Vehicles, Volume 7, Issue 4 (December 2025) – 62 articles

Unmanned Aerial Vehicles (UAVs) are promising nodes for Integrated Sensing and Communication (ISAC), but accurate Direction-of-Arrival (DoA) estimation on a small airframe is challenged by platform loading, motion, attitude, and multipath. Traditionally, DoA algorithms have been developed and evaluated for stationary, ground-based (or otherwise mechanically stable) antenna arrays. Extending them to UAVs violates these assumptions. This work designs a six-element Uniform Circular Array (UCA) at 2.4 GHz (radius $\approx 0.5\lambda$) for a quadrotor and introduces a Pose-Aware MUSIC (MUltiple SIgnal Classification) estimator for DoA. The novelty is a MUSIC formulation that (i) applies pose correction using the drone’s instantaneous roll–pitch–yaw (pose correction) and (ii) applies a Doppler correction that accounts for platform velocity. Performance is assessed using data synthesized from embedded-element patterns obtained by electromagnetic characterization of the installed array, with additional channel/hardware effects modeled in post-processing (Rician LOS/NLOS mixing, mutual coupling, per-element gain/phase errors, and element–position jitter). Results with the six-element UCA show that pose and Doppler compensation preserve high-resolution DoA estimates and reduce bias under realistic flight and platform conditions while also revealing how coupling and jitter set practical error floors. The contribution is a practical PA-MUSIC approach for UAV ISAC, combining UCA design with motion-aware signal processing, and an evaluation that quantifies accuracy and offers clear guidance for calibration and field deployment in GNSS-denied scenarios. The results show that, across 0–25 dB SNR, the proposed hybrid DoA estimator achieves <$0.5^{\circ }$ RMSE in azimuth and elevation for ideal conditions and ≈$5^{\circ }$–$6^{\circ }$ RMSE when full platform coupling is considered, demonstrating robust performance for UAV ISAC tracking. Full article

This paper presents a methodological framework for inferring energy-related operating states of plug-in hybrid electric vehicles (PHEVs) under conditions of limited and incomplete on-board diagnostic (OBD) data. The proposed approach is illustrated using a single short real-world urban trip recorded for one PHEV operating in electric mode. Unsupervised clustering based on k-means is applied in progressively expanded state spaces (3D–5D) to decompose the driving process into physically interpretable operating states, despite the absence of direct measurements of key variables such as regenerative braking power. Cluster validity indices, per-cluster silhouette values, temporal segmentation, and robustness checks are employed to support the interpretability and internal consistency of the results. The study demonstrates that even a single, non-representative OBD time series contains sufficient internal structure to recover meaningful energy-related information when appropriate state-space decomposition is applied. While no statistical generalization is intended, the results highlight the potential of the proposed framework for analyzing real-world vehicle operation under constrained data availability. Full article

A promising approach to advancing sustainable urban mobility is the increased use of light electric vehicles, such as e-cycles and their cargo-carrying variants: e-cargo cycles. These micromobility vehicles fall between e-cycles and conventional vehicles in terms of transport capacity, range, and cost. A key advantage of e-cargo cycles over their non-electrified counterparts is the electric powertrain, which enables them to carry heavier payloads, travel longer distances, and reduce driver fatigue. Since the primary use of e-cargo cycles is urban parchment deliveries, trip efficiency plays a critical role in their effectiveness within urban logistics. This efficiency is influenced by factors such as travel distance, traffic density, and the weight and volume of the delivery payload. While higher delivery capacity generally enhances efficiency, studies have shown that as the drop size increases, the efficiency of e-cargo cycle delivery trips tends to decline. A practical way to address this limitation is the use of cargo attachments, such as trailers. These micromobility solutions are already widely implemented globally and significantly enhance transport capacity. This paper reports the process of designing and testing the control algorithm of an electrical system for an experimental attachment demonstrator that can be used to convert most cycle vehicles into cargo variants. The system integrates two 250 W BLDC hub motors, two 576 Wh lithium-ion batteries, dual load-cell sensing in the coupling element, and an STM32-based controller to provide independent propulsion and synchronization with the leading cycle. The force-based control strategy enables automatic adaptation to varying payloads typically encountered in urban logistics, which is supported by the variable storage volume capable of transporting payloads of up to 200 kg. Full article

Railroad-highway grade crossings (RHGCs) are critical points of conflict between roadway and rail systems, contributing to over 2000 crashes and 250 fatalities annually in the United States. This study applied machine learning methods (ML) techniques to model and predict crash frequency at RHGCs, using a comprehensive dataset from the Federal Railroad Administration (FRA) and Tennessee Department of Transportation (TDOT). The dataset included 807 validated crossings, incorporating roadway geometry, traffic volumes, rail characteristics, and control features. Five ML models—Random Forest, XGBoost, PSO-Elastic Net, Transformer-CNN, and Autoencoder-MLP—were developed and compared to a traditional Negative Binomial (NB) regression model. Results showed that ML models significantly outperformed the NB model in predictive accuracy, with the Transformer-CNN achieving the lowest Mean Squared Error (21.4) and Mean Absolute Error (3.2). Feature importance analysis using SHAP values consistently identified Annual Average Daily Traffic (AADT), Truck Traffic Percentage, and Number of Lanes as the most influential predictors, findings that were underrepresented or statistically insignificant in the NB model. Notably, the NB model failed to detect the nonlinear relationships and interaction effects that ML algorithms captured effectively. While only three variables were statistically significant in the NB model, ML models revealed a broader spectrum of critical crash determinants, offering deeper interpretability and higher sensitivity. These findings emphasize the superiority of machine learning approaches in modeling RHGC safety and highlight their potential to support data-driven interventions and policy decisions for reducing crash risks at grade crossings. Full article

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO₂-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NO_x, VOCs, SO_x, PM_2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. Full article

The review has explored the application of Geographic Information Systems (GIS) in evaluating road traffic crashes, stressing its role in identifying crash spatial patterns and hotspots. GIS offers a framework for integrating spatial and non-spatial data, allowing scholars and planners to visualize crash-prone areas and understand their distribution. A total of 77 research articles from the publication period of 2010–2025 were included for final reviews. A Systematic Reviews and Meta-Analyses (PRISMA) approach is followed to provide well-structured, transparent, and standardized information on articles. The intention is to assess how different GIS techniques contribute to road safety analysis and to the development of effective intervention strategies. The review focused particularly on four key GIS-based spatial analysis methods: Kernel Density Estimation (KDE), Network KDE, Moran’s I (Global and Local), and Getis-Ord Gi*. Among these, KDE and Moran’s I were the most frequently adopted techniques, covering about 31.17% and 23.38% of reviewed articles, respectively. These techniques are essential for identifying statistically significant clusters and crash concentration. Despite their promising results, the studies also reveal limitations, including inconsistent data quality, high computational demands, and limited use of variables such as road geometry characteristics. Although GIS is an effective tool for planning and analyzing road safety, these deficiencies might be addressed by future studies that advance the use of real-time spatial analytics and incorporate more diversified information. Overall, the review has reinforced the critical role of GIS in improving traffic safety through real-time data-driven interventions. Full article

This work investigates how the vehicle-to-tube suspension gap governs compressible flow physics and operating margins in Hyperloop-class transport at 10 kPa. To our knowledge, this is the first study to apply adjoint aerodynamic optimization to mitigate gap-induced choking and shock formation in a full pod–tube configuration. Using a steady, pressure-based Reynolds-averaged Navier-Stokes (RANS) framework with the GEnerlaized K-Omega (GEKO) turbulence model, a simulation for the cruise conditions was performed at M = 0.5–0.7 with a mesh-verified analysis (medium grid within 0.59% of fine) to quantify gap effects on forces and wave propagation. For small gaps, the baseline pod triggers oblique shocks and a near-Kantrowitz condition with elevated drag and lift. An adjoint shape update—primarily refining the aft geometry under a thrust-equilibrium constraint—achieves 27.5% drag reduction, delays the onset of choking by ~70%, and reduces the critical gap from d/D ≈ 0.025 to ≈0.008 at M = 0.7. The optimized configuration restores a largely subcritical passage, suppressing normal-shock formation and improving gap tolerance. Because propulsive power at fixed cruise scales with drag, these aerodynamic gains directly translate into operating-power reductions while enabling smaller gaps that can relax tube-diameter and suspension mass requirements. The results provide a gap-aware optimization pathway for Hyperloop pods and a compact design rule-of-thumb to avoid choking while minimizing power. Full article

Urbanisation and the increasing concentration of populations in cities present significant challenges for achieving sustainable mobility and advancing the energy transition. Private vehicles, particularly those powered by internal combustion engines, remain the primary contributors to urban air pollution and greenhouse gas emissions. This situation has prompted the European Union to accelerate transport decarbonisation through comprehensive policy frameworks, notably the “Fit for 55” package, which aims to reduce net greenhouse gas emissions by 55% by 2030. These measures underscore the urgency of shifting towards low-emission transport modes. In this context, electric vehicles (EVs) play a key role in supporting Sustainable Development Goal 7 by promoting cleaner and more efficient transport solutions, and Sustainable Development Goal 11, aimed at creating more sustainable and liveable cities. Despite growing policy attention, the adoption of EVs remains constrained by users’ concerns regarding purchase costs, driving range, and the availability of charging infrastructure, as shown by the findings of this study. In this context, this study explores the determinants of EV adoption in Italy by employing a combined methodological approach that integrates a stated preference (SP) survey with discrete choice modelling. The analysis aims to quantify the influence of economic, technical, and infrastructural factors on users’ willingness to switch to EVs, providing insights for policymakers and industry stakeholders to design effective strategies for accelerating the transition toward the sustainable mobility. Full article

The design-related behaviour of structural dynamics for electric-assisted bicycle (e-bike) drive units significantly influences the mechanical system—e.g., vibrations and durability, stresses and loads, or functionality and comfort. Identifying the underlying mechanical principles opens up optimisation possibilities, such as improved e-bike design and user experience. Despite its potential to enhance the system, the structural dynamics of the drive unit have received little research attention to date. To improve the current situation, this paper uses a flexible multibody modelling approach, enabling new insights through virtual trials and analyses that are not feasible solely from measurements. The incorporation of the drive unit’s system-level topology regarding mass, moment of inertia, stiffness, and damping enables the analysis of critical system states. Experiments accompany the analysis and validate the model by demonstrating a load-dependent shift of the first torsional mode around 35 Hz to 60 Hz, capturing comparable resonance frequency ranges up to 6 kHz, and yielding qualitatively consistent peak positions in both steady-state and ramp-up analyses (mean deviations of 0.03% and 0.06%, respectively). Theoretical considerations of the multibody system highlight the effects, and the stated modelling restrictions make the method’s limitations transparent. The key findings are that the drive unit’s structural dynamic behaviour exhibits solely one structural mode until 0.5 kHz, and further 27 modes up to 10 kHz, solely originating due to the multibody arrangement of the drivetrain. These modes are also load-dependent and lead to resonances during operation. In summary, the approach enables engineers, for the first time, to significantly improve the structural dynamics of the e-bike drive unit using a full-scale system model. Full article

The automotive industry is currently undergoing significant transformations driven by challenges such as fierce competition, supply chain disruptions, and stringent legislative regulations aimed at reducing pollutant emissions. The research employs a combination of theoretical analysis and numerical modeling to investigate the manufacturing processes of stamped automotive components. Data collection methods include experimental testing of materials, LS-DYNA simulations, and non-contact scanning for dimensional analysis. The study also utilizes a workflow diagram to illustrate the various phases involved in the design and validation of automotive assemblies. The findings detail the critical role of digital transformation in the automotive industry, particularly in enhancing the accuracy and reliability of manufacturing processes. Implementing digital twins improves product quality and reduces product development time. The experimental results were compared with simulation data, and a good correlation was identified, showing, for the numerical model with complete history (thickness and stress), a difference of $1.6\%$. Furthermore, to simplify the process of developing the numerical models for the initial iterations, a scale factor of $~1.1$ is proposed for the testing load. This factor is not limited to the current design, as the manufacturing stages are similar for this range of products. Full article

Some European Heavy Duty (HD) vehicle manufacturers have adopted Open Crankcase Ventilation (OCV) systems to improve reliability and performance. The emission compliance of HD vehicles both during certification and In-Service Conformity (ISC) testing need to also account for the crankcase ventilation. Despite that, the contribution of crankcase emissions to the overall emissions profile of modern trucks remains underexplored. This study experimentally characterizes the crankcase emissions of a Euro VI Step E HD truck equipped with an OCV system under controlled conditions on a chassis dynamometer. Emissions were measured over the World Harmonized Vehicle Cycle (WHVC) and an ISC-compliant driving cycle at two test cell temperatures. The results indicate that crankcase emissions account for up to 4% and 8% of the current regulatory limits for nitrogen oxides (NO_x) and 23 nm solid particle number (SPN₂₃), respectively. The tightening of NO_x limits under Euro 7 regulations would increase these contributions to approximately 11%. SPN₁₀ crankcase emissions were found to be on the order of 10¹¹ (11% of the Euro 7 limit). Real-time SPN₁₀ and SPN₂₃ measurements revealed that the fraction of nanosized particles increases significantly during cold start, suggesting increased oil combustion within the cylinder. These findings highlight the need to refine crankcase emissions measurement procedures within regulatory frameworks. A systematic investigation of measurement setups and ageing effects, taking into account variations in OCV system designs and piston ring wear, is essential to determine whether characterization during certification is sufficient or if ISC testing throughout the vehicle’s useful life will be required. Full article

This study presents a predictive maintenance framework for hybrid electric vehicles (HEVs) based on emissions behaviour under laboratory-simulated driving conditions. Vehicle speed, road gradient, and ambient temperature were selected as the principal input variables affecting emission levels. Using simulated datasets, three machine learning model, specifically Linear Regression, Multilayer Perceptron (MLP), as well as Random Forest, were trained and evaluated. Within that set, the Random Forest model demonstrated the best performance, achieving an R² score of 0.79, Mean Absolute Error (MAE) of 12.57 g/km, and root mean square error (RMSE) of 15.4 g/km, significantly outperforming both Linear Regression and MLP. A MATLAB-based graphical interface was developed to allow real-time classification of emission severity using defined thresholds (Normal ≤ 150 g/km, Warning ≤ 220 g/km, Critical > 220 g/km) and to provide automatic maintenance recommendations derived from the predicted emissions. Scenario-based validation confirmed the system’s ability to detect emission anomalies, which might function as early indicators of mechanical degradation when interpreted relative to operating conditions. The proposed framework, developed using laboratory-simulated datasets, provides a practical, interpretable, and accurate solution for emissions-based predictive maintenance. Although the results demonstrate feasibility, the framework should be further confirmed with real-world on-road data prior to large-scale use. Full article

To improve recognition accuracy, convergence speed, and numerical stability in estimating the road adhesion coefficient for distributed-drive electric vehicles, a nonlinear seven-degree-of-freedom vehicle dynamics model was developed based on a modified Dugoff tire model. Using the Unscented Kalman Filter (UKF) as a foundation, a Square-Root Unscented Kalman Filter (SR-UKF) algorithm was derived through covariance-square-root processing and Singular Value Decomposition (SVD). A co-simulation platform was built with CarSim and Simulink, and a vehicle speed-following model was developed for simulation analysis. The results show that the SR-UKF algorithm for road identification consistently maintains matrix positive definiteness, ensures numerical stability, speeds up convergence, and fully utilizes measurement information. Simulations under various road conditions (high-adhesion, low-adhesion, split-μ, and opposite-μ) and driving scenarios demonstrate that, compared to the traditional UKF, the SR-UKF converges faster and provides higher estimation accuracy, enabling real-time, accurate estimation of the road adhesion coefficient across multiple scenarios. Final results confirm that the SR-UKF exhibits excellent estimation accuracy and robustness on low-adhesion surfaces, confirming its superiority under high-risk conditions. This offers a dependable basis for improving vehicle active safety. Full article

Traffic accidents pose unpredictable and severe social and economic challenges. Rapid and accurate accident detection, along with reliable severity classification, is essential for timely emergency response and improved road safety. This study proposes DSGF-YOLO, an enhanced deep learning framework based on the YOLOv13 architecture, developed for automated road accident detection and severity classification. The proposed methodology integrates two novel components: the DS-C3K2-FasterNet-Block module, which enhances local feature extraction and computational efficiency, and the Grouped Channel-Wise Self-Attention (G-CSA) module, which strengthens global context modeling and small-object perception. Comprehensive experiments on a diverse traffic accident dataset validate the effectiveness of the proposed framework. The results show that DSGF-YOLO achieves higher precision, recall, and mean average precision than state-of-the-art models such as Faster R-CNN, DETR, and other YOLO variants, while maintaining real-time performance. These findings highlight its potential for intelligent transportation systems and real-world accident monitoring applications. Full article

This paper presents an experimental study of the Automatic Emergency Braking (AEB) system, focusing on three essential testing phases: verifying the match between calculated and actual brake actuator operation time, validating the forecasted vs. real-time stabilized deceleration onset duration, and comparing the theoretically computed braking distance derived from mathematical models with its actual measurement. Standard instrumentation coupled with an original test procedure was utilized during the experiments. A full-scale experimental campaign was conducted on a specialized proving ground, thus substantiating the validity and robustness of the computational models used for assessing the AEB system parameters. The empirical outcomes confirmed that current-generation AEB systems offer dependable predictions regarding braking dynamics and exhibit prompt responsiveness to imminent collisions. However, it should be noted that variations in road conditions, driver behavior, and sensor precision may affect their performance. Consequently, additional efforts aimed at optimizing existing AEB solutions are required to minimize potential errors and enhance overall reliability. Finally, the significance of complying with design specifications and continuously upgrading AEB systems to meet evolving road safety standards is emphasized. Full article

This study confronts the significant challenges inherent in Traffic State Estimation (TSE) for rural arterial networks, where sparse sensor coverage and complex, dynamic traffic flows complicate effective management and safety assurance. Traditional TSE methodologies, often dependent on single-source data streams, fail to accurately model the intricate spatiotemporal dependencies present in such environments. This fundamental limitation precipitates critical safety hazards, including pervasive over speeding and dangerous queue spillback phenomena at intersections. To address these deficiencies, we introduce a novel hybrid intelligence framework that synergistically combines a Graph Attention Temporal Convolutional Network (GAT-TCN) with advanced Kalman Filter variants, specifically the Extended, Unscented, and Sliding Window Kalman Filters. The GAT-TCN component is engineered to excel at learning complex, non-linear correlations across both space and time through multi-source data fusion. Empirical validation conducted on a real-world rural toll corridor demonstrates that our proposed model achieves a statistically significant superiority over conventional benchmarks, as rigorously quantified by substantial reductions in both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Beyond mere predictive accuracy, the framework delivers transformative safety enhancements by facilitating the proactive identification of hazardous events, enabling earlier detection of over speeding and queue spillback compared to existing methods. Consequently, this research provides a scalable and robust framework for proactive rural traffic management, fundamentally shifting the paradigm from achieving incremental predictive improvements to generating decisive, safety-actionable insights for infrastructure operators. Full article

The air–ground integrated Internet of Vehicles (IoV), which incorporates unmanned aerial vehicles (UAVs), is a key component of a three-dimensional intelligent transportation system. Task offloading is crucial to improving the overall efficiency of the IoV. However, blackhole attacks and false-feedback attacks pose significant challenges to achieving secure and efficient offloading for heavily loaded roadside units (RSUs). To address this issue, this paper proposes a reputation-aware, multi-objective task offloading method. First, we define a set of multi-dimensional Quality of Service (QoS) metrics and combine K-means clustering with a lightweight Proximal Policy Optimization variant (Light-PPO) to realize fine-grained classification of offloading data packets. Second, we develop reputation assessment models for heterogeneous entities—RSUs, vehicles, and UAVs—to quantify node trustworthiness; at the same time, we formulate the RSU task offloading problem as a multi-objective optimization problem and employ the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to find optimal offloading strategies. Simulation results show that, under blackhole and false-feedback attack scenarios, the proposed method effectively improves task completion rate and substantially reduces task latency and energy consumption, achieving secure and efficient task offloading. Full article

Collisions with fallen pedestrians pose a lethal challenge to current advanced driver-assistance systems. This paper introduces and quantitatively validates the Advanced Falling Object Detection System (AFODS), a novel safety framework designed to mitigate this risk. AFODS architecturally integrates long-wave infrared, near-infrared stereo and ultrasonic sensors, processed through a novel artificial intelligence pipeline that combines YOLOv7-Tiny for object detection with a recurrent neural network for proactive threat assessment, thereby enabling the system to predict falls before they are complete. In a rigorous controlled study using simulated adverse conditions, AFODS achieved a 98.2% detection rate at night, a condition where standard systems fail. This paper details the system’s ISO 26262-aligned architecture and validation results, proposing a framework for a new benchmark in active vehicle safety, demonstrated under controlled test conditions. Full article

The straightness of rail joints is one of the critical factors affecting passenger comfort in high-speed railways, and investigating its influence on the dynamic performance of the vehicle–track system and riding comfort is of great significance. In this study, long-term field measurements were conducted at a turnout joint of a newly constructed high-speed railway in China, combined with multibody dynamics simulations, to systematically analyze the long-term evolution of rail joint straightness under various conditions, including pre- and post-grinding, joint commissioning, official operation, and extreme weather. Based on normalized data processing, the root mean square (RMS) index of joint straightness was extracted for feature quantification. Together with vertical acceleration and the Sperling index obtained from vehicle–track coupled dynamics simulations, a quantitative relationship between straightness and comfort was established. The results indicate that the cubic polynomial fitting method can effectively characterize the nonlinear mapping between the RMS of joint straightness and the Sperling index, further revealing a critical threshold at approximately 0.4 mm RMS beyond which vehicle running stability deteriorates and ride comfort significantly worsens. This study provides a reliable theoretical basis and engineering reference for the evaluation of rail joint quality and the optimization of maintenance strategies. Full article

The optimization of clutch engagement strategies is of great significance for improving vehicle power performance, fuel economy, and driving comfort. Traditional control strategies are difficult to adapt to complex working conditions and lack coordinated optimization of fuel and clutch. This paper proposes a multi-objective optimization method for clutch engagement strategies based on the Deep Deterministic Policy Gradient (DDPG) algorithm. A simulation environment is constructed, which includes a vehicle longitudinal dynamics model, clutch state switching logic, and a reinforcement learning agent. A multi-dimensional state space and action space are designed, and a composite reward function combining power performance, fuel economy, and comfort is developed to achieve multi-objective optimization of the fuel–clutch coordination curve. Experimental results show that the optimized engagement strategy significantly reduces sliding friction power (by 94.07%), power interruption speed (by 8.75%), and jerk (with a maximum reduction of 35.6%), while the average fuel consumption per distance is reduced by 0.39%. Through weight sensitivity analysis, it is found that when the weight of fuel economy is 0.3 and the weight of power performance is 0.5 (Scheme P5E3), the optimal balance among multiple objectives can be achieved. This study provides a new theoretical framework and engineering practice reference for the intelligent control of clutches. Full article

As typical dynamic loads, electric vehicles (EVs) introduce significant uncertainty into distribution network operations due to the randomness of their travel behavior and charging demand. To achieve precise spatiotemporal forecasting of charging loads, this paper constructs a multi-dimensional transportation network model that accounts for dynamic road impedance factors and introduces a unit-distance energy consumption calculation method based on road impedance. By integrating the division of urban multifunctional zones and differentiated state-of-charge (SOC) threshold distributions across various EV types, a mapping model between travel chains and charging behaviors is established. Subsequently, large-scale travel and charging events are generated using an origin–destination (OD) probability matrix and Monte Carlo sampling to derive the spatiotemporal distribution of regional EV charging loads. Simulation results for a representative city in southwest China show that the predicted charging loads exhibit a dual-peak pattern, with significant differences across regions and vehicle types, and align well with observed load trends, validating the effectiveness and engineering applicability of the proposed method. Full article

This study investigates drivers’ visual attention strategies during naturalistic urban driving using mobile eye-tracking (Pupil Labs Neon). A sample of experienced drivers participated in a realistic traffic scenario to examine fixation behaviour under varying traffic conditions. Non-parametric analyses revealed substantial variability in fixation behaviour attributable to driver identity (H(9) = 286.06, p = 2.35 × 10⁻⁵⁶), stimulus relevance (H(7) = 182.64, p = 5.40 × 10⁻³⁶), and traffic density (H(4) = 76.49, p = 9.64 × 10⁻¹⁶). Vehicles and pedestrians elicited significantly longer fixations than lower-salience categories, reflecting adaptive allocation of visual attention to behaviourally critical elements of the scene. Compared with the fixed-rule method, which produced inflated anomaly rates of 7.23–14.84% (mean 12.06 ± 2.71%), the DSGP algorithm yielded substantially lower and more stable rates of 1.62–3.33% (mean 2.48 ± 0.53%). The fixed-rule approach over-classified anomalies by approximately 4–6×, whereas DSGP more accurately distinguished contextually appropriate fixations from genuine attentional deviations. These findings demonstrate that fixation behaviour in driving is strongly shaped by individual traits and environmental context, and that driver-specific modelling substantially improves the reliability of attention monitoring. Therefore DSGP framework offers a robust, personalised alternative evaluated at the proof-of-concept level to fixed thresholds and represents a promising direction for enhancing driver-state assessment in future ADAS. Full article

Traditional low-cost DC drives, such as Buck converter-fed DC drives, do not take into consideration the power quality requirements regarding the total harmonic distortion (THD) and the input power factor (PF). This paper proposes a high-performance one-quadrant DC drive based on the ultra-sparse matrix rectifier (USMR). The scheme is suitable for single-quadrant applications such as DC pumping systems. The proposed system leverages the advantages of the USMR, such as the accomplishment of the IEEE standards requirements related to harmonic limits and distortions of the AC currents and operation at (or near) unity PF. Two pulse width modulation (PWM) techniques were investigated: the hysteresis current controller with a tolerance band and the triangular carrier-based PWM modulator. The system was studied under different operating conditions. The obtained results demonstrate the high performance of the USMR system with both types of PWM techniques. A comparative study with the one-quadrant Buck converter-based DC drive was conducted. The USMR-based DC drive outperforms the conventional scheme in power quality issues. The quantitative assessment proves the validity and suitability of the USMR for developing high-performance DC drives for single-quadrant applications. Full article

Stable attitude control of unmanned or autonomous operations of vehicles moving in three spatial dimensions is essential for safe and reliable operations. Rigid body attitude control is inherently a nonlinear control problem, as the Lie group of rigid body rotations is a compact manifold and not a linear (vector) space. Prior research has shown that the largest possible domain of convergence is provided by smooth attitude feedback control laws are obtained using a Morse function on $\mathrm{SO}\left(3\right)$ as a measure of the attitude stabilization or tracking error. A polar Morse function on $\mathrm{SO}\left(3\right)$ has four critical points, which precludes the possibility of global convergence of the attitude state. When used as part of a Lyapunov function on the state space (the tangent bundle $\mathrm{TSO}\left(3\right)$) of attitude and angular velocity, it gives a globally continuous state-dependent feedback control scheme with the minimum of the Morse function as the almost globally asymptotically stable (AGAS) attitude state. In this work, we explore the use of explicitly time-varying gains for Morse functions for rigid body attitude control. This strategy leads to discrete switching of the indices of the three non-minimum critical points that correspond to the unstable equilibria of the feedback system. The resulting time-varying feedback controller is proved to be AGAS, with the additional desirable property that the time-varying gains destabilize the (locally) stable manifolds of these unstable equilibria. Numerical simulations of the feedback system with appropriate time-varying gains show that a trajectory starting from an initial state close to the stable manifold of an unstable equilibrium, converges to the desired stable equilibrium faster than the corresponding feedback system with constant gains. Full article

Traffic congestion continues to pose a significant challenge to contemporary urban transportation systems, exerting substantial effects on economic productivity, environmental sustainability, and the overall quality of life. This systematic literature review thoroughly explores the development of traffic congestion forecasting methodologies from 2014 to 2024 by analyzing 100 peer-reviewed publications according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We examine the technological advancements from traditional machine learning (achieving 75–85% accuracy) through deep learning approaches (85–92% accuracy) to recent large language model (LLM) implementations (90–95% accuracy). Our analysis indicates that LLM-based systems exhibit superior performance in managing multimodal data integration, comprehending traffic events, and predicting non-recurrent congestion scenarios. The key findings suggest that hybrid approaches, which integrate LLMs with specialized deep learning architectures, achieve the highest prediction accuracy while addressing the traditional limitations of edge case management and transfer learning capabilities. Nonetheless, challenges remain, including higher computational demands (50–100× higher than traditional methods), domain adaptation complexity, and constraints on real-time implementation. This review offers a comprehensive taxonomy of methodologies, performance benchmarks, and practical implementation guidelines, providing researchers and practitioners with a roadmap for advancing intelligent transportation systems using next-generation AI technologies. Full article

The paper primarily focuses on the control strategy of an electric dump truck equipped with an M100 methanol range extender. In response to the significant adverse impact of the constant power control strategy on the lifespan of power batteries and the large rotational speed fluctuations of range extenders under the power-following control strategy, a constant-speed and variable-torque range extender control strategy based on the rule-based control strategy is proposed. This strategy enables power following within the range of 70 kW to 130 kW and fixed-point operation at 50 kW and 150 kW. Through co-simulation using AVL Cruise and MATLAB R2022b/Simulink, the results indicate that under the China Heavy-duty Commercial Vehicle Test Cycle-Dynamic (CHTC-D), with an average vehicle speed of 23.19 km/h, the constant-speed and variable-torque range extender control strategy achieves a higher methanol saving rate compared to both the constant power control strategy and the power-following control strategy, thereby demonstrating better fuel economy. The methanol consumption per 100 km for the dump truck using the constant power control strategy, the power-following control strategy, and the constant-speed and variable-torque control strategy are 62.89 L, 64.49 L, and 62.53 L, respectively. Compared with the same type of diesel range-extended electric dump truck, its fuel usage cost has a significant advantage. Full article

This paper presents a detailed experimental evaluation of a newly developed diesel fuel additive, specifically formulated to enhance the energy efficiency and emission characteristics of internal combustion engine (ICE) vehicles, with particular emphasis on its applicability to aging vehicle fleets. Diesel engines are known for producing significant amounts of harmful emissions, necessitating the development of effective mitigation strategies. One such approach involves the use of fuel additives. The additive under investigation is a proprietary formulation containing 1-(N,N-bis(2-ethylhexyl)aminomethyl)-1,2,4-triazole and other compounds. To the best of our knowledge, this specific additive composition has not yet been tested or reported in the existing scientific literature. To evaluate the real-world contribution of such additives, a comprehensive set of controlled measurements was conducted in a certified chassis dynamometer laboratory, including an exhaust gas analyser and supplementary diagnostic equipment. The testing protocol comprised repeated measurement cycles under identical driving conditions, both without and with the additive. Exhaust gas concentrations of CO₂, CO, and NOx were continuously monitored. Simultaneously, fuel consumption and engine performance were tracked over a cumulative driving distance of 2000 km. The results indicate measurable improvements across all monitored domains. CO₂ emissions decreased by 4.57%, CO by 14.29%, and NOx by 3.12%. Fuel consumption was reduced by 4.79%, while engine responsiveness and power delivery showed moderate but consistent enhancements. These improvements are attributed to more complete combustion and an increased cetane number enabled by the additive’s chemical structure. The findings support the adoption of advanced additive technologies as part of transitional strategies towards low-emission transportation systems. Full article

This study investigated the impact of converting High Occupancy Vehicle (HOV) lanes to High Occupancy Toll (HOT) lanes on fundamental traffic flow characteristics, focusing on speed, density, and flow relationships. A 25-mile HOV corridor along I-24 Westbound in Nashville, Tennessee was evaluated using both microscopic simulation via VISSIM and data-driven machine learning through a Multi-Layer Perceptron (MLP) neural network. Four operational scenarios were assessed: (1) HOV lanes without enforcement, (2) HOV lanes with effective occupancy enforcement, (3) HOT lanes with limited access points, and (4) HOT lanes with intermediate access points. Flow-density and speed-flow relationships were modeled using Greenshields theory to extract key traffic performance thresholds including free-flow speed, jam density, and maximum flow. Results indicate that while free-flow speeds were generally consistent across scenarios (ranging from 71 to 80 mph), HOV and HOT lanes exhibited higher values compared to general-purpose lanes. Capacity increases were observed following HOV-to-HOT conversions, especially when intermediate access points were introduced. The MLP neural network successfully replicated nonlinear flow relationships and predicted maximum flow near 2000 vph with a jam density of approximately 215 vpmpl—values that closely matched simulation outputs. Both the VISSIM and MLP-derived diagrams demonstrated curve shapes and capacity thresholds that were highly consistent with Highway Capacity Manual (HCM) standards for freeway segments. However, slightly higher thresholds were observed for HOV/HOT lanes, suggesting their potential for improved operational performance under managed conditions. The integration of simulation and machine learning offers a robust framework for evaluating managed lane conversions and informing data-driven policy. Beyond the scenario-specific findings, the study demonstrates an innovative hybrid methodology that links detailed microsimulation with an explainable neural network model, providing a concise and scalable approach for analyzing managed-lane operations. This combined framework highlights the contribution of integrating simulation and AI to enhance the analytical depth and practical relevance of traffic flow studies. Full article

For vertical takeoff and landing (VTOL) control of distributed-propulsion, fixed-wing UAVs exhibiting strong nonlinearity and aerodynamic/propulsive coupling, traditional linearization methods incur significant modeling errors in pitch–roll coupling and vortex interference scenarios due to neglected high-order nonlinearities, leading to inherent control law limitations. This study focuses on a non-tilting, distributed-propulsion VTOL UAV featuring integrated airframe-propulsion design. Each of its four propulsion units contains six ducted rotors, arranged in tandem wing configuration on both fuselage sides. A revised propulsion–aerodynamic coupling model was established and validated through bench tests and CFD data, enabling the design of an Incremental Nonlinear Dynamic Inversion (INDI) control architecture. The UAV dynamics model was constructed in Matlab/Simulink incorporating this revised model. An INDI-based attitude control law was developed with cascade controllers (angular rate inner-loop/attitude outer-loop) for VTOL mode, integrated with propulsion-system and control-surface allocation strategies. Digital simulations validated the controller’s effectiveness and robustness. Finally, tethered flight tests with physical prototypes confirmed the method’s applicability for high-precision control of strongly nonlinear distributed-propulsion UAVs. Full article

This paper presents a fault-tolerant control strategy that dynamically reconfigures the proposed system, and the inverter leg with a fault is isolated through a MOSFET-based clamping branch. With the use of a modified Vector Control (VC) and Pulse-Width Modulation (PWM) technique, the remaining two phases can continue operating. MATLAB/Simulink is used to create a thorough simulation model that examines various fault scenarios and evaluates how well the control process adjusts to each one. The obtained findings demonstrate that, in the event of a fault, the system can maintain accurate speed regulation, maintain a tolerable current balance, and deliver steady torque. The obtained findings demonstrate that, in the event of a fault, the system can maintain accurate speed regulation, maintain a reasonable current balance, and deliver steady torque. In contrast to traditional methods that rely on hardware redundancy, this software-driven technique maintains the electric vehicle’s functionality even when a malfunction arises. In just a few milliseconds, normal operation is restored without the need for more sensors or additional expenses. Because of these characteristics, the suggested approach is a sensible option for actual EV applications. Full article