Search Results (2,299)

With the continuous growth in motor vehicle ownership, traffic safety risks caused by dangerous driving behaviors remain an important concern. This paper presents RMDD, a Raspberry Pi-based multimodal dangerous driving monitoring feasibility prototype using YOLO26 visual models, personalized facial fatigue estimation, emotion2vec-based speech recognition, and hierarchical reliability-gated BFV fusion (HRG-BFV). HRG-BFV treats object detection as positive-only support for behavior evidence and scales its contribution by development-fold reliability, thereby avoiding hard rejection when the object detector fails. On locked module tests, behavior classification achieved 96.77% Top-1 accuracy, auxiliary detection reached 0.916 mAP@0.5, personalized facial calibration reduced the false-positive rate from 0.877 to 0.211, and speaker-disjoint speech recognition achieved 0.890 accuracy at 0 dB vehicle noise. The historical fixed BFV baseline achieved 0.601 balanced accuracy and 0.506 macro-F1 on P03–P08 (96 clips). In a stricter three-fold leave-one-external-participant-out evaluation on P04/P05/P08 (48 clips), HRG-BFV achieved 0.726 balanced accuracy, 0.562 macro-F1, 0.833 specificity, and 0.677 ROC-AUC, versus 0.655, 0.469, 0.833, and 0.595 for the historical frozen hard gate on the same cohort. Target support reliability was low (0.097–0.194), so the adaptive term appropriately reverted toward behavior evidence rather than producing an unsupported object detection gain. A sealed P08 behavior test exposed substantial cross-subject degradation (0.320 frame accuracy). A cooled 30 min Raspberry Pi 5 run completed without thermal throttling at 0.927 processing windows/s. The evidence supports controlled prototype feasibility but not population-level or on-road generalization. Full article

This work assesses the real-world performance and emission characteristics of a compression-ignition (CI) engine fuelled with a blend of conventional diesel and a bioethanol-derived higher-alcohol fuel (Catalyxx C4+), evaluated under the World Harmonized Light-Duty Vehicle Test Cycle (WLTC). Both cold- and hot-start conditions were analysed, corresponding to coolant temperatures of 20 °C and 70 °C, respectively, in order to capture representative operating scenarios ranging from short-distance urban driving to extended real-world use. Catalyxx C4+ is a renewable drop-in biofuel produced via the thermocatalytic conversion of bioethanol and consists of a mixture of linear and branched higher alcohols spanning C4 to C8. A fuel blend containing 20% Catalyxx C4+ by volume (80% diesel) demonstrated substantial emission benefits relative to neat diesel. Under cold-start operation, particle number (PN), particle mass (PM), and CO emissions were reduced by 61.6%, 77.8%, and 39.3%, respectively. Even greater reductions were observed under hot-start conditions, with decreases of 64.0% in PN, 78.5% in PM, and 66.9% in CO emissions. These results highlight the strong potential of bioethanol-derived higher-alcohol drop-in fuels to significantly mitigate pollutant emissions in CI engines. Furthermore, they emphasize the importance of evaluating sustainable alternative fuels under test conditions that closely reflect real-world vehicle operation, encompassing both low-temperature engine start-up and fully warmed, long-distance driving scenarios. Full article

Electric motorcycles are central to Sub-Saharan Africa’s transition to electric mobility, yet manufacturers in the region typically rely on costly and time-consuming physical prototyping to optimise powertrains built from imported components. This paper presents a validated full-powertrain digital twin of the Roam Air, an electric motorcycle assembled in Nairobi, Kenya, developed in MATLAB/Simulink as four interconnected subsystems: the battery, the controller, the motor, and the vehicle dynamics. The battery is modelled as a Thévenin equivalent circuit whose parameters were experimentally derived at the pack level through Hybrid Pulse Power Characterisation tests, and the controller replicates the motorcycle’s field-oriented control with a maximum torque per ampere strategy, including its battery current and voltage limiting behaviour. The motorcycle’s regenerative braking characteristics, drag coefficient, and rolling resistance coefficient were experimentally obtained through braking, coasting, and coast-down tests. The digital twin ingests rider inputs and environmental information, and it predicts the motor’s speed and the battery’s power. Validation against six measured drive cycles in Stellenbosch, South Africa, demonstrates high correlation between predicted and measured profiles, with Pearson’s r values of 0.905–0.981 for battery power and 0.912–0.996 for motor speed, and energy consumption predicted to within 2.71% for five of the six trips. The presented modelling and characterisation framework offers manufacturers a transferable, computationally efficient alternative to iterative physical prototyping for powertrain optimisation. Full article

Road surface hazards remain a persistent concern for vehicle safety, passenger comfort, and the operational continuity of transport infrastructure. Among these hazards, potholes are particularly significant because they can cause tire damage, suspension wear, wheel misalignment, and sudden vehicle instability. In addition to direct mechanical damage, potholes may reduce driving comfort, increase maintenance costs, and degrade traffic efficiency in urban environments where roads are heavily used and rapidly deteriorate. For these reasons, the timely detection of potholes is an important requirement for road safety and infrastructure management. This work presents a tailor-made dataset for road pothole detection in urban environments, referred to as the Bridgestone Dataset (BS Dataset). The dataset was designed to support object detection from vehicle-mounted imagery collected from a test vehicle under realistic road conditions, thereby aligning the training data more closely with the target deployment scenario. The resulting dataset is intended to support real-time monitoring systems for road hazard detection and maintenance planning. The dataset was also designed as a multimodal resource. In addition to pothole bounding-box annotations, it provides accelerometer and GPS signals to characterize the vehicle dynamics during operation which might help identifying hazard severity and the potential risk to the vehicle. To collect the dataset, the authors developed a smartphone application, which supports the acquisition of both images and vehicle telemetry by leveraging the device’s internal sensors. Full article

Seed germination and early survival are important phenotypes for plant breeding and agricultural management, yet they are still commonly assessed through labor-intensive manual stand counting. We present the Maize Seedling Detection Dataset (MSDD), a curated high-resolution red–green–blue (RGB) dataset derived from unmanned aerial vehicle (UAV) imagery collected over the 2019–2022 growing seasons. MSDD contains 3152 images and 163,921 annotated objects across three classes—single (92.47%), double (6.07%), and triple (1.45%) clusters of seedlings—and captures substantial variability in growth stage (V2–V12), illumination, soil appearance, wind, and camera viewpoint. Unlike many existing datasets, MSDD explicitly annotates clustered seedlings as double and triple classes, which are important for stand evaluation. We benchmarked YOLOv9, YOLO11, YOLOv12, and Faster-RCNN on MSDD to evaluate detection accuracy, class-specific performance, inference efficiency, and generalization across field conditions. Single-seedling detection was reliable across models, with the best mean average precision at 0.5 IoU (mAP@0.5) reaching 0.916, whereas double- and triple-seedling detection remained challenging because of class imbalance, occlusion, and annotation ambiguity. Detection was most reliable in high-contrast scenes and declined under wind, strong shadows, and bright soil backgrounds. YOLO11 provided the fastest evaluation throughput among the tested models (≈27 frames per second (fps)), while YOLOv9 achieved the strongest single-seedling detection performance. Synthetic augmentation improved class balance but did not improve generalization to naturally occurring clustered seedlings. Frames, labels, and trained models are available at Google Drive and Hugging Face. MSDD provides a public benchmark for maize seedling detection and for evaluating stand counting models under realistic field conditions. Full article

This paper presents a novel fractional-order hybrid observer framework for robust sensorless control of brushless DC (BLDC) motor drives in unmanned aerial vehicle (UAV) propulsion systems, addressing the fundamental limitations of conventional integer-order observers through the lens of fractional calculus. The proposed architecture synergistically integrates high-frequency square-wave signal injection for zero/low-speed operation with an adaptive fractional-order extended Kalman filter (AFEKF) augmented by online stator resistance and flux linkage estimation, capitalizing on the memory and hereditary properties inherent to fractional-order systems. A minimum-order current observer enables accurate three-phase current reconstruction using a single DC-link sensor, substantially reducing hardware complexity and cost. The complete algorithm is implemented on an STM32H7 microcontroller and experimentally validated on a 1.5 kW drone propulsion testbench and in-flight platform. Results demonstrate reliable startup under 50% rated load, stable operation from standstill to 5000 RPM on the UAV motor (and validated up to 22,000 RPM on a high-speed test motor, <4° electrical position error at 5 kRPM, and strong robustness against 35% stator resistance variation. In-flight tests confirm improved thrust smoothness and hover stability compared to conventional sensorless strategies. The proposed fractional-order architecture offers a practical, resilient, and computationally feasible solution for next-generation autonomous aerial systems, establishing a new paradigm for observer design in electric propulsion. Full article

Cooperative adaptive cruise control (CACC) platoons rely on Vehicle-to-Vehicle communication and GPS to maintain sub-second headways, creating cyberattack surfaces underrepresented in standard surrogate-safety metrics. We built a fully equation-based, Routh–Hurwitz- and Lp-string-stability-verified simulation of a ten-follower (eleven-vehicle, including the leader) CACC platoon (point-mass dynamics, actuator lag, PD spacing control) and subjected it to a two-channel GPS-spoofing attack corrupting both the attacked vehicle’s control loop and its broadcast position; velocity and acceleration broadcasts, and the CACC feed-forward term they drive, are left uncorrupted, so the reported boundaries are conditional on this restricted, single-channel threat model and should be read as a lower bound on attack severity rather than a worst case. Across a 64-cell severity–duration grid (2–20 m, 1–10 s; h = 0.6 s), minimum time-to-collision fell from 31.7 s to a simulated collision in 6/64 cells (9.4%), driven more by magnitude than duration; the disturbance decays sharply after the first hop rather than cascading down the platoon, so the resulting risk is local, not cascading. A 48-cell headway grid showed h ≥ 0.7 s eliminated all collisions at the originally tested attack duration (3/8 → 0/8 at fixed severity), a result that held under two alternative controller-gain sets tested for sensitivity and was largely, though not universally, robust to a substantially stiffer third set. A position sweep found risk invariant across nine of ten platoon positions. Batch-computed first-hop propagation and tail-to-origin amplification ratios showed the disturbance transiently amplifies (ratio > 1) at its first hop in a third of tested attacks despite decaying three orders of magnitude by the platoon’s tail, a behavior distinct from the front-injected Lp string stability verified separately. Peak root-mean-squared jerk stayed within the comfortable range (≤1 m/s³) in every tested cell, including collisions, showing collision and comfort risk are governed by different parameters. Embedding a representative detection and elastic-control layer alongside headway optimization eliminated collisions within the tested range and remained robust at three times that severity, where headway alone failed; because the detector’s residual is computed directly from the true offset magnitude and detector failure is not modeled, this joint-defense result is illustrative rather than a validated-detector-calibrated estimate. These results give a reproducible, quantified basis for headway- and detection-based mitigation policy in connected-vehicle platoons. Full article

Nitrogen dioxide (NO₂) drives ozone and secondary aerosol formation and harms human health. Chongqing, a mountainous megacity of 32 million people, lacks a fine-scale satellite assessment of its NO₂ evolution. We analyzed tropospheric NO₂ vertical column density (VCD) over Chongqing for 2019–2024. The analysis used Sentinel-5P TROPOMI observations. We computed monthly, seasonal, and annual composites at 5.5 km resolution. Trends were quantified with the Theil–Sen slope and, at the pixel level, the Seasonal Mann–Kendall (SMK) test applied to the full 72-month series. A MODIS land-cover mask separated urban built-up from non-urban pixels. NO₂ concentrated in the central districts and along the Yangtze valley. The core exceeded the mountainous counties by a factor of 3 to 4. TROPOMI resolved the Wanzhou and Yongchuan–Jiangjin hotspots as separate features. The record was divided into three phases. The 2020 lockdown produced the minimum, 31% below the prior February. Rebound emissions produced the 2021 maximum of 5.6 × 10¹⁵ molecules cm⁻² under near-normal dispersion conditions, with ERA5 (the European Centre of Medium-range Weather Forecasts Reanalysis v.5) showing the January 2021 boundary layer 4.3% deeper than its climatological norm. Thereafter the regional mean stabilized: the area-weighted SMK slope was +0.042 × 10¹⁵ molecules cm⁻² yr⁻¹ and not significant (p = 0.14), because emission controls in the core and rising county emissions canceled in the average. Trends diverged sharply in space. The nine core districts declined (median urban Sen slope −0.11 × 10¹⁵ molecules cm⁻² yr⁻¹), whereas 41% of peripheral pixels rose significantly (p < 0.05). The urban-to-rural ratio narrowed from 3.0 in 2019 to 2.3 in 2024 (annual means). This convergence was robust to the built-up threshold (30–50%). Industrial relocation and county urbanization explain the peripheral rise. The results support extending vehicle and industrial emission standards from the core to the receiving counties. Full article

Autonomous commercial vehicles (ACVs) frequently encounter drastic variations in payload and complex road conditions during practical operations. Consequently, effectively suppressing external disturbances caused by payload and road slope uncertainties has become a critical challenge in enhancing the robustness of their low-level control systems. To address this issue, this paper proposes a sliding mode control (SMC) strategy based on Luenberger observer disturbance compensation (LOSM), aiming to simultaneously mitigate the adverse effects of these two uncertainties on the vehicle’s speed control performance. First, according to the driving characteristics of commercial vehicles, a full-condition longitudinal dynamic model encompassing uphill, downhill, and flat road scenarios is established. Second, by deeply integrating the Luenberger observer with sliding mode control theory, an active disturbance rejection LOSM speed controller is designed. Furthermore, the boundary conditions for the closed-loop system to achieve asymptotic stability are rigorously derived and proven using Lyapunov functions. Finally, to comprehensively verify the effectiveness of the proposed strategy, eight typical testing scenarios are constructed, and three benchmark algorithms—PI control, radial basis function adaptive sliding mode (RBFSM) control, and radial basis function backstepping sliding mode (RBFBSSM) control are introduced for comparative analysis. The validation results demonstrate that although all four methods can achieve speed tracking and suppress disturbances, the proposed LOSM strategy exhibits the optimal comprehensive performance across various scenarios. Specifically, its steady-state mean error is typically maintained below 2.5%, and it yields the minimum steady-state variance in the majority of scenarios. These results demonstrate that the designed LOSM method can significantly improve the precision and smoothness of ACVs’ speed control under the dual disturbances of unknown mass and road slope. Full article

Automated Guided Vehicles (AGVs) are a key part of today’s industrial automation, especially for material handling and intralogistics operations. The conventional AGV designs, such as differential-drive vehicles and fixed-steering AGVs, tend to exhibit limited maneuverability in restricted spaces. Such behavior can be attributed to comparatively large turning radii in classic designs, which limit the possibility of efficient movement. Thus, the production of affordable AGVs with high motion flexibility and load stability remains a challenge in AGV development. To resolve this issue, PID-controlled reverse-phase steering method is suggested. Experimental evaluation with 12 trials demonstrated a decreased turning radius for the designed AGV from 1.5 ± 0.08 m (literature-reported value) to 0.84 ± 0.05 m (current study finding), corresponding to an approximately 46.7% reduction. Results demonstrate the proposed AGV’s improved cornering capabilities. In addition, the lateral deviation achieved from the designed AGV stands at an average of 3.1 ± 0.5 cm, while the Root Mean Square Error (RMSE) is 3.5 cm, resulting in an overall accuracy rate of 96% ± 1.2%. Obstacle avoidance tests confirm successful performance within an obstacle range of up to 80 cm. Overall, the developed AGV represents a scalable and economical system for intelligent material handling within the industrial environment. Full article

This paper presents the design, kinematic modeling, and dynamic simulation of a novel conical-slider-based transformable wheel with five deployable wheel-leg elements for mobile robotic systems. The proposed wheel can operate in a closed-wheel configuration for regular terrain and in an open wheel-leg configuration for enhanced interaction with rough terrain and obstacle profiles. The transformation motion is generated through a central linear actuation input transmitted by a conical slider mechanism integrated into the wheel hub. A CAD-supported mechanical design was developed to examine the geometric feasibility of the proposed wheel structure and to verify the radial deployment motion of the wheel-leg elements. The kinematic formulation was revised in a compact indexed form by consistently considering the angular offsets of all five wheel-leg elements. In addition, a dynamic model including the six-wheel vehicle body, suspension elements, wheel–ground contact, wheel-leg–ground contact, and wheel driving inputs was formulated. A unilateral contact model was used to represent contact, loss of contact, and re-contact events while preventing non-physical tensile normal forces. The proposed wheel concept was evaluated using a MATLAB-based representative mixed-terrain simulation scenario that combines rough-terrain locomotion and traversal of a 0.35 m single obstacle. The simulation results show that the fully deployed wheel-leg configuration successfully traverses the tested 0.35 m obstacle, whereas the closed-wheel configuration fails under the same terrain condition. Because the conical slider is continuously adjustable, an intermediate deployment state was also evaluated: it traverses a 0.30 m obstacle that the closed configuration cannot, yet fails against the 0.35 m obstacle, so that the traversal threshold varies monotonically with the deployment stroke. The comparison demonstrates that the deployed wheel-leg elements improve obstacle traversal capability by increasing the effective contact geometry and providing additional interaction with the obstacle surface. The results indicate that the proposed conical-slider-based transformable wheel has the potential to improve the terrain adaptability and obstacle traversal performance of six-wheel mobile robotic systems. Since the present study is limited to CAD-supported design verification and MATLAB-based dynamic simulation, future work will focus on prototype manufacturing, actuator design, structural analysis, and experimental validation under real terrain conditions. Full article

The rapid growth of battery energy storage systems, renewable energy integration, electric vehicles, and DC microgrids has significantly increased the demand for compact, efficient, and bidirectional isolated DC–DC converters. Conventional Dual Active Bridge (DAB) converters commonly employ high-frequency transformers to provide galvanic isolation and bidirectional power transfer. Although transformer-based DAB converters offer excellent performance, their magnetic components increase converter volume, weight, core losses, leakage inductance, manufacturing complexity, and overall cost. Consequently, recent research has explored alternative high-frequency energy transfer techniques based on capacitive coupling, aiming to reduce magnetic components while preserving efficient resonant power conversion.This paper proposes a Capacitively Coupled Dual Active Bridge (CC-DAB) converter employing high-power metallized polypropylene (MKPH) capacitors as the high-frequency energy transfer medium. The proposed converter operates at a relatively low switching frequency while investigating the safe operating conditions of the capacitive coupling network to ensure reliable and efficient power transfer. A microcontroller-based single-phase-shift (SPS) modulation strategy is implemented to generate the gate-driving signals of the full bridges, whereas the switching frequency is selected to achieve zero-voltage switching (ZVS) throughout the investigated operating range. The phase-shift angle ($\varphi$) regulates the transferred power by controlling the voltage difference between the primary and secondary bridges across the capacitive coupling network. The proposed converter is analyzed theoretically and validated through simulation and experimental testing. Experimental results demonstrate stable bidirectional power transfer, soft-switching operation, and a peak conversion efficiency of 91.3% at a relatively low switching frequency of 52 kHz. The experimental verification confirms the practical feasibility of capacitive coupling for resonant bidirectional power conversion and demonstrates its potential as an alternative architecture for low- and medium-power applications requiring compact size, high efficiency, reduced magnetic component requirements, and reversible energy transfer. Furthermore, the proposed topology contributes to the ongoing development of transformerless resonant converters by experimentally validating a high-frequency capacitive coupling network capable of supporting efficient bidirectional power flow under practical operating conditions. Full article

The development of fuel cell electric vehicles (FCEVs) remains challenged by complex system integration, powertrain design, and the limited availability of experimental data under realistic operating conditions, which constrains the validation of energy management systems (EMSs) and system-level performance assessment. To address this gap, this study presents a validated test bench platform for fuel cell powertrains that combines a hardware-based powertrain test bench with a simulation environment for EMS analysis. The platform enables the integration and testing of a fuel cell powertrain in an electric van under realistic operating conditions. Validation under the US06 driving cycle shows an equivalent hydrogen consumption deviation of only 5.1 g (2.4%) between the hardware and simulation environments, demonstrating high platform reliability. A comparative analysis of load-following and average load power strategies is conducted. Results indicate that the average load power strategy achieves higher energy efficiency, reducing equivalent hydrogen consumption by 1.8%, 3.8%, and 6.4% under city, rural, and highway conditions, respectively. The efficiency advantage becomes increasingly pronounced as power demand rises. The proposed platform provides a validated framework for system-level development, validation, and evaluation of fuel cell powertrain systems. Full article

The rapid development of electric vehicles has increased the need for a better understanding of the relationships between vehicle dynamics, energy consumption and control strategies. This study presents an experimental investigation of the acceleration and coasting characteristics of a 2024 Kia Niro EV. Road tests were combined with laboratory measurements of the vehicle mass properties, including the centre of gravity. Vehicle motion parameters were recorded using a GNSS/INS measurement system, while electric powertrain data were acquired from the vehicle CAN bus using proprietary software developed by the authors. The influence of driving mode, accelerator pedal position and regenerative braking intensity was analysed. The results showed that the selected driving mode significantly affects the acceleration characteristics only at intermediate accelerator pedal positions, whereas identical maximum performance is obtained with the accelerator pedal fully depressed. The energy required to accelerate the vehicle to 90 km/h remained nearly constant under most operating conditions, indicating high electric powertrain efficiency. During coasting, regenerative braking recovered up to 50% of the energy previously required for acceleration. The obtained results provide valuable experimental data for the validation of vehicle dynamics and energy consumption models and support the development of more efficient electric vehicle control strategies. Full article

In aerospace applications, structural vibration can cause fatigue accumulation and shorten the service life of aircraft. This makes vibration suppression based on Macro Fiber Composite (MFC) piezoelectric composites an important research topic. Considering the asymmetric high-voltage operating range of the M-8557-P1 MFC from −500 V to 1500 V and its capacitive impedance characteristics within the 1000 Hz operating frequency band, this paper designs a laboratory prototype of a high-voltage driver. The prototype adopts a voltage–current dual closed-loop structure and a current-tracking PWM control strategy. Under the tested laboratory conditions, the prototype exhibited a relatively fast transient response and a certain dynamic driving capability for capacitive loads. Based on the laboratory prototype, an auxiliary signal-conditioning module and a digital control module equipped with an active control algorithm were further developed. These modules were integrated with the laboratory prototype to form a high-voltage closed-loop control system for MFC piezoelectric sensing and actuation. Ground laboratory tests were conducted on a high-aspect-ratio unmanned aerial vehicle wing. The experimental results show that, when the dominant vibration frequency is approximately 3.6 Hz, the response converges to a steady state within 4.77 s after control is applied. In the steady state, the root-mean-square displacement decreases from 15.57 mm to 4.28 mm, corresponding to a reduction of 72.52%. This result demonstrates the effectiveness of the active vibration control system under this representative application scenario. Full article