mengchao Wang, Zhongcheng Fu, Yu Wang 
  Abstract: Aiming at the problem of poor stability of side-unloading dump trucks under lifting conditions, an anti-rollover control strategy combining active suspension and fuzzy sliding mode control was proposed. Considering the changes in the mass and spatial position of the cargo during lateral unloading, a four-degree-of-freedom side-unloading dump truck nonlinear model was established, and the model's accuracy was validated using MATLAB/Simulink. The adjustment of sliding mode control parameters is realized through fuzzy logic, and the fuzzy-sliding mode controller is used to obtain the anti-roll moment that suppresses suspension deformation. A rollover warning controller was designed using the ratio of the right wheel load to the total vehicle weight as the rollover evaluation index. Simulation is conducted using ordinary sliding mode control and without any control for comparison. The results indicate that this strategy enhances the lateral stability of side-unloading dump trucks under lifting operations, effectively preventing rollover incidents.
  Keywords: side-unloading dump truck; active suspension; anti-rollover; fuzzy-sliding mode control.
  DOI: 10.1504/IJHVS.2024.10062414
   
  
• Lightweight security scheme for data management in the e-commerce platform  
  by Zhiwen Cai, Jingying Ke 
  Abstract: This paper proposes Lightweight Distributed Accessible Control (LW-DAC) for data management to secure users' data in an E-commerce Platform. The Lightweight Security method enables distributed user information access control for protected users. This system's security is reduced to the Bilinear Discrete Diffie-Hellman (BDDH) assumption, which helps the Decision Making Algorithm minimize the execution time. The proposed method provides easy data encryption, trapdoor keyword generation and data recovery. BDDH filters the keyword search to avoid accessing the terminal. The algorithm has been compared with the existing system and proven based on reliability and scalability highly efficient. The simulation analysis demonstrated that the proposed Lightweight Security Scheme for data management system encrypts the user data and prevents the backdoor access with high scalability, performance, and lesser mean square error.
  Keywords: bilinear Diffe-Hellman; lightweight; security; keyword search; backdoor and data management.
  DOI: 10.1504/IJHVS.2024.10064242
   
  
• Real-time emission test and evaluation method of heavy-duty diesel vehicle SCR system based on dynamic time warping  
  by Xuejian Ma, Tao Qiu, Yan Lei, Zexun Chen 
  Abstract: Selective catalytic reduction (SCR) technology is the most important technical to solve the high NOx emission of diesel vehicles. In-use heavy-duty diesel vehicles must undergo emission spot checks at inspection stations to avoid exceeding the NOx emission limits due to abnormal operation of the SCR system. A method that can quickly carry out in-situ emission tests on heavy-duty diesel vehicles on the road to evaluate SCR operation status is of great significance. In this paper, a method based on dynamic time warping (DTW) is proposed to quickly judge the SCR operation status when the vehicle is stationary. The variation of similarity between SCR inlet NOx and SCR outlet O2, the variation of similarity between SCR outlet O2 and SCR outlet NOx, and the effect of SCR working efficiency on SCR outlet O2 were analysed through experiments, and the proposed method was validated.
  Keywords: selective catalytic reduction technology; dynamic time warping; SCR outlet NOx; SCR outlet O2; diesel vehicle.
  DOI: 10.1504/IJHVS.2024.10064600
   
  
• A review of road surface recognition and tyre-road friction coefficient estimation methods  
  by Linhui Wang, Xiaobin Fan, Xueliang Yu, Zipeng Huang, Kaikai Zhao 
  Abstract: The tyre-road friction coefficient (TRFC) characterises the maximum interaction force that can be generated between the road surface and the tire, which directly affects the driving, braking, and handling stability of vehicles. Obtaining accurate estimates of TRFC can optimise the vehicles active safety control and improve decision-making and planning performance in autonomous driving. However, the existing TRFC identification methods are not very accurate and real-time when dealing with sudden changes inroad conditions under extreme working conditions. Therefore, this paper discusses the current domestic and international road recognition methods, provides a review based on recognition principles, and elaborates on the two main categories of existing identification methods. It introduces the adhesion rate estimation and road type recognition methods commonly involved in TRFC identification, analyses the new methods brought by neural networks and rubber friction theory to the adhesion coefficient estimation issue, and provides an outlook on future development directions.
  Keywords: tyre-road friction coefficient estimation; road recognition; Kalman filtering; neural networks; rubber friction.
  DOI: 10.1504/IJHVS.2024.10064673
   
  
• Lateral stability control of heavy-towing taxi-out based on differential braking  
  by Jiahao Qin, Jiaqi Ma, Peiyang Xu, Wei Zhang 
  Abstract: The new mode of towing taxi-out consisting of rodless tractor and aircraft, is prone to extreme lateral instability accidents such as slipping and jack-knifing during high-speed turns. To address this issue, a ten-degree-of-freedom dynamic model of the aircraft towing system is established using the Lagrangian analysis method. This model does not consider constraints and limitations at hinge points. Based on this model, a high-speed turn differential braking controller uses yaw rate deviation as yaw moment controller inputs and determine their respective additional yaw moments. According to the braking strategy and braking moment allocation rules, the controller outputs the required braking moments for the target wheels. Finally, through Matlab/Simulink simulations, the steering angle input and initial speed are varied under J-turn and double lane change condition. This allows the delineation of safe and dangerous areas for steering angles and speeds in towing taxi-out mode, and a comparison of the control effectiveness of differential braking.
  Keywords: Lagrangian analysis method; jack-knifing; differential braking; lateral stability.
  DOI: 10.1504/IJHVS.2024.10065548
   
  
• ANN-based RBF prediction for maximal energy recovery using hybrid optimisation in electric vehicles  
  by A. Velu, N. Chellammal 
  Abstract: Owing to the increasing number of Electric vehicles (EVs) that have propelled the global trend towards transportation electrification over the past few decades, the automotive industry has expanded its excessive investment in transportation electrification technology. However, the widespread maintenance of EVs is significantly complicated by their short driving range. Therefore, a lot of research is done to improve the efficiency and driving range of automobiles in both business and academia. The operational range of EVs can be expanded using regenerative braking technology. Initially, the parameters, like speed of the EV and State of Charge (SOC) of the battery are taken as input to the Artificial Neural Network (ANN) and the corresponding predicted regenerative braking force has been obtained as the desired outcome of ANN. The estimated accurateness of the ANN classifier is then enhanced by optimally altering its weight parameters by Wild Horse Insisted Sparrow Search Optimization (WHI-SSO).
  Keywords: regenerative braking force; EV; energy recovery; ANN; optimisation.
  DOI: 10.1504/IJHVS.2024.10066991
   
  
• Moving vehicle detection and tracking under hazy environment for traffic surveillance system  
  by Agha Asim Husain, Tanmoy Maity, R.K. Yadav 
  Abstract: Vehicle location is crucial for transportation and computer vision Bounding boxes distinguish cars, crucial for real-time applications like movement estimation, requiring precise area data This study presents an adaptive approach for accurate vehicle detection and tracking in challenging scenarios such as heavy traffic, poor visibility, and adverse weather conditions The proposed method integrates Fuzzy Subtraction and Gradient Partial Equation (FGPE) techniques for background subtraction, overcoming fluctuations and shadows It also uses energy and histogram-oriented gradient features, chosen through recursive feature elimination, to improve discrimination capability Further, a Normalization-based Attention Module (NAM) is integrated into the Enhanced YOLOv5 model for vehicle detection The feature extractor is enhanced with the Multi-Object based DeepSORT algorithm for vehicle tracking Deployment on edge devices achieves a traffic flow detection accuracy of 0 98% Evaluation metrics including Multiple-Object Tracking Algorithm (MOTA) and Multiple-Object Tracking Precision (MOTP) validate the effectiveness of the proposed model for real-world traffic
  Keywords: vehicle detection; tracking; MOTA; multiple-object tracking algorithm; traffic monitoring systems; MOTP; multiple-object tracking precision.
  DOI: 10.1504/IJHVS.2024.10067276
   
  
• Coordinated control of acceleration slip regulation and direct yaw moment control for distributed in-wheel motor drive electric articulated heavy vehicles  
  by Wei Gao, Wei Minxiang, Yuping He, Deng Zhaowen, Yonghui Jin, Baohua Wang 
  Abstract: This paper presents a coordinated control strategy for acceleration slip regulation (ASR) and direct yaw moment control (DYC) to improve the lateral stability of a distributed in-wheel motor drive electric articulated heavy vehicle (DIMDEAHV). A non-linear TruckSim model and a linear three-degree-of-freedom (3-DOF) yaw-plane model of DIMDEAHV are generated, and their fidelity is evaluated. An algorithm is then developed to identify road conditions using the - standard curve of the Burckhardt tire model. Built upon the road identification algorithm, an ASR controller is designed and proved to be effective in preventing wheel slip. Finally, a coordinated control strategy for ASR and DYC is developed and validated using co-simulation under a double lane change (DLC) manoeuvre. The simulation results demonstrate that compared to the DYC alone, the ASR and DYC coordinated control can effectively prevent the wheel slip and improve the lateral stability of the DIMDEAHV traveling at high speeds on
  Keywords: distributed in-wheel motor drive electric articulated heavy vehicle; acceleration slip regulation control; coordinated control; lateral stability; numerical simulation.
  DOI: 10.1504/IJHVS.2024.10068044
   
  
• Discovery of the global landscape for railroad pantograph research: a bibliometric review  
  by Munaliza Ibrahim, Mohd Azman Abdullah, Mohd Hanif Harun, Fathiah Mohamed Jamil, Fauzi Ahmad 
  Abstract: Railroad vehicles are highly dependent on the proper functioning of the pantograph, which makes it an indispensable device that picks up the electric current from the overhead line cable system. This study addresses the lack of a comprehensive analysis by conducting a bibliometric study of research publications on pantographs in railroads available in the Scopus database. The data was analysed using a sample of 1,234 publications between 1928 and 2023. The bibliometric analysis shows a significant increase in this research. The publication involved contributions from 159 authors across 41 countries and 160 institutions. The year 2018 saw the highest number of published articles, with 89. Southwest Jiaotong University from China was the most prolific institution in this research area. This study provides a pioneering overview of worldwide publications on pantographs using three bibliometric tools Microsoft Excel, VOSviewer and, Harzing's Publish or Perish software package.
  Keywords: railroad pantograph; Scopus database; bibliometric review; frequency analysis; Microsoft excel; data visualisation; VOSviewer; citation metrics; Harzing’s publish or perish.
  DOI: 10.1504/IJHVS.2024.10068045
   
  
• Research on ride comfort control of electro-hydraulic active suspension based on intelligent optimisation control strategy  
  by M.C. Wang, Renjun Liu, Qinghe Guo, Huihui Zhou, Shilu Guo, Weiliang Xu 
  Abstract: High-frequency excitation signals pose a severe impact on human health during vehicle operation. Designing a control strategy to enhance vehicle dynamic performance for the system of electro-hydraulic active suspension, characterised by parameter uncertainties and high-dimensional nonlinearity, is a significant challenge. In response, an intelligent optimisation control strategy was proposed: The Multi-Strategy Improved Beluga Whale Optimisation (MSIBWO) was used to optimise the parameters of a Hybrid Hierarchical Controller (HHC). The outer layer of the HHC employs impedance control to calculate the desired force. The inner layer uses a Fractional Order PID (FOPID) controller to control the nonlinear electro-hydraulic servo actuator, characterised by time-varying parameters, ensuring it tracks the desired force. Simulation analyses conducted in MATLAB/Simulink reveal that the proposed MSIBWO algorithm surpasses the BWO algorithm regarding convergence accuracy and speed. Furthermore, the intelligent optimisation control strategy significantly improves vehicle ride comfort while maintaining driving safety on high-frequency road surfaces.
  Keywords: high-frequency excitation; electro-hydraulic active suspension; ride comfort control; intelligent optimisation control; hybrid hierarchical controller; optimisation algorithm.
  DOI: 10.1504/IJHVS.2024.10068073
   
  
• Research on optimisation of regenerative braking strategy for pure electric mining dump truck based on MPC  
  by Weiwei Yang, Wenming Zhang, Nong Zhang 
  Abstract: With the development of environmental protection concepts, the proportion of battery electric mining trucks is gradually increasing. However, the problems of charging mileage and battery life degradation limit the application. Meanwhile, production efficiency is also reduced due to slower charging and battery replacement speeds. The paper mainly focuses on the problem of decreased battery life and the shorter driving range of a 50-ton pure electric mining dump truck, considering the influence of the shifting strategy. A model predictive control (MPC) algorithm is proposed, which verifies that the MPC strategy can reduce energy consumption and improve battery lifetime by comparing the original rule-based control algorithm. The simulation results show that the proposed MPC strategy can reduce energy consumption by 16.92% and reduce battery life loss by 18.21% in the typical cyclic working conditions of mining roads.
  Keywords: pure electric mining dump truck; gear shifting strategy; model predictive control; energy optimisation; battery life.
  DOI: 10.1504/IJHVS.2024.10068193
   
  
• Lateral stability classification and active stabilisation of towing taxi-out system based on K-clustering classification  
  by Weimin Huang, Jiahao Qin 
  Abstract: The towing taxi-out system composed of a tractor and a civil aircraft has the structural characteristics of driving in the front and mass in the back, which leads to accidents such as lateral instability and even system "folding" when the system is steering. In this paper, a lateral stability evaluation method based on K-means cluster analysis is proposed to characterise the lateral stability state parameters of the system. The lateral stability of the system is evaluated into four grades of safety, safety hazard, potential danger and danger, and a fuzzy control-based control method is designed to control the additional lateral moment, and a differential braking method is used to achieve the lateral stability of the system. The simulation results show that the k-mean cluster analysis can achieve the hierarchical evaluation of the lateral stability of the system.
  Keywords: traffic engineering; lateral stability; K-means clustering; fuzzy control; graded control.
  DOI: 10.1504/IJHVS.2024.10068194
   
  
• Analytical modelling and experimental study on the design optimisation of an electromagnetic retarder  
  by Kalyan Kumar Rajamanickam, Sridhar Seshadri 
  Abstract: An investigation on design optimization of an electromagnetic retarder has been carried out as it is crucial to balance braking performance and structural mass of assembly This article presents a cost-effective mathematical model as an alternative to expensive and time-consuming computational methods for predicting braking performance, considering factors like eddy current power loss, skin effect, and the magnetic reluctance of excitation devices, which were not addressed in earlier studies for a complex system with higher capacity in dynamic conditions. The mass and torque-influencing factors are systematically assessed to optimize design that provides significant mass savings while maintaining the necessary performance levels with the analytical results in close agreement with the experimental findings The optimized design led to a 12 3% reduction in mass compared to the initial design, with only a 2 1% decrease in performance The study offers guidance for manufacturers to optimize the design to fit the vehicle's
  Keywords: electromagnetic retarder; design; optimization; eddy current; skin effect; magnetic reluctance; braking torque; speed; deceleration; heavy vehicles.
  DOI: 10.1504/IJHVS.2024.10068465
   
  
• Improvements of a tram shape for pedestrian protection  
  by Hechao Zhou, Wanting Liu, Wenbin Wang 
  Abstract: In this paper, a three-dimensional collision between a city tram and a pedestrian is simulated based on the finite element method (FEM). From the point of view of pedestrian protection, two principles are proposed for the geometry design of the city tram front end. Firstly, the front end structure should be designed to reduce the pedestrian injury severity. Secondly, after the collision the front end structure should push the pedestrian away from the city tram's running zone so as to avoid the run over. Based on these principles, four key parameters for the optimization of the front end geometry are proposed, such as the clearance between the city tram and ground, the horizontal and vertical inclination angles of the streamline and whether there is any convex/concave structure. According to the simulation results, the influences of these parameters on the collided pedestrian’s moving posture and injury severity are comprehensively analysed.
  Keywords: city tram; collision; pedestrian injury; geometry design; optimisation.
  DOI: 10.1504/IJHVS.2024.10068466
   
  
• Enhancing performance of diesel engine with the collective impact of ternary nano fuel blends using DBO-vCANN approach  
  by S. Neelamegan, Y. Ras Mathew, M. Sivakumar, D.R. Srinivasan 
  Abstract: The rising global population is driving an increasing energy demand, primarily met by fossil fuels. This manuscript presents a hybrid approach for enhancing diesel engine performance and emissions characteristics of ternary nanofuelblends (TNFB). The proposed hybrid technique is the joint execution of Dung Beetle Optimizer (DBO) and Viscoelastic Constitutive Artificial Neural Networks (vCANN). Hence, named as DBO-vCANN technique. The main objective of proposed technique is to improve engine performance and minimize emission characteristics. The DBO method optimizes biodiesel production parameters, while vCANN model forecasts engine performance and emissions. The performance of proposed system is run on the MATLAB platform and is compared with existing methods. The proposed model accurately predicted compression ignition (CI) engine performance and emissions. The proposed approach provides better outcomes in existing Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and Ant Lion Optimization (ALO), methods. The proposed method achieves efficiency of 98%, demonstrating improved system performance.
  Keywords: viscosity; nanoparticles; esterification; trans esterification; ultra-sonication technique; combustion; biodiesel combustion; conventional fuels.
  DOI: 10.1504/IJHVS.2024.10068504
   
  
• Improvement ride and handling for a passenger van based on Olley criteria  
  by Ghasem Karimi, Masoud Masih-Tehrani, Hossein Nazemian 
  Abstract: Light commercial passenger vans are commonly used in urban transportation, prioritising passenger comfort and effective handling. To ensure a smooth ride, especially over uneven road surfaces, optimising the vans suspension is essential. Key parameters like transmissibility ratio, suspension travel, and vertical acceleration must be regulated to maintain comfort. The suspension system significantly impacts vehicle dynamics and passenger experience, so selecting the right spring stiffness and dynamic frequencies is vital. This study uses a quarter-car model to determine the primary spring rates based on leaf spring deflection and shock absorber forces. The front suspension coil spring is also analysed, considering the front axles characteristics. Optimising leaf spring rates based on Olley criteria minimises vibration isolation issues, ensuring better pitch and bounce frequencies. The positioning of front oscillation centers near the axles further enhances ride comfort and handling by improving suspension performance and minimising vibrations.
  Keywords: oscillation centre; ride comfort optimisation; ride–handling; van suspension design; vehicle dynamics; vibration control; bounce frequency.
  DOI: 10.1504/IJHVS.2024.10068935
   
  
• Analysing stability of an automated transit network vehicle at Y-junction based on multi-body dynamics simulation  
  by Seungwoon Park, Burford Furman, Ron Swenson, Andries Louw, Chul-Hee Lee 
  Abstract: Eco-friendly vehicles are being developed to reduce carbon emissions and address global warming. The solar powered automated rapid transit ascendant network (SPARTAN) Superway project at San Jos
  Keywords: ATN; automated transit network; curved guideway; multi-body dynamics simulation; vehicle dynamics; Y-junction.
  DOI: 10.1504/IJHVS.2024.10069050
   
  
• Safety analysis at railway level crossing  
  by Sharad Nigam, Divya Kumar 
  Abstract: A level crossing is a place where a track crosses the road. A level crossing is a point where frequent train-to-vehicle accidents occur, so trains are given more priority than road vehicles to pass from the level crossing To minimize the accidental risk and allow road vehicles to pass from the level crossing railway authority deployed some safety methods i e closure, merger, manning, and subway In this paper author includes a new safety method i e automatic level crossing gate as a 5th class to improve the decision tree prediction model performed on the dataset, and tries to conduct a comparative analysis of 4-class classification model and 5-class classification model Automatic level crossing as a safety method tries to solve multiple conflict of the safety decision as present in the dataset and improve the prediction model to provide suitable methods in the context of safety, facility, and cost.
  Keywords: level crossing; TVU; decision tree; entropy; information gain; automatic level crossing.
  DOI: 10.1504/IJHVS.2024.10069120
   
  
• Investigation of MPC path tracking control for articulated unmanned mining trucks in reversing environments of mining areas  
  by Qing Ye, KongJia Meng, Wang Ruochen, Zeyu Sun, Yao Zhang 
  Abstract: This paper introduces a trajectory tracking control algorithm for articulated autonomous mining trucks during reversing, utilizing Model Predictive Control (MPC) to address vehicle articulation challenges. A dynamic model with three degrees of freedom is established to analyze the reverse motion characteristics of these trucks, which forms the basis for developing a virtual steering angle mapping model tailored for reversing maneuvers. Subsequently, an MPC path tracking algorithm is proposed, emphasizing lateral stabilization control torque for both the tractor and the semi-trailer to ensure precise tracking control of the articulated system. Hardware-in-the-loop (HIL) testing, along with simulation and experimental results, demonstrates that the proposed control strategy effectively satisfies reversing control requirements. It also outperforms conventional preview control and PID control methods, further validating the efficacy of the algorithm.
  Keywords: MPC; unmanned mining trucks; reverse movement; HIL.
  DOI: 10.1504/IJHVS.2024.10069236
   
  
• Traffic accident analysis and development of accident prediction model for four-lane divided national highway  
  by Vinod Kumar, Sanjeev Kumar Suman 
  Abstract: This study analyses traffic incidents on divided four-lane national highways to identify trends and develop a novel accident prediction model. By applying the weighted severity index (WSI) method, the research highlights accident-prone areas (dark spots) based on both frequency and severity, guiding targeted safety interventions. The research employs a cutting-edge approach by integrating geometric and traffic parameters using the CGAN-EB methodology, a non-parametric Empirical Bayes method leveraging conditional generative adversarial networks (CGANs) that provide a dynamic and data-driven prediction model. The model, incorporating safety coefficients from the analytic hierarchy process (AHP), contributes to improved highway design and safety measures. MATLAB can be a powerful tool for conducting traffic accident analysis and developing accident prediction models for divided four-lane National Highways. Achieving a high accuracy of 98% in a predictive model for traffic accident analysis.
  Keywords: traffic; accident black spots; WSI; weighted severity index; Empirical Bayes; CGANs; conditional generative adversarial networks; AHP; analytic hierarchy process.
  DOI: 10.1504/IJHVS.2025.10069693
   
  
• Traffic anomaly detection with wild geese dwarf mongoose optimisation_DQNN  
  by M. Ahsan Shariff, C. Nelson Kennedy Babu 
  Abstract: SDN probe focuses more programmability and growth of control plane from starting. SDN is affected tremendously by DDoS attacks as SDN controller is a main portion that controls and manages all actions of network. Thus, controller breakage may direct to network collapse. Here, WGDMO_DQNN is devised for traffic flow detection in SDN. Firstly, SDN is simulated and then, packet transmission is conducted. Thereafter, traffic flow detection at data plane is conducted wherein feature extraction and traffic flow detection stages are carried out. The traffic flow detection is accomplished utilizing DQNN. If traffic flow is detected at individual switch, then control plane attack mechanism is done that consists of identifying, electing and composition stages. In composition stage, WGDMO is employed for swapping switches to underloaded controller. Moreover, WGDMO is an amalgamation of optimizations such as WGA with DMOA.
  Keywords: DQNN; deep quantum neural network; WGA; wild geese algorithm; DMOA; dwarf mongoose optimisation algorithm; SDN; software-defined networking; packet transmission.
  DOI: 10.1504/IJHVS.2024.10069794
   
  
• A hierarchical estimation algorithm for heavy-duty vehicle mass and road grade based on UKF and RLS  
  by Zhijun Ren, Baoan Ding, Fenggang Li, Xiaotian Zhang, Xinfa Xu 
  Abstract: Real-time estimation of vehicle mass and road grade is essential for intelligent vehicle control However, the joint estimation of vehicle mass and road grade is often constrained by data quality issues Addressing the reliability and prediction accuracy of existing estimation algorithms, this study proposes a hierarchical sequential estimation method tailored for heavy-duty vehicles In the first layer, vehicle mass is estimated within tens of seconds after the vehicle starts In the second layer, road grade is estimated based on the previously estimated vehicle mass Traditional algorithms based on longitudinal dynamics struggle under non-normal conditions such as braking and shifting, where these dynamics equations fail To address this, the improved method uses recursive least squares (RLS) and the Unscented Kalman Filter (UKF) under normal conditions During non-normal conditions, the vehicle mass estimation holds the value determined before the event, while the road grade is predicted using an ARIMA model based on historical grade data. Real-vehicle experiments show that the vehicle mass estimation error is less than 4.2%, and the road grade estimation achieves an RMSE of less than 0.2
  Keywords: vehicle mass; road grade; recursive least squares; unscented Kalman filter; longitudinal dynamics.
  DOI: 10.1504/IJHVS.2025.10070139
   
  
• Vibration tests on different electric buses and routes in the city of Gyr  
  by Péter Őri, István Lakatos 
  Abstract: In the vibration tests, 3-axis accelerometers were installed on different electric buses (BYD, IKARUS). One sensor was placed on the chassis and another on the body. During the measurements, 2 directions of 2 sensors were recorded using an oscilloscope. During the recording, the sensors were positioned identically for each measurement and backups were performed as routes. There were some conditions that were outside the 30 minute recording limit of the measurement system. In these cases, the routes were divided into sections that were the same for all measurements. On 5 routes, 10 different sections were recorded. We separated the different measurement channels in the measurements and then evaluated the RMS pulsation values of the vibrations in each case, as well as the maximum amplitude vibration of each section. In order to compare the different sections, we took the one with the highest amplitude in each case as 100% and compared the others to it. The comparison tables show that there can be a difference of up to 40% in vibration intensity between the lowest and the highest loaded section: In the vibration tests, 3-axis accelerometers were installed on different electric buses (BYD, IKARUS). One sensor was placed on the chassis and another on the body. During the measurements, 2 directions of 2 sensors were recorded using an oscilloscope. On 5 routes, 10 different sections were recorded. We evaluated the root mean square (RMS) pulsation values of the vibrations in each case, as well as the maximum amplitude vibration of each section. The comparison tables show that there can be a difference of up to 40% in vibration intensity between the lowest and the highest loaded section. The comparative data show that the effect of chassis damping is to reduce the body vibration to 1/6 of the chassis load.
  Keywords: vibration; NVH; Pico; bus; city; maintenance.
  DOI: 10.1504/IJHVS.2025.10070248
   
  
• Estimation of wheel-rail dynamic load in vicinity of track local defects using on-board train acceleration measurements  
  by Mohammad Hassan Esmaeili, Javad Sadeghi, S. Masoud Nasr Azadani, Saeed Motamedi 
  Abstract: This research addresses the limitations of the current wheel-rail dynamic load (WRDL) measurement techniques by considering the effect of track local defects. A new model is developed based on measurement of train accelerations passing over five common track local defects. The defects are step up, step down, corrugation, negative pulse joint and pulse joint. A train is instrumented on the bogie frame, axle and car body. On-board accelerations are then measured. Kalman filter was used to obtain WRDL from recorded accelerations. The effectiveness of the new model was shown, compering its results with corresponding values of multi-body dynamic models (MBD) currently used in WRDL calculations. Results showed that the new model is able to consider the effect of track local defects in WRDL calculations. As the new method does not require direct access to the tack, it is faster and easy to use compared with existing approaches.
  Keywords: track local defects; wheel rail dynamic load; rail corrugation; track joint; track monitoring.
  DOI: 10.1504/IJHVS.2025.10070439
   
  
• Hybrid approach for energy management strategy and energy management strategy for plug-in hybrid electric vehicles using GTO-DRN approach  
  by A. Shri Vindhya, L. Rama Parvathy, K. Paul Joshua, Chinthalacheruvu Venkata Krishna Reddy 
  Abstract: This manuscript presents a hybrid approach to energy management strategy for plug-in-hybrid electric vehicles. The proposed hybrid technique is the joined execution of the Gorilla Troops Optimization Algorithm (GTO) and Dilated Residual Convolutional Neural Networks (DRN). Hence it is named as GTO-DRN system. The proposed method's main objective is to minimize the energy loss and reduces the cost. The GTO method is utilized to optimize the battery by developing an interaction among the independent operating parameters, voltage and current, using response surface methodology. The GTO method is utilized to optimize to charge or discharge the battery, and other control decisions to minimize energy consumption or emissions. The DRN technique is used to model and predict the vehicle's power demand.
  Keywords: PHEV; plug-in-hybrid electric vehicles; charge; Gorilla Troops Optimisation; DRN; dilated residual convolutional neural networks; battery; energy loss.
  DOI: 10.1504/IJHVS.2025.10070574
   
  
• A wheel polygon recognition model based on improved statistical geometric feature and support vector machine  
  by Chengdong Wu, Huiming Yao 
  Abstract: Addressing the problems of long processing time, low accuracy and poor real-time performance in wheel polygon recognition, a new recognition model is proposed. The vertical vibration data of the axle box is converted from 1D to 2D and normalized into gray degree images. Subsequently, the gray degree image is decomposed into binary images through bit plane decomposition, and an improved statistical geometric feature (ISGF) method is utilized to extract textural features from the binary image. Finally, these features are used for the training and classification process of support vector machine (SVM) to diagnose and recognize wheel polygon faults. Through dynamics simulation verification and double-wheel test bench experiments, the results show that the model organically combines the fault data with the image recognition method, effectively restrains the noise interference in vibration data, and significantly improves the recognition accuracy, reduces recognition time and demonstrates strong generalization performance.
  Keywords: Wheel polygon; Gray degree image; Improved statistical geometric feature; Texture analysis; Support vector machine.
  
  
• A global path planning manner for unmanned ground vehicles in off-road situations based on terrain data  
  by Xu Li, Sen Liu, Jianchun Wang 
  Abstract: To address safe path planning for unmanned ground vehicles (UGVs) in off-road environments, this study proposes a global terrain-datadriven method integrating risk assessment and kinematic constraints. A 3D grid map is established to quantify terrain risks (e.g., slopes, obstacles), while vehicle passability and speed constraints define traversable regions. A comprehensive cost model evaluates navigation risks, energy consumption, and motion stability. An enhanced A* algorithm, incorporating a multi-objective cost function, generates optimal paths, further smoothed via B
  Keywords: unmanned ground vehicle; off-road scenario risk characterization; digital elevation model; integrated cost function; global path planning.
  DOI: 10.1504/IJHVS.2025.10070808
   
  
• Energy consumption and makespan by considering set up and transportation time: a hybrid AHO-MARR technique  
  by Kilari Jyothi, R.B. Dubey 
  Abstract: The job shop scheduling problem (JSP) with sequence-dependent set-up and transportation times is addressed using a hybrid approach in this manuscript. Using the median-average round robin (MARR) scheduling method and the archerfish hunting optimizer (AHO),the procedure is carried out. As a result, it is called the AHO-MARR technique. The energy-efficient JSP is analyzed in relation to the requirements of manufacturing, and the effects of the two times factors-namely, setup and transportation times-on the production and energy objectives are looked at. High-quality initial solutions are being produced by the intended AHO population.AHO is used to calculate the total processing time and energy used by every job on each machine. AHO method depends on the critical-path is intended to reduce the overall setup time while maintaining the machine assignment system. When compared to existing methods like the Salp swarm algorithm(SSA), Wild horse optimizer(WHO),& heap-based optimizer(HBO), the AHO-MARR is lower.
  Keywords: energy consumption; setup; transportation time; makespan; efficient; job shop scheduling; archer fish hunting optimisation; median average round robin algorithm.
  DOI: 10.1504/IJHVS.2025.10071177
   
  
• Investigation on the influence of augmented rail geometries on rail gun design parameters using MSNN-PFOA approach  
  by J. Lydia, R. Karpagam, R. Murugan, S. Leones Sherwin Vimalraj 
  Abstract: In this manuscript proposes, augmented rail geometries on rail gun design. The proposed method is the combined execution of Multiresolution Sinusoidal Neural Networks (MSNN) and Piranha Foraging Optimization Algorithm(PFOA). Hence it is named the MSNN-PFOA approach. Heat minimization in electromagnetic rail guns is the main goal of the hybrid method MSNN-PFOA technique. The proposed MSNN algorithm is utilized to predict the extrapolation of electromagnetic force. Consequently, the Piranha Foraging Optimization Algorithm is proposed to optimize the MSNN weight parameters. The proposed approach is evaluated in MATLAB and compared with existing methods like ANN, PSO and DBN-DNN. The basis error value of the proposed approach is 10% less than that of the existing methods. The ANN error value of the proposed method is more than that of the current one, which is 20%. The findings show that the efficiency of the proposed strategy is less than that of the existing approaches.
  Keywords: armature velocity; electromagnetic force; energy; heat reduction; inductance gradient; rail gun and temperature.
  DOI: 10.1504/IJHVS.2025.10071232
   
  
• Path tracking and stability control of articulated vehicle based on multi-point preview  
  by Xu Li, Hongzheng Song, Jianchun Wang 
  Abstract: To address the issue of poor path tracking accuracy and vehicle stability of automated articulated vehicle under emergency track changing conditions, a speed-based variable weight multi-point preview path tracking, stability decision making and optimal control strategy is proposed. Considering the dual effects of vehicle speed on preview deviation and the articulated relationship between tractor and semi-trailer on the trajectory, a three-point preview driver model based on the adaptive weight of vehicle speed is designed. The phase plane method and lateral load transfer ratio are used to determine the articulated vehicle’s stability.A direct yaw moment by differential braking stability control strategy for articulated vehicle based on model predictive control (MPC) is designed to reduce the yaw rate, mass center side slip angle and lateral load transfer ratio of tractor as well as semi-trailer during the path tracking of articulated vehicle to improve the vehicle running stability.
  Keywords: articulated vehicle; three-point preview; MPC; path tracking; stability control.
  DOI: 10.1504/IJHVS.2025.10071269
   
  
• Enhancing autonomous vehicle security in intelligent transportation systems through quantum computing and optimisation based federated learning  
  by S. Sellakumar, Kavin FrancisXavier, K. Pradeepa, N. Bharathiraja 
  Abstract: Autonomous vehicles (AVs) and smart transportation systems have the ability to change modern society, but confidentiality and security must be guaranteed. The ecosystems of AVs employ machine learning models, which are susceptible to adversarial attacks during training by federated learning and data poisoning. Hyperparameters are vital for creating a resilient federated learning model. The proposed system addresses these issues by employing adversarial attacks to automatically adjust hyperparameters. The methodology consists of two phases: updating learning rate hyperparameters during global and local epochs, and designing a framework to defend against attacks. The proposed system is evaluated using two benchmark datasets, Fashion-Modified International Standards for Telecommunication (MNIST) and MNIST, enhancing AV security through real-time hyperparameter optimisation using quantum computing. This proposed system improves resistance to external attacks while safeguarding data privacy.
  Keywords: autonomous vehicles; intelligent transportation systems; quantum computing; federated learning; adversarial attacks; data privacy.
  DOI: 10.1504/IJHVS.2025.10071425
   
  
• AAM-YOLO: a novel articulated angle observer of towbarless aircraft towing systems  
  by Hengjia Zhu, Zishuo Xu, Chao Wang, JiYuan Liu, Wei Zhang 
  Abstract: The measurement of the articulated angle is the key to achieving the automated towing operation of the towbarless aircraft towing system (TLATS). Due to the structural characteristics of the towing system, it is challenging to measure the articulated angle accurately using contact methods or dynamic models. To tackle these problems, this article proposes a novel observer called articulated angle measured YOLO (AAM-YOLO), which features a plug-and-play articulated angle measured module (AAM). This module uses the segmentation mask generated by the output to determine the articulated angle of the TLATS. The effectiveness of the AAM-YOLO is validated on a self-built AN-SEG dataset. The validation results show that the absolute errors of the measured results are all less than 1.5
  Keywords: TLATS; towbarless aircraft towing vehicles; articulated angle measurement; state estimation; YOLO.
  DOI: 10.1504/IJHVS.2025.10071582