Browse Topic: Data acquisition and handling

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Latency-Aware Optimal Scheduling Scheme for Computational Offloading12-10-01-000210/9/2026
Proactive safety and vehicle automation typically requires high energy use from sensors and energy-intensive computing from sensor data processing. For high-quality, reliable perception and localization within a driving environment at the vehicle level, incoming data from multiple sensors need to be fused using advanced computational algorithms, which demand a high compute load. Alternatively, computational offloading of automated driving tasks shifts energy consumption from the vehicle to cloud infrastructure, where renewable energy sources, such as hydropower or solar power, can be utilized more efficiently. Herein, an optimal scheduling strategy for autonomous driving tasks via the cloud layer is formulated as a mixed-integer linear programming (MILP) problem and verified using measurement-informed task graphs. It is shown that cloud-based computational offloading enables energy-efficient operation while maintaining task deadlines to ensure the timely availability of perception and localization information, which is consistent with prior studies in the literature. Simulation results demonstrate that on average, 35.78% of the computational load was offloaded to the cloud, which can achieve significant energy savings for the onboard system. The compute operations achieved an average energy savings of 35.65%, while total system savings ranged from 26.16% to 30.67% under different cloud energy efficiency scenarios, highlighting the advantages of offloading compute-intensive tasks. The framework was further evaluated using multiple directed acyclic graph (DAG) configurations to assess its scalability and adaptability. Critical tasks, such as sensor fusion, were executed exclusively on the vehicle to ensure real-time responsiveness.
Sharma, Sachin, Meyer, Richard, Feinberg, Ben, Asher, Zachary
Development and Validation of Quasi-Dimensional Multi-Zone Combustion Model for Homogeneous Reactivity-Controlled Compression Ignition with Renewable Fuels—Part 1: Fuel and Temperature Stratification Prediction03-19-05-002310/9/2026
As part of the dtec.bw MORE project, serial hybrid powertrains featuring alternative combustion regimes, specifically homogeneous reactivity-controlled compression ignition (hRCCI), are under investigation to maximize thermal efficiency while minimizing NOx and soot emissions. The proposed hRCCI concept utilizes dual-fuel stratification, employing early direct injection (DI) of octanol (high-reactivity fuel) alongside port fuel injection (PFI) of ethanol (low-reactivity fuel). While 0D/1D engine modeling is essential for developing predictive powertrain simulation frameworks, conventional models often lack the robustness required to capture the complexities of low-temperature combustion (LTC). This study addresses this limitation by developing a multi-zone “onion skin” quasi-dimensional model. Since for LTC, ignition and heat release rates are highly sensitive to thermal and chemical stratification, capturing these gradients is critical. 3D computational fluid dynamics (CFD) simulations are capable of capturing thermal and chemical stratifications to a high degree of accuracy, whereas 0D simulation models, in general, do not consider them. The primary contribution of this work is the translation of high-fidelity 3D CFD data into a computationally efficient quasi-dimensional environment. A simplified 3D CFD architecture was utilized to map the effects of various operating parameters on mixture distribution. Using these results, a regression learning model was trained to predict octanol and temperature stratification within the combustion chamber. Validation against simulation data demonstrates that this coupled machine learning and multi-zone approach provides a robust, predictive tool for evaluating advanced LTC concepts within larger system-level simulations.
Sundaram, Pravin Kumar, Grundl, Larissa Michaela, Trapp, Christian Thorsten, Tinschmann, Georg
Terrain-Aware Probabilistic Prediction of Suspension Component Forces in Unmanned Ground Vehicles2026-01-75099/22/2026
Unmanned ground vehicles (UGVs) operating in unstructured environments must account not only for terrain traversability but also for terrain-induced loads that affect component durability. Existing path planning approaches primarily consider obstacle avoidance and mobility, while neglecting cumulative structural degradation due to repeated loading. High-fidelity physics-based simulations can capture these effects but are computationally prohibitive for real-time applications. This study investigates machine learning-based surrogate models for predicting vehicle component reaction forces from terrain height sequences generated using a controlled parametric terrain formulation. Both feed-forward and recurrent neural network architectures are evaluated, and ensemble-based probabilistic techniques are incorporated to quantify predictive uncertainty. Results show that the ensemble long short-term memory (Ens-LSTM) model achieves the lowest prediction error (mean absolute error of 0.621 kN) while maintaining narrow 95% prediction intervals (3.22–4.28 kN). A simpler ensemble feed-forward network (Ens-NN) achieves comparable accuracy (0.626 kN) with reduced model complexity. These results demonstrate that data-driven surrogate models can provide accurate and uncertainty-aware force predictions, enabling the integration of structural reliability considerations into fatigue-aware path planning for UGVs.
Chua, Yang Kang, Mundiwala, Mohammad, Wang, Xudong, Castanier, Matthew, Hu, Zhen, Hu, Chao
Deep Learning-Based Rotor Temperature Estimation for Rare-Earth-Free Motors2026-01-75069/22/2026
This paper presents a deep learning-based approach for online rotor temperature estimation in electrically excited synchronous motors (EESMs). Accurate rotor temperature estimation is critical for ensuring safe operation, improving performance, and enabling reliable thermal management of electric traction motors. Recurrent neural network (RNN) architectures, including gated recurrent unit (GRU) and long short-term memory (LSTM) networks, are investigated to develop a data-driven thermal virtual sensor capable of capturing the temporal dynamics of motor operation. Experimental data collected from a 190 kW EESM prototype are used to train and evaluate the proposed models. A systematic training, testing, and 10-fold cross-validation framework is employed to assess prediction accuracy and generalization capability. The results demonstrate that the GRU-based model achieves higher prediction accuracy than the LSTM model while maintaining comparable inference latency. The proposed approach provides an efficient and lightweight solution for real-time rotor temperature estimation suitable for embedded motor control applications.
Tatari, Farzaneh, Aligoudarzi, Mohsen Mirza
Streamlined Model Composition for OneSAF for Use in Rapid Trade Study Data Collection and Analysis2026-01-75479/22/2026
This research is meant to enhance the analytic capabilities of OneSAF for usage as a Monte Carlo-style data generator for use in large problem space trade studies. Using novel ground vehicle data such as: RHA armor values, weapon penetration prediction models, and sensor values, new models can be developed in OneSAF for use in data generation and analysis. This process and companion software developed for this purpose enables the rapid construction and evaluation of differing vehicle variants in a fraction of the time of the baseline process, improving the efficiency of using OneSAF as a data analysis tool. This approach facilitates a more comprehensive virtual experimentation approach that can use manufactured data as a part of the process.
Sapunkov, Oleg, Roberts, Bradshaw, Jorgensen, Maxwell
Leveraging Domestic Automotive Software Development Best Practices for MOSA and GCIA/Victory Implementation2026-01-75389/22/2026
ISO 26262ISO/SAE 21434ISTQB/ASPICEMOSAGCIAVICTORY
Priemer, Douglas, Sime, Karl
Scene-Level Multi-View Instance Consistency Using Multi-Feature Vision Fusion for Synthetic Ground Vehicle Environments2026-01-75029/22/2026
Maintaining consistent object identities across multiple camera viewpoints is a critical challenge in synthetic perception environments used for autonomous ground vehicle evaluation. This paper presents a scene-level multi-view instance consistency framework that integrates OpenUSD scene composition, Omniverse Replicator synthetic-data generation, and a multi-feature vision fusion pipeline. The proposed approach combines semantic embeddings from CLIP, patch-level descriptors from DINOv2, geometric correspondences from LoFTR, mask-derived shape invariants using Hu moments, and relative-position priors to associate object instances across views, including visually identical objects. A compact composite scoring function fuses these complementary cues to achieve robust cross-view identity assignment while preserving OpenUSD asset modularity through grouped-prim support. Synthetic experiments across 120 multi-camera scenes demonstrate improved Top-1 Match Accuracy and Identity Consistency Rate, with reduced ID-switch occurrences compared to single-cue baselines. The framework supports scalable, repeatable, and traceable digital engineering workflows for defense-oriented perception evaluation.
Bhattacharya, Sambit, Nakamoto, Kyle
Change Detection Filtering with Visual Language Models2026-01-75089/22/2026
Intelligence, surveillance and reconnaissance (ISR) often require review of significant quantities of video. While machine vision is used to flag objects for human review, too many flags are generated. Integrating newer methods like Open-Vocabulary Object Detection (OVOD) that support zero shot detection, but with significantly lower accuracy only make the problem worse. This work addresses the utility of OVOD in ISR missions by focusing only what has changed between successive runs through an environment. A Vision Language Model (VLM) compares current observations against a registered “cleared” baseline to focus only on what has changed. Testing across three distinct environments, and using either monocular camera phones or RGB-D equipped vehicles, demonstrates that integrating change detection can automatically remove as much as 80% of unchanged objects without impacting recall.
Martinson, Eric, Fishta, Igri
SOSA Enabled Automotive Technologies Disrupt the Battlefield2026-01-75359/22/2026
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Pilaud, William J.
Multimodal Digital-Twin Simulations for Off-Road Autonomous Vehicles2026-01-75509/22/2026
Research in autonomous driving has largely focused on structured, on-road environments in densely populated urban areas, leaving off-road autonomy comparatively underexplored despite its importance for applications such as search and rescue, agriculture, and defense. Progress in developing solutions for off-road autonomy is heavily constrained by the high cost, safety risks, and limited coverage of real-world off-road data collection, particularly for rare terrain-induced failure cases. To address these challenges, we introduce a large-scale simulation platform for off-road autonomous driving generated in the Unity3D engine. Our simulator provides indefinite data from multiple RGB cameras, LiDAR, GPS, and IMU sensors to support research in long-duration applications. Our platform also facilitates research in risk-aware planning through dynamic weather, lighting, and annotations for sequence-level failures like collisions and weather-induced sensor blockages. By combining long-duration temporal coverage, multimodal sensing, and procedurally generated terrain diversity, our simulator facilitates progress for systematic evaluation of learning-based autonomous driving models in unstructured, safety-critical environments.
Ross, Timothy, Boone, Julia, Afghah, Fatemeh
Analyzing Open-Source Data to Inform Military Vehicle Design2026-01-75599/22/2026
The modern battlefield is increasingly transparent, generating large volumes of open-source data on the use, damage, and loss of military vehicles. This paper presents a structured methodology to exploit such data for deriving operational requirements for future vehicles. It uses a mixed-method framework combining qualitative reporting with quantitatively verified loss data. Daily battlefield reports are analyzed with large language models to extract operational context, employment patterns, and tactical conditions. These insights are cross-referenced with loss data to assess how operational factors affect vehicle survivability, with the findings being used to prioritize requirements that improve vehicle performance. The approach is demonstrated through a case study of Leopard tanks in the Russia-Ukraine war, using Institute for the Study of War reports and Oryxspioenkop loss data. Results show how open-source intelligence can systematically inform survivability, mobility, and combat effectiveness in modern vehicle design.
Lynch, Benjamin, Mittal, Vikram
Distributed Intelligence at the Tactical Edge: Enabling Contested Logistics in Degraded Networks2026-01-75589/22/2026
Contested logistics environments expose the limitations of both legacy fragmented systems and emerging Next Generation Command and Control architectures that assume persistent connectivity. In degraded or denied conditions, sustainment operations face latency, bandwidth constraints, and reduced decision velocity. Expanded decision support tools further increase reliance on timely, relevant data exchange. This paper argues that contested logistics requires distributed, mission-aware intelligence at the tactical edge. Low-power onboard compute enables real-time inference, adaptive data conditioning, and connectivity-aware transmission across Radio-Frequency and non-RF pathways. By selectively elevating critical information based on mission context and network state, edge-intelligent architectures improve survivability, bandwidth efficiency, and sustainment effectiveness in degraded networks.
Baumann, Edward, Pardee, Shawn
Golden Tester Framework for SAE J1939-91C Cybersecurity2026-01-75229/22/2026
The impending formal adoption of SAE J1939-91C creates an urgent need for rigorous, repeatable validation methods that extend beyond functional conformance. This paper presents a structured validation and benchmarking framework, with a focus on performance characterization across dynamic vehicle configurations. Building on prior work in secure network formation, rekeying, and Golden Tester concepts, we define a minimal, transport-agnostic set of cryptographic and protocol test vectors for deterministic validation of secure message authentication, alongside simulation-based methods for evaluating network formation and rekey behavior. The framework integrates performance metrics such as secure message latency, rekey time and throughput, while also introducing cybersecurity-specific diagnostics and logging requirements for gateway module implementation. Security validation scenarios are mapped to explicit detection and response benchmarks. The resulting methodology provides OEMs, Tier-1 suppliers, and research organizations with a practical, reproducible approach to validating J1939-91C implementations, supporting both development-phase evaluation and ongoing lifecycle assurance.
Zachos, Mark, Kulkarni, Prakash
Intelligent Optimization Simulation Framework for Test and Evaluation of Autonomous Systems2026-01-75519/22/2026
This paper describes ongoing research and development of an efficient optimization/search–based modeling and simulation framework for rapidly identifying low-performance scenarios in advanced autonomous systems. Ensuring predictable, safe behavior across complex, integrated systems remains a core operational test-and-evaluation challenge. Our goal is to balance rigorous validation with timely deployment. We are developing TEAAS (Test & Evaluation of Advanced Autonomous Systems), a scalable, faster-than-real-time framework designed to uncover critical failure scenarios efficiently. Key features include GPU-accelerated parallel simulation and learning, computational intelligence–based search of optimal parameters, uncertainty quantification for reproducibility, and real-time physics-accurate sensor models. We conducted simulation experiments to evaluate and demonstrate the framework performance for two black-box ground-vehicle autonomous systems. Key results were that adequate uncertainty quantification can be achieved with as few as 10 repeated runs per simulation scenario, sensor realism has a significant effect on failure rate, distinct differences between the two autonomies failure modes were identified, and our efficient optimization/search methods identify critical performance regions in a small fraction of the number of simulations required by a naïve Monte Carlo search.
Snarski, S., Menozzi, A., Persons, B., Lazar, D., Khan, N.
Experimental Comparison Between Real and Simulated Battery Pack Efficiency in Light Quadricycles Using a Custom Inertia Dynamometer2026-24-00089/21/2026
This study compares the energy efficiency of a real battery pack and a simulated battery pack using a hardware-in-the-loop battery emulator, through experimental testing on a dedicated inertia dynamometer for light quadricycles. The investigated LiFePo4 battery pack has a nominal voltage of 48 V and a nominal capacity of 100 Ah. Initial characterization identified a reduced State of Health based on capacity (SOHC), with a measured usable capacity of 48 Ah (from the nominal 100 Ah) and a corresponding reduction in charge acceptance capability. The emulator was configured to replicate the degraded battery characteristics, including the open-circuit voltage (OCV)-SOC relationship, internal resistance, and current limitations, enabling a direct comparison between simulated and experimental dynamic behavior. The experimental setup was designed to overcome the limitations of conventional chassis dynamometers for low-mass, independent four-wheel-drive quadricycles operating without mechanical friction braking systems. Multiple driving cycles were reproduced using a PLC-based closed-loop control system, with data acquisition performed via CAN communication. The comparative analysis highlights significant differences during regenerative braking events. While the emulator accurately reproduces baseline electrical behavior under mild operating conditions, the aged battery exhibits strong limitations during both high-power acceleration and severe deceleration phases. In particular, increased internal resistance and transient electrochemical polarization lead to premature saturation of charge acceptance, resulting in rejection of high-frequency current transients. Consequently, the experimentally observed energy recovery is significantly lower than the theoretical values predicted by the emulator. In addition, due to the absence of mechanical braking, the reduced regenerative capability directly leads to speed tracking deviations, as the required braking torque cannot be fully achieved. These results identify battery degradation as a key physical constraint affecting both energy efficiency and dynamic braking performance, highlighting the importance of improved electro-thermal and aging-aware model calibration for realistic system-level simulations.
Sementa, Paolo, Vaglieco, Bianca Maria, Altieri, Nunzio
Adaptive Neural Network Fuel Mass Correction for Structural Model Error Compensation in Diesel Engines with Variable Biodiesel Properties2026-24-00239/21/2026
Compression-ignition engines operating with biodiesel blends often exhibit variability in fuel properties, such as density, viscosity, and cetane number, which can lead to systematic deviations in injected fuel mass when using conventional physics-based models. These deviations can reduce combustion efficiency and increase brake-specific fuel consumption (BSFC). This study proposes a lightweight neural network–based approach to compensate for structural errors in baseline injection models, using a single-layer perceptron trained on the relative error (delta) between actual and modeled injected mass. By normalizing engine and fuel parameters and introducing a small amount of measurement noise, the network learns to predict a corrective factor that adapts the injected mass to match the desired target under varying fuel conditions. Simulation results demonstrate that the neural correction significantly reduces systematic bias: in test cases with intentionally introduced structural error, the average injection deviation of −1.7% in the baseline model is reduced to approximately 0.002% after correction. Root-mean-square (RMS) error over training and validation datasets remains below 0.16%, indicating robust generalization. The proposed method offers a computationally efficient solution suitable for embedded engine control units, requiring minimal additional complexity while ensuring precise fuel delivery. By eliminating bias caused by fuel property variability, the approach has the potential to improve fuel economy, reduce emissions, and maintain consistent engine performance under a wide range of operating conditions. This framework provides a practical path for integrating adaptive, data-driven correction mechanisms in diesel engines operating with heterogeneous or variable biofuels.
Gutierrez, Marcos, Taco, Diana
EKF-Based Machine Learning Approach for Battery SOH Prediction in Commercial Vehicles Fleet2026-24-00029/21/2026
Estimating battery state of health (SOH) from field data is essential to ensure successful operation and increase the uptime of battery electric vehicles (BEVs). Most studies in the literature propose methods relying on datasets acquired under controlled laboratory conditions. However, SOH estimation becomes significantly more challenging when dealing with real-world data due to the increased variability and complexity of operating conditions. In this work, CAN telematics data, sampled at 1 Hz, were collected over approximately 20 months of operation from 10 electric commercial vehicles. During this period, a maximum battery degradation of 4% is observed within the fleet. Firstly, a model-based framework was introduced, in which a second-order battery equivalent circuit model (ECM) was coupled with an extended Kalman filter (EKF) to estimate the battery SOH. Results confirmed that the EKF is able to accurately capture the battery's physical behavior and degradation trend, yielding a maximum root mean square error (RMSE) of 1.23% when compared with the SOH signal provided by the onboard BMS. However, a Kalman filter requires accurate model parameter identification and high-frequency measurement data, leading to increased computational costs. To bridge these gaps, this paper utilizes the SOH estimates obtained from the EKF to train and validate a feedforward neural network (FNN) model, specifically designed to operate on aggregated metrics. The FNN model can provide accurate SOH estimates, with a RMSE as low as 0.26% during the testing phase. The approach proposed in this work combines the interpretability of model-based methods with the scalability and reduced data dimensionality of machine learning (ML) ones, making it more suitable for monitoring battery SOH in large fleets of BEVs.
D'Agostino, Valerio, Pulvirenti, Luca, Shanker, Anirudh, Cardone, Massimo, Rizzoni, Giorgio, Vitale, Francesco
Computational Electro-Thermal and Degradation Modeling Framework for Lithium-Ion Batteries in Electric Vehicles2026-24-00119/21/2026
This paper presents an integrated computational framework that couples electro-thermal and degradation dynamics for lithium-ion batteries used in electric vehicles (EVs). The model is implemented using Python. At the cell level, the model describes charge and discharge behavior, state of charge (SOC), terminal voltage, internal resistance losses, and heat generation. An energy balance equation is used to estimate temperature variation during operation. Temperature-dependent resistance and capacity are included to represent nonlinear battery behavior under different load conditions. At the system level, feedback relationships between SOC, temperature, state of health (SOH), and degradation rate are modeled using system dynamics. Battery aging is represented through mathematical functions that relate capacity loss to temperature and usage cycles. This allows simulation of long-term performance under different driving scenarios. The model enables parametric and sensitivity analyses to evaluate the effects of discharge rate, ambient temperature, and degradation parameters. Results show the strong interaction between thermal behavior and battery aging. Model validation against experimental data is not performed in this study and remains an important direction for future research. The current work focuses on the derivation and parametric analysis of the modelling framework. The proposed framework provides a clear, low-cost, and scalable computational approach for EV battery analysis, design studies, and engineering education.
Gutierrez, Marcos, Taco, Diana
Neural-Network-Enhanced Air-Standard Thermodynamic Modeling for Spark-Ignition Engines Fueled with Low Heating Value Alternatives2026-24-00169/21/2026
This study presents a computational framework that integrates an air-standard thermodynamic engine model with artificial neural networks to predict the performance of spark-ignition (SI) engines operating with alternative fuels of reduced lower heating value (LHV). A deterministic thermodynamic simulator was developed in Excel, incorporating engine geometric parameters (compression ratio, bore, stroke, displacement), operating speed, and fuel properties, with particular emphasis on LHV as the dominant energetic descriptor. The model computes in-cylinder states, thermal efficiency, indicated mean effective pressure, and power output under idealized air-standard assumptions. To extend predictive capability beyond fixed-parameter analyses, a feedforward neural network was trained using datasets generated from systematic parametric sweeps of engine geometry, speed, and fuel LHV. The neural network captures nonlinear interactions between compression ratio, combustion energy release, and performance indicators, enabling rapid estimation of engine response when substituting conventional gasoline with lower-LHV alternative fuels. Results demonstrate that the hybrid thermodynamic–neural approach accurately predicts trends in efficiency degradation and power reduction associated with decreasing LHV, while identifying compensatory design adjustments, particularly through compression ratio optimization. The methodology provides a low-cost and computationally efficient tool for preliminary evaluation of alternative liquid fuels in SI engines without resorting to complex CFD or experimental campaigns. This work contributes a transparent, reproducible modeling strategy suitable for early-stage engine design studies and fuel screening, supporting sustainable fuel transitions in spark-ignition propulsion systems.
Gutierrez, Marcos, Taco, Diana
Comparative Analysis of BEV Performance from Simulated Environment and Physical Hardware Platform2026-24-00059/21/2026
The proliferation of simulation environments has accelerated technological progress across various scientific domains by offering a cost-effective and time-efficient framework for data acquisition and analysis. In the automotive sector, high-fidelity modelling of vehicle components and driving scenarios bypasses the logistical constraints associated with hardware procurement and the intensive requirements of large-scale testing infrastructures. However, pre-calibrated or native software models often imply simplified hypotheses, missing relevant aspects of the entire powertrain-to-wheel energy chain. This study presents a comparative analysis of battery performance within a battery electric vehicle (BEV) by synchronizing virtual simulations with experimental hardware at the test bench. The methodology involves the concurrent modelling of the driving environment, the vehicle chassis, and the propulsion system, followed by the execution of identical driving cycles on a physical platform. The experimental setup comprises a fully instrumented BEV featuring an integrated electric motor and battery pack, specifically configured for high-precision signal acquisition. The virtual section starts with the development of a digital twin within a commercial simulation suite, parameterized according to the vehicle specific dynamic and energy requirements. This is followed by the integration of the electric propulsion system and a battery pack model based on the equivalent circuit model method. To ensure high fidelity, the battery model is experimentally calibrated via multi-step pulse discharge tests performed on the physical hardware. Subsequently, various driving scenarios from the simulated environment are translated into speed-time profiles and are replicated on the real vehicle using a PID-controlled actuator on the accelerator pedal. The battery pack that serves the vehicle is monitored during the cycle to collect information on the electrical performance. Finally, a comparison between the simulated and real battery behaviour is performed. This dual approach used in the present work, which compares the simulation accuracy against real-world performance, provides critical insights into the inherent advantages and technical boundaries of digital modelling in electromobility applications.
Sequino, Luigi, Sementa, Paolo, Altieri, Nunzio, Vaglieco, Bianca Maria, Sorrentino, Chiara
Short-Term Load Forecasting of Sustainable Power Systems Based on Newton–Raphson-Based Optimizer–Extreme Gradient Boosting under Extreme Weather Events13-07-02-00089/17/2026
With continuous advancements in load-side resources such as distributed photovoltaic systems, electric vehicles, and virtual power plants, the low-carbon and sustainable development attributes of power systems have been significantly enhanced. Meanwhile, the coupling intensity between sustainable power systems and meteorological conditions has been further consolidated. Considerable impacts are exerted by weather variations, particularly extreme weather events, on the dispatching and operation of sustainable power systems. Accurate load forecasting is critical for enabling sustainable power systems operators to optimize power generation strategy, ensuring supply stability and resilience against extreme weather-induced disruptions. However, the intrinsic non-stationarity and volatility of extreme weather events present significant challenges to conventional forecasting approaches. Herein, we introduce a hybrid algorithm integrating Newton–Raphson-based optimizer (NRBO) with extreme gradient boosting (XGBoost) to enhance short-term load predictions under such conditions. The model uses optimally selected meteorological and load features as inputs, while NRBO systematically tunes XGBoost’s hyper-parameters to maximize performance. Evaluated on an Irish dataset, the proposed framework is quantitatively compared against five baseline models, including traditional decision trees and neural networks. The case studies show that the mean absolute percentage error (MAPE) of the proposed model is 2.57%, which is the lowest among these decision tree and neural network algorithms.
Wang, Yi, Zhou, Jian, Wu, Gang, Ma, Tiannan, Ma, Ruiguang, He, Chuan, Zhu, Huixian
Further Investigation of the Variables Affecting Brake Squeal: Dual-Piston and Single-Piston Calipers with NAO Pads2026-01-08259/14/2026
An earlier publication reported that brake squeal occurrence increases with increasing (inboard/outboard) pads wear rate difference in the case of a front dual-piston (twin-piston) caliper for a GVW vehicle of 2,510 kg fitted with Lowmet pads of straight chamfers and diamond chamfers. The current investigation was undertaken to find out if a front dual-piston caliper for a heavier vehicle (GVW 3,200 kg) fitted with NAO pads of straight chamfers, and a lighter single-piston caliper (GVW 2,100 kg) fitted with NAO pads of straight chamfers behave the same or not, using the SAE J2521 and Los Angeles City Traffic simulation procedures. In all cases, brake squeal is found to increase with increasing (inboard/outboard) pads wear rate differences (wear differentials); increasing pad radial taper is associated with increasing (I/O) pads wear differential; pad tangential taper lowers the (I/O) pads wear differential. Increasing friction coefficients do not relate to increasing squeal occurrences. To minimize brake squeal occurrence, caliper should be designed to minimize (I/O) pads wear differential.
Sriwiboon, Meechai, Rhee, Seong Kwan, Sukultanasorn, Jittrathep, Khathinhorm, Nicha, Kunthong, Jitpanu
Predicting Wear of NAO Brake Pad Using Scaled-Down FMVSS 135 Testing and Machine Learning2026-01-08089/14/2026
Brake pad wear is a major and growing source of non-exhaust particulate emissions, projected to reach 1.3 million tons annually by 2030 and contributing up to roughly 55% by mass of non-exhaust traffic-related PM10 in urban environments, underscoring the need for improved durability and material optimization. This study investigates a three-stage eXtreme Gradient Boosting (XGBoost) ensemble paired with a residual Fully Connected Neural Network (FCNN) corrector to predict brake pad wear rate and support formulation optimization. Experiments used a simplified FMVSS 135 protocol on a Universal Mechanical Tester (UMT) simulating realistic braking across eight friction regimes. Wear rate was the sole machine-learning prediction target, while coefficient of friction (CoF) was retained as an input feature rather than a target. Despite a limited but high-quality 280-cycle dataset, regime-aware stratified splitting, sample reweighting, and hyperparameter optimization enabled robust generalization. The three-stage XGBoost ensemble with residual FCNN correction achieved a global held-out test R2 of 0.976 for wear rate prediction. A Taguchi L8 design of experiments defined the brake pad compositions, reducing experimental time and material consumption compared to conventional approaches. The framework demonstrated strong agreement between measurements and predictions for the dominant low-severity regime, while per-regime analysis identified the high-severity minority regimes as the priority for additional data collection, since within-regime R2 remains negative for every regime given current sample sizes. A sequence-aware mean absolute scaled error (MASE) analysis further shows that, despite the high global R2, none of the four pipeline stages currently outperforms a naive one-cycle persistence forecast on absolute error, a distinction reported here for transparency. The scalable architecture enables straightforward integration of additional material and process parameters, supporting iterative brake formulation development in industrial settings and, by reducing empirical testing requirements, sustainable brake material development with reduced replacement frequency and associated emissions.
Katakam, Abhishek, Eslamiat, Hossein, Kancharla, Sai Krishna, Filip, Peter
Physics-Informed Machine Learning Digital Twin of Electro-Mechanical Brakes for Clamp-Force and Friction Estimation2026-01-08069/14/2026
Recently, there has been a drastic shift in the industry towards wire architectures like steer-by-wire and brake-by-wire. For safe and accurate force control, diagnostics, and consistent performance over the operating envelope, accurate plant modeling of the Electro-Mechanical Brake (EMB) is important. Classical approaches involved linearized dynamic EMB models and the use of the characteristic stiffness curve for calibration at the operating points. These methods often perform poorly over regions where hysteresis, compliance, and friction are strongly nonlinear. Prior research on state or force estimation for EMB has focused on pad contact detection, thermal adaptation, and hysteresis-aware clamp force estimation. However, there are still accuracy gaps in practical applications during transients and under shifting friction regimes. In this work, a digital twin based on Physics-Informed Machine Learning is introduced, following the governing dynamics of the actuator-caliper assembly of EMB while learning (i) a physically significant parameter—system damping (Bsys) and (ii) a non-linear friction term constrained as a function of the actuator motion states and operating conditions. Non-linear friction is captured through gray-box friction formulation and learning unmodeled residual dynamics such as hysteresis and backlash. An EMB test stand is used to collect steps, ramps, holds/engagements, APRBS, and swept-sine excitations, with signals including time-aligned force command, motor torque/current, actuator position/velocity, and pad force measurement from a force sensor for model training. Results demonstrate a decrease in pad-force prediction error, along with non-linear and residual friction estimation. The resulting digital twin can enable sensor-less force estimation, friction compensation design, predictive analytics, and health monitoring through tracking parameter drift and friction signatures.
Rai, Prakhar, Gadhvi, Tirth
Bridging Human Expertise and Machine Intelligence in Chassis System Teardown Evaluation2026-01-08059/14/2026
Teardown evaluation of chassis system components plays a critical role in benchmarking, failure analysis, and competitive product assessment. These inspections rely heavily on experienced engineers who interpret visual defect patterns, material conditions, wear signatures, and manufacturing variations. However, expert driven evaluation processes are often subjective, difficult to standardize, and challenging to scale across global engineering teams. This paper presents a structured AI-assisted expert evaluation framework developed to enhance consistency, preserve institutional knowledge, and enable continuous improvement in chassis component teardown analysis. The proposed system integrates convolutional neural network architectures, including ResNet18 and its variants, into a human in loop inspection workflow. AI models perform initial classification of component images (e.g., OK/not OK and defect subclasses) and provide associated confidence scores. These predictions are presented as decision support, while final authority remains with the evaluating expert. Experts can confirm or override AI outputs, annotate defect regions using bounding boxes, assign subclass categories, and provide structured technical comments. All expert interactions, including AI disagreements, are systematically recorded. Correction instances are analyzed to identify model limitations, ambiguous defect conditions, and data gaps. Expert validated evaluations are incorporated into the training dataset to enable iterative model refinement. This closed loop process supports progressive improvements in model robustness and classification accuracy across varying teardown conditions and component types. A centralized cloud-based repository maintains full traceability of inspections, including timestamps, AI confidence levels, expert modifications, and annotation metadata. This structured knowledge capture converts tacit engineering judgment into a persistent digital asset, supporting auditability, cross-site alignment, and accelerated onboarding of new engineers. The framework demonstrates how AI can be effectively deployed as an assistive technology in chassis teardown evaluation, improving repeatability, enhancing data driven benchmarking, and enabling scalable knowledge preservation without displacing expert authority.
Divakaruni, Saikiran, Vaibhav, Veer, Hansen, Scott, Hood, Trevor, Agrawal, Rahul
Real-time Vehicle Speed Detection Based on YOLOv11 and DeepSORT2026-99-15959/11/2026
The technology of real-time and effective vehicle speed detection is considered a key technology to improve traffic monitoring efficiency and traffic safety management grade. To address the limitations of traditional speed detection schemes—including reliance on dedicated hardware, poor environmental adaptability, and high construction and maintenance costs—this paper proposes a r10eal-time vehicle speed detection system based on YOLOv11 and the DeepSORT algorithm. The proposed system uses the YOLOv11 target detection algorithm as its primary model. DeepSORT multi-target tracking technology is integrated to enhance tracking performance. Speed measurement is implemented using a virtual detection line. This approach enables accurate vehicle detection, continuous tracking, and real-time speed measurement within video frames. Through the experiments, the result shows that the improved YOLOv11n model reaches mAP@0.5 of 0.982 and a recall rate of 0.956 in the test set, higher than the YOLOv8n and YOLOv5s models. The speed detection error can be restricted to 3 km/h, satisfying the real-time detection need. There is no need for road surface modification, and the detection system has a flexible layout, providing a dynamic basis for traffic law enforcement and traffic data support for road construction and traffic control optimization.
Jin, Guowei, Ma, Wenlong, Jiang, Dali, Li, Nan
Research on Torque Allocation Control of Multi-Articulated Vehicle2026-99-11239/11/2026
The multi-articulated vehicle uses distributed drive mode. Due to its large degree of freedom of movement and the large number of driving shafts, different torque distribution methods affect the operational stability of the vehicle, how to coordinate and distribute the torque of each driving motor has become an urgent problem to be solved. To improve drive stability of the multi-articulated vehicles, propose a layered torque allocation control strategy. The upper-layer sliding mode controller determines the required additional yaw moments of each car body based on the linear reference model, the controller is characterized by swift response and a strong ability to resist interference. The lower-level allocation module comprehensively considers the torque output limitations of the electric hub motors, the prevailing road adhesion state, and the corrective yaw moment constraints given by the upper layer, and constructs an optimization objective function centered on the uniformity and stability of tire load. The optimal distribution of driving forces for each wheel is completed by solving this function dynamically. To validate the strategy's effectiveness, a vehicle dynamics model is built in the multi-body dynamics software ADAMS/View. Using a joint simulation framework integrating ADAMS/View and MATLAB®/Simulink, the effect of the layered control strategy is evaluated in comparative simulation with uncontrolled situation under U-turn and single lane change conditions. The simulation outcomes demonstrate that, compared to uncontrolled situation, the yaw rate deviation of each car body under the torque layered control are significantly reduced, and the adhesion utilization rate of tire is also effectively controlled, thereby the driving stability is improved.
An, Guanbo, Zhang, Liwei
Effect Evaluation of Emergency Barrier-free Blocking Methods in Highway Tunnels Based on Visual/Driving Behavior2026-99-15319/11/2026
To address the lack of safe and effective on-site vehicle blocking and control methods in the event of fires or other emergencies in extra-long tunnels — which can significantly reduce traffic safety risks and prevent secondary accidents — this study proposes a novel barrier-free light–smoke curtain interception method. The method integrates conventional traffic safety warning facilities (gantry-mounted variable message signs and audio–visual alarms) with two light–smoke curtain interception images to form a composite early-warning and interception system. Driving simulation experiments were conducted to comprehensively evaluate its warning effectiveness, interception performance, and operational safety in comparison with methods employing only traditional warning facilities or light curtain images. Furthermore, field drills were performed to validate its real-world applicability and interception effectiveness under both daytime and nighttime conditions. The main findings are as follows: 1) The fixation ratio and interception success rate associated with the proposed method were significantly higher than those of the other two methods, demonstrating enhanced visual attention and superior warning and interception performance. 2) The maximum deceleration observed with the proposed method was lower than that of the light curtain–only method and did not trigger emergency braking, thereby indicating high operational stability and driver comfort. 3) In field drills, after activation of the interception equipment, only one and two vehicles entered the tunnel under daytime and nighttime conditions, respectively, and full control of on-site vehicles was achieved within two minutes without any traffic accidents, verifying the system’s rapid response and effective safety assurance.
Shi, Mingjun, Li, Shicao, Wang, Haohuan, He, Qifei, Che, Zhengzhang, Li, Yanbo
Research on Heavy-Haul Train Operation Adjustment Considering the Degree of Chaos in Train Timetable2026-99-15229/11/2026
Heavy-haul railway development is as important a strategic direction for China’s railway sector as high-speed railway development. With the continuous expansion of heavy-haul railway operational mileage and the growing transportation demand, the complexity of operational adjustment in heavy-haul railways continues to escalate. The operational adjustment for heavy-haul trains subjected to speed restrictions following maintenance windows, aimed at maximizing the restoration of timetable regularity and guaranteeing freight volume, constitutes a critical and urgent research problem. Grounding on the operational principle of heavy-haul railways that prioritizes freight volume preservation over strict punctuality, this paper proposes a rescheduling model that incorporates the timetable disruption level. By adopting the order entropy theory, the model is formulated to minimize the total system disruption level and freight time cost, thereby framing the rescheduling problem as an order entropy optimization problem. The train disruption level is a metric that quantifies the perturbation intensity of a rescheduled timetable relative to the original operational plan within a given railway section. By initializing the entropy value of the pristine operational order to zero, any subsequent entropy variation directly facilitates a systematic investigation into the evolution of train operational order. Effective timetable adjustment strategies are subsequently derived, contingent upon the available buffer time and freight volume constraints. Guided by actual operational timetables and sectional line characteristics, this research designs a case study. The Gurobi solver is employed to obtain a rescheduled heavy-haul train timetable that successfully restores the original freight volume. The proposed methodology contributes to the enrichment of railway transportation organization theory and provides robust decision-making support for the strategic deployment of heavy-haul train services.
Fu, Lu, Lin, Li, Xu, Shichao, Ji, Guanggang, Li, Zheng
Study on Low-Velocity Impact Damage of Glass Fiber Reinforced Composite Pipeline2026-99-14169/11/2026
Through low-velocity impact testing, the effects of punch shape (conical, hemispherical, and cylindrical) and impact energy (5, 10, and 15 J) on damage characteristics in glass fiber composite pipes were investigated. Ultrasonic A-scan inspection was employed to detect internal delamination damage at the impact points within the composite pipes. Test results indicate that the contact area between the punch and the pipe is a key factor influencing the severity of pipe damage. A smaller contact area results in a higher energy absorption rate, greater punch displacement, larger area under the load-displacement curve, and longer contact time, leading to more severe damage characteristics. When the conical punch delivered 15 J of impact energy, the energy absorption rate of the glass fiber composite pipe reached 91.6%, exhibiting multiple damage characteristics, including pitting, penetration, and cross-shaped cracks. As impact energy increases, the area of internal delamination damage caused by the three punch shapes exhibits near-linear growth. The conical punch induces severe damage characteristics in the thickness direction but results in the smallest delamination area. Blunt-shaped punches (hemispherical and cylindrical) disperse impact energy over a wider region, leading to increased delamination damage area.
Wang, Xuan, Cao, Yanzhen
Research on a Brake Energy Recovery Strategy Based on Improved ANFIS2026-99-11349/11/2026
This paper addresses the issue of regenerative braking energy recovery in new energy vehicles and designs and optimizes a braking force distribution strategy. The strategy uses an ANFIS controller to dynamically optimize the proportion of front-axle regenerative braking force. The introduction of a pruning algorithm reduces computational complexity, thereby enabling a significant increase in mileage while maintaining stable driving performance. Co- simulations integrating Simulink and AVL Cruise, alongside Hardware-in-the-Loop (HiL) tests, the proof is that this strategy can still maintain excellent stability under different braking intensities. Moreover, it exhibits significantly higher energy recovery efficiency compared to benchmark strategies, while its effectiveness and real- time performance are successfully validated.
Lin, Hui, Zhao, Xuezhan, Tian, Jiahao
Analysis of Traffic Capacity at the Intersection of Xiyou Road and Qianshan Road in Hefei City2026-99-15519/11/2026
With the advancement of urbanization and the popularization of automobiles, the traffic load on urban roads is becoming increasingly heavy, resulting in many traffic problems. Road intersections serve as crucial linchpins in the urban transportation grid, wielding considerable influence over the overall traffic capacity of a city’s road network. Enhancing intersection efficiency and cutting down on delays stand at the heart of tackling urban congestion challenges. This study zeroes in on the crossroads where Xiyou Road intersects with Qianshan Road in Hefei City. Employing hands-on observation and photographic documentation, the research examines traffic flow and signal configurations during the peak demand period (7:30-8:30). The analysis evaluates traffic capacity and utilization rates for through, left-turn, and right-turn lanes at this intersection. Findings reveal that the right-turn lane at the southern entrance and the left-turn lanes at both northern and eastern entries show relatively low saturation levels, while the saturation of other lanes is greater than or close to 1. Therefore, this intersection does not have sufficient capacity. The actual traffic operation at the intersection, particularly during peak traffic times, is analyzed to identify the reasons for congestion Finally, improvement plans for optimizing traffic organization at intersections are proposed, such as optimizing signal timing schemes and transforming traffic channelization. Simulation analysis using VISSIM shows a 9.34% reduction in total intersection parking time, a 34.26% decrease in average queue length, and an 8.12% reduction in average vehicle delay. These results provide a reference for future optimization work, including intersection signal timing and channelization.
Wang, Yanmei, Wang, Chen, Fu, Ziyue, Meng, Xianglong
DisReID: Single Domain Generalizable Person Re-ID Method for Highway Construction & Operation2026-99-15309/11/2026
With the advancement of computer vision technologies and the widespread deployment of video surveillance systems, traffic safety and the development of intelligent highways have been significantly enhanced. As a key component of the intelligent video analysis module in smart highways, person re-identification (re-ID) addresses critical challenges, including cross-segment tracking of pedestrians illegally using emergency lanes, multi-camera joint searches for lost persons in service areas, and trajectory tracing of individuals involved in traffic accidents. These functions directly support the core goals of "safety assurance and efficient service" for smart highways. However, due to the complexity of the application scene, its generalization to unseen environments remains a core challenge. This problem is formally studied under the setting of Single-Domain Generalizable Person Re-identification (SDG re-ID), which aims to train a model on a single source domain that can perform well on arbitrary unseen target domains. To handle this issue, this paper proposes a novel Disentangled Augmentation re-ID Framework (DisReID) that disentangles and augments both structure and style. Specifically, DisReID consists of two modules: Structure-aware Viewpoint Simulation (SVS), a novel pre-processing technique that simulates cross-camera perspective changes by perspective transformation, diversifying geometric structure without harming identity semantics; and Style-Dominant Frequency Perturbation (SFP), which selectively focuses on the style-dominant frequencies and applies perturbation to enable controllable style augmentation while preserving structure cues. Furthermore, to alleviate the BN-induced domain bias, we introduce a simple yet effective test-time adaptation strategy, termed Cluster Fine-tuning (CF), that performs unsupervised clustering on target-domain features to assign pseudo-labels and subsequently fine-tunes the model, enhancing adaptability to unseen domains. Extensive experimental results on four public datasets demonstrate that our DisReID achieves superior generalization performance compared to the state-of-the-art methods. This work provides key technical support for the large-scale application of re-ID in smart highways, advancing the goal of "full-domain perception and intelligent collaboration".
Pan, Hong, Yu, Fangying
Convoy Size Effects in Virtual Coupling: Analysis of How the Number of Trains in a Convoy Influences Capacity and Delay2026-99-15219/11/2026
Rail transportation capacity is related to the number of trains that can be grouped within a convoy under a virtual coupling (VC) system. Grouping trains into larger convoys allows them to operate as a single train, reducing headway and increasing track usage compared with classic signaling systems. However, grouping trains has limitations, such as increased communication requirements between trains and stricter safety conditions. On the other hand, smaller convoys may offer less capacity increase but give more flexibility and operational resilience. Therefore, convoy size is an important parameter in balancing system performance. The research investigates the operational impacts of varying convoy sizes on route capacity and delay using the UIC 406 standard methodology. Simulation results indicated that larger convoys increase route capacity, particularly as trains transition into convoy formation faster. In contrast, there are increased delays. These conclusions demonstrate that convoy size optimization is important for increasing capacity and managing delays, revealing the practical benefits of a convoy management system.
Prompianpong, Nammont, Ketphat, Naphat
Spatial Sight Distance Value for Curved Tunnel of Mountainous Highway Based on Driving Behaviour2026-99-15539/11/2026
In order to conduct more in-depth research on the driving sight distance of curved tunnels in mountainous highways, a systematic theoretical calculation model of spatial sight distance of curved tunnels based on three-dimensional characteristics is established, and the spatial sight distance value of curved tunnels in mountainous highways is recommended in combination with the changes of driving behaviour under different spatial sight distances. Firstly, the concept of spatial sight distance of curved tunnels is proposed, the theoretical calculation model of spatial sight distance of curved tunnels is established, and the model is verified by a multi-scale neural network; Secondly, five UC-win/road simulation models of curved tunnel with different spatial sight distances are established, and the simulation experiments are carried out in combination with mp160 multi-channel physiological recorder and SMI etgtm eye tracker; Finally, the mathematical statistics method and SPSS software are used to analyse the operation behaviour, psychological behaviour and eye movement behaviour of drivers in curved tunnels with different spatial sight distances, and to verify the different effects of the critical value of spatial sight distance on driving behaviour in the theoretical calculation. Furthermore, by taking the spatial sight distance as the independent variable, the regression model is established with the average speed, trajectory offset, heart rate change rate, and pupil diameter change rate as the dependent variables. Based on the driver’s behaviour threshold, the recommended spatial sight distance of a curved tunnel is proposed. The results show that the recommended range of spatial sight distance of the curved tunnel of mountainous highway with a design speed of 80 km/h is 125 m to 140 m, the limit value is 110 m, and the appropriate value is 155 m. There is a critical value between two-dimensional sight distance and spatial sight distance, which has a significant impact on the change of driving behaviour in a curved tunnel.
Tang, Xie, Zheng, LiWen, Lin, GuoJin, Gao, YanYang, Lan, FuAn
Analysis of Latency-Influencing Factors in Vehicle-Road-Cloud Integrated Systems Based on Fault Tree Analysis and Analytic Hierarchy Process2026-99-15439/11/2026
Vehicle–road–cloud integrated systems have great potential in terms of improving traffic efficiency and achieving intelligent automatic driving through the integration of on–board terminals, roadside facilities, and cloud computing. However, their operational capabilities are heavily reliant on ultra-low-latency collaborative communication. This paper constructs a latency fault tree model to comprehensively analyze multi-source triggering paths of computation delay and reveals the formation mechanism of the delay path from “germination–induction–evolution”. On this basis, the Analytic Hierarchy Process is used to construct a three-level evaluation framework, and the influencing factors are quantitatively evaluated using NS-3 simulation data of the 004-V2X Communication Performance Testing Dataset. The result shows that the weight value of the network communication layer is the largest, 0.498, which shows that the bottleneck of performance in network communication is the wireless link quality. The cloud processing layer is second 0.327, which is dominated by the computational complexity and resource allocation policy. The impact of the onboard terminal layer is the smallest, 0.175. The FTA–AHP framework supported by empirical data can find the key factors affecting delay, which can help engineering optimization. It is noted that the AHP consistency check (CR) just checks the inner transitivity of expert judgment (i.e., the matrix consistency), while it cannot assure the objectivity and the bias elimination. We reduce the subjectivity by combining multiple experts, anchoring judgment with the simulation data, and performing a sensitivity check on the perturbation of the weights.
Xu, Yunchuan, Wang, Xiaomeng, Wang, Yan
Research on the Stability of Three-Axis Vehicles with Coordinated Steering under High-Speed Low-Adhesion Conditions2026-99-11369/11/2026
Three-axle vehicles are widely used in engineering, transportation, and other heavy-duty applications, but they are prone to lateral instability at high speeds or on low-adhesion road conditions, which severely degrades handling stability. To enhance their dynamic performance under extreme operating conditions, this paper proposes a direct yaw-moment control (DYC) strategy based on an incremental linear quadratic regulator (ILQR) for a distributed-drive three-axle vehicle equipped with active front-wheel steering (AFS) and differential drive assist steering (DDAS), thereby improving the accuracy and responsiveness of lateral stability control. Furthermore, to mitigate the mutual coupling and interference among multiple control subsystems, a coordinated steering strategy based on phase-plane analysis is proposed to achieve effective integration and dynamic coordination of AFS, DDAS, and DYC. Co-simulation studies conducted in Matlab/Simulink and TruckSim reveal that the proposed coordinated steering strategy substantially diminishes the peak yaw rate and vehicle sideslip angle across diverse driving conditions, thereby considerably enhancing the lateral stability of the three-axle vehicle during extreme maneuvers.
Hu, Jiadong, Wang, Tie
Structural Airworthiness and Mechanical Characteristics Analysis of Real-Time Flight Data Acquisition Equipment2026-99-14119/11/2026
The rising complexity of civil aviation and recent incidents, including Sichuan Airlines “14 May” and China Eastern Airlines “21 March,” highlight the urgent need for real-time flight data acquisition systems to support timely safety monitoring and early warning. Current methods rely mainly on post-flight data, while real-time monitoring remains constrained by limited bandwidth and insufficient device capabilities. This study presents a comprehensive structural and mechanical analysis of a real-time flight data acquisition device designed according to ARINC-600 standards and airworthiness regulations (ED112A, DO-160G). The device integrates data acquisition, storage, processing, and power modules, utilizing a Xilinx Zynq UltraScale™ platform for hardware-software co-processing, enabling high-speed data parsing, time synchronization, encryption, redundant storage, and secure transmission. Finite element modal analysis was performed to evaluate the structural airworthiness and vibration characteristics. The first ten natural frequencies range from 246.41 Hz to 1109.2 Hz, significantly exceeding typical aerospace excitation frequencies, effectively preventing resonance under operational conditions. Mode shape analysis indicates an evolution from low-order global bending and torsion to high-order local complex deformations, revealing relative stiffness weaknesses at panels and connection points, providing guidance for structural optimization. The study establishes a closed-loop framework connecting mechanical characterization, data security, and equipment reliability. Combined simulation and experimental validation ensures accurate assessment of dynamic performance, supporting operational robustness and airworthiness. The findings not only advance the development of high-performance real-time flight data acquisition systems but also enhance risk identification, early warning, and overall flight safety management in civil aviation.
Xu, Xiaodong, Gao, Jianwei, Guo, Qiang, Zhang, Yun, Kong, Xiangjun
Research on the Application of Multi-Source Data in Bus Route Network Optimization2026-99-15669/11/2026
This research aims to optimize the bus route network in Shiyan City using bus Origin-Destination (OD) data. By integrating multi-source data, including GDP, population, and bus OD big data from 2018 to 2022, short-term and long-term indicators are forecasted by time-series methods. Shiyan city is divided into 77 Traffic Analysis Zones (TAZs) considering its mountainous terrain, population-industry distribution, and urban planning. A conventional four-stage traffic demand model is applied, calibrated with bus OD data in 2021. The investigation reveals peak-hour bus passenger travel demand of 29,700 short-term and 31,800 long-term person-trips in the city center and key corridors. Bus passenger travel forms a four-vertical and five-horizontal layout in the short term, evolving to a five-vertical, five-horizontal, and two wings pattern in the long term with eastward urban expansion. Accordingly, an optimized and upgraded bus route improvement strategy is devised. In the short term, there are seventy existing bus routes that are adjusted, creating a five-layer bus route network with diverse functions. Long-term plans involve optimizing twenty bus routes and adding eight new routes to align with urban development. This research not only aids an integrated bus route network optimization framework using bus OD data in a time-consuming way, but also provides a sample of bus route network adjustment for a typical mountainous city.
Ye, Qian, Chang, Sheng, Shen, Yucan, Li, Tanfeng, Tian, He, Tian, Shimo, Cen, Jian
Research on the Synergetic Coupling Evaluation between the Yangtze River Delta Airport Cluster and the Regional Economy2026-99-15429/11/2026
To explore the coordinated development status between the Yangtze River Delta (YRD) airport cluster and the regional economy, this study takes the period from 2015 to 2023 as the research timeframe. It constructs an evaluation index system covering two dimensions: regional economy (including scale, structure, and benefit) and airport cluster development (including transportation scale, operation efficiency, among others). The Gini coefficient method and Pearson correlation coefficient method are used to screen indicators, while the entropy weight-standard deviation combined weighting method is adopted to calculate weights. Additionally, the coupling coordination model and geographical detector are integrated for in-depth analysis. The results show that the coupling coordination degree of the Yangtze River Delta region as a whole and its internal provinces and cities has rapidly recovered from the severe imbalance during the COVID-19 pandemic, featuring an inherent characteristic of “gradient catch-up and coordinated upgrading”. Factors such as the growth rate of passenger throughput and local fiscal general budget revenue have been identified as core influencing factors, and the interaction among these factors presents trends of two-factor enhancement and nonlinear enhancement. This study provides a theoretical basis and practical reference for promoting the integrated and coordinated development of the Yangtze River Delta airport cluster and the regional economy.
You, Zihao, Li, Yanwei
Assessment Model for Fitness for Work of Construction Personnel in Expressway Bridge Engineering Projects2026-99-15099/11/2026
To mitigate safety risks inherent in highway bridge construction, this research establishes a practical framework for assessing workers’ fitness for work. Using grounded theory, we analyzed interview records and documented accident cases through systematic coding, identifying critical indicators spanning physiological states, safety training effectiveness, and atypical behavioral markers. Rather than relying on single-method approaches, we combined Delphi expert consultation with entropy weighting to capture both professional judgment and data-driven variance, thereby reducing bias while preserving information richness. The resulting assessment protocol enables quantifiable classification of workers into distinct risk tiers. Implementation at the Zhangjinggao Yangtze River Bridge demonstrated the system's discriminatory power through field data collection and direct behavioral monitoring, successfully segmenting the workforce into low-, medium-, and high-risk categories. Results suggest the tool functions effectively as a pre-employment screening mechanism, allowing project managers to intercept potentially unfit workers before they enter hazardous work zones, consequently lowering the incidence of human-factor accidents.
Wu, Zhongguang, Dai, Junping, Ruan, Jing, Shi, Yonglong, Yuan, Zhenzhong, Hao, Jiatian
A Three-Dimensional Safety Domain Method for LNG Vessels in Ports Based on Human Factors and Speed Characteristics2026-99-15239/11/2026
To accurately assess the navigation safety status of LNG vessels in port waters and balance safety control with waterway capacity efficiency, this study constructs a 3D dynamic safety domain model for port LNG vessels, integrating human–ship–environment multi-factors. The model introduces the Weibull function to quantify the impact of drivers’ knowledge, skills, and physiological-psychological states on safety boundaries, combines a ship motion mathematical model to establish a 2D safety domain boundary equation, and incorporates hull subsidence to build a vertical dimension, forming a complete 3D model. Longitudinally, the safety distance is calculated using the car-following braking theory, while laterally, boundaries are determined by controlling the ratio of inter-vessel interference force to navigation resistance. Through static scenario analysis and dynamic simulation verification, results show that the safety domain scale is dominated by ship speed and environmental conditions, and its shape tends to shrink as the driver’s state improves, making it more suitable for actual port scenarios than traditional models. Verified with a specific LNG hub port as a case, the safety distance calculated by the model is significantly reduced compared with current specifications, while the delay impact rate and average delay time on other vessels are decreased. The research results establish a quantifiable framework for dynamic safety assessment, providing maritime administrations and on-board pilots with a scientifically-grounded tool to determine real-time safe navigation boundaries in complex port environments, balancing safety control with operational efficiency.
Wang, Yangang, Jia, Changsheng, Zhu, Jinshan
Train Delay Reduction Approach based on a Virtually Coupled Trains Following Model with Timetable Recovery Process: A Case of High-Speed Rail2026-99-15169/11/2026
One challenge in railway operation is how to achieve high levels of punctuality and reliability. However, especially in peak hour operation, a high volume of train traffic will affect timetables, which are more sensitive to the increase in travel time. The concept of virtual coupling has been introduced for controlling train movement mainly to increase capacity. As the operation under virtual coupling requires a short separation distance between trains, it might be applied to reduce the delay and recover the train timetable. However, there is no approach proposed detailing how the coupling is applied to reduce delay. In this paper, the virtual coupling state movement approach based on a vehicle following model with the coupling conditions determined to couple a group of trains for reducing or preventing secondary delay is proposed. The train operation under the proposed approach is simulated in MATLAB software, then applied to the hypothetical case, High-speed line, Bangkok - Nakhon Ratchasima, Thailand. The delay analysis is performed, and the waiting probability is determined to prove the effectiveness of the proposed approach. The simulation results show that trains will be virtually coupled with their front train as a form of train convoy when they cannot proceed at the ideal speed. Thus, operating train movement based on the proposed approach can reduce secondary delay and bring a train to arrive on time compared to the operation under the moving block control.
Chansong, Sukanya, Ketphat, Naphat
Research on Passenger Flow Prediction of Urban Rail Transit Based on Grey Improvement Model2026-99-15649/11/2026
In recent years, China's urban rail transit sector has undergone rapid expansion, with passenger demand consistently increasing. Accurate passenger flow forecasting is essential for ensuring efficient and safe metro operations. This paper takes Nantong Metro Line 1 as a case study and applies an optimized forecasting approach that integrates a grey metabolism model with the Holt double-parameter exponential smoothing method. Based on an analysis of Automated Fare Collection (AFC) data from March 2023 to February 2024, passenger flow on this line demonstrates a clear linear growth trend, which aligns well with the assumptions of the grey metabolism model. The results indicate that the optimized grey metabolism model not only significantly enhances prediction accuracy but also greatly reduces the variance ratio, demonstrating high reliability in forecasting outcomes. This improved methodology provides a more robust tool for metro operators in planning services, managing capacity, and optimizing resource allocation.
Fan, Fan, Zhao, Zeheng, Zhang, Jin, Ma, Junhao, Qian, Beiyue
SAE TOMORROW TODAY - Liability in the Age of Autonomous Vehicles135839/8/2026
When AI is driving, who's responsible? Listen in as we sit down with Jennifer Dukarski, Emerging Tech, Media, and Intellectual Property Shareholder at Butzel, to unpack the rapidly evolving legal landscape surrounding SDVs and AVs. From AI decision-making and over-the-air updates to data retention, cybersecurity, consumer education, and the growing complexity of liability, we explore how existing legal frameworks are being tested by next-gen mobility. Whether you're developing autonomous technology, managing risk, or shaping industry standards, this conversation offers an insightful look at the legal challenges that will define the future of transportation. Have your own thoughts on this topic? We'd love to hear from you! Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
Ultrasonic Guided Wave Thickness Measurement of Cortical Bone with Soft Tissue2026-99-12209/4/2026
Early diagnosis of osteoporosis is crucial for preventing fractures and improving the quality of life of patients. In clinical practice, the mainstream diagnostic methods, such as dual-energy X-ray absorptiometry (DXA), are limited by high equipment costs and ionizing radiation, resulting in a low coverage rate of large-scale early screening. Only less than one-third of brittle fracture patients have received a DXA assessment. To address this issue, this study proposes an innovative diagnostic method based on ultrasonic guided wave technology. This technology is cost-effective and portable, and it overcomes the limitations of X-ray detection in terms of its unsuitability for large-scale early diagnosis. The Young’s modulus and Poisson’s ratio of water are similar to those of soft tissue, so this method utilizes water coupling to simulate the environment of soft tissue around the bone and combines the transverse isotropy of cortical bone, which is an important characteristic that most existing models ignore, to analyze the propagation of guided waves in anisotropic cortical bone. Through the processing of ultrasonic signals using two-dimensional short-time Fourier transform (2D-STFT), the local thickness of cortical bone can be inverted. By establishing a fluid-coupled orthotropic anisotropic plate model, deriving the dispersion equation, solving the theoretical method for the dispersion curve, using the bovine long plate to construct a water-coupled detection platform, and obtaining experimental data to invert the thickness of the bone plate, the local thickness of the bone plate was obtained, proving that this method can effectively reconstruct the thickness changes of anisotropic and variable cross-section cortical bone under simulated soft tissue conditions, with an average relative error of 13%. This lays the foundation for subsequent in vivo experiments and provides a reliable solution for large-scale early osteoporosis screening.
Nong, Kexin, Li, Bing
A Novel Physics-Informed Graph Convolutional Reduced-Order Model for Fluid Flow on Unstructured Meshes2026-99-12459/4/2026
This study proposes a physics-informed graph convolutional reduced-order model, namely Phys-GCN, for high-fidelity and computationally efficient prediction of steady incompressible flow fields. In Phys-GCN, the incompressible Navier–Stokes equations are embedded into the loss function via residual constraints, such that the spatial feature extraction of graph convolutional networks is integrated with the physics-constrained learning strategy of physics-informed neural networks. This mixed design enables the model to capture complex nonlinear flow features while maintaining a clear level of physical interpretability. Benefiting from the node-edge encoding inherent to graph neural networks, Phys-GCN operates directly on unstructured CFD meshes to learn flow features from graph representations constructed using node attributes and adjacency relationships. In doing so, Phys-GCN dispenses with voxelization or SDF preprocessing and fully preserves the local geometric and topological characteristics of the flow domain. The proposed model is systematically evaluated on steady flows past circular and elliptical cylinders, where the predicted velocity and pressure fields are compared against reference CFD solutions in both interpolation and extrapolation scenarios. Results show that, for all physical quantities, the reconstructed steady flow fields achieve mean relative errors below 5%, exhibiting excellent agreement with the CFD benchmark solutions. After offline training, Phys-GCN achieves inference times that are several orders of magnitude faster than conventional CFD solvers, while maintaining comparable predictive accuracy. These findings demonstrate that Phys-GCN provides an accurate and efficient graph-based and physics-informed surrogate for steady flow-field reconstruction on non-uniform, unstructured meshes, thereby laying a solid foundation for future extensions to more complex three-dimensional and compressible flow configurations.
Xie, Haoran, Zhou, Hao, Yu, Changhao, Li, Qiang, Liu, Tianyu, Peng, Jiangzhou
Aerodynamic Load Calculation and Prediction Method for Folded Wing Mechanism2026-99-12899/4/2026
The folding wing mechanism is widely used in aircraft design. Whether the folding wing surface can unfold smoothly determines whether the aircraft can fly normally. Therefore, studying the aerodynamic loads and structural deformations during the unfolding process of folded wing surfaces is very important. The motion process of a folded wing mechanism is a typical fluid-structure interaction (FSI) process. During deployment, the wing surface moves under the combined action of the actuator’s pull and the aerodynamic loads from the incoming flow, while the large deformation of the wing surface during its movement, in turn, affects the aerodynamic loads on the mechanism from the flow field. Considering the FSI effects during the unfolded motion process of the folded wing, simulation was conducted using the ALE algorithm in LS-DYNA to obtain the kinematic and dynamic parameters in the unfolded motion process, and also to get the aerodynamic torque on the wing under different angles and angular velocities. In practical engineering applications, the actuation force of the deployment mechanism can vary due to factors such as the amount and performance of the pyrotechnic material. Consequently, the final velocity and the whole motion process of the wing mechanism will also change. For the calculation of aerodynamic external loads under multiple operating conditions, using the ALE algorithm will consume a large amount of computational time and cost. Given the high computational cost and long computation time of finite element simulations, a BP neural network was established to calculate the aerodynamic loads on the wing surface under different actuation forces. This allows for a rapid assessment of whether significant deformation or damage will occur to the folding mechanism or nearby components during the deployment process.
Wei, Ting, Li, Naitian, Tong, Zongkai
Experimental Study on Heat Transfer Performance of Gravity Heat Pipes in Green Highway Construction2026-99-12399/4/2026
Gravity heat pipe technology offers an innovative solution for utilizing shallow geothermal energy to melt pavement snow and ice in winter, aligning with the requirements of green highway construction. By leveraging the evaporation and condensation of internal working fluids, these heat pipes efficiently transfer underground thermal energy to the ground surface, delivering a continuous and stable heat supply for road pavements in cold weather. To explore the factors affecting heat transfer efficiency, this study built an indoor environmental simulation platform and systematically examined the impacts of heat pipe shape, working fluid type (R-134a, R245fa), heating temperature (15°C–25°C), and working fluid filling rate (15%–30%). A winter pavement snow- melting simulation experiment was conducted to quantify key indicators such as pipe wall temperature and heat transfer power under medium-low temperature conditions. Experimental results show that R-134a heat pipes outperform R245fa counterparts in heat transfer power under simulated shallow geothermal snow-melting conditions. Low filling volumes tend to induce temperature gradients in the condensation section of L-shaped heat pipes, reducing overall efficiency. Straight heat pipes work best at a 15% filling rate, while L-shaped models achieve optimal performance at 25%. Comparative experimental analysis yielded parameter-effect diagrams for heat transfer power and thermal conductivity, which clarify the variation rules of heat pipe performance and provide engineering guidance for gravity heat pipe applications in green highway construction.
Wang, Zhen-kun, Yuan, Zhi-ming, Wang, Kang, Zhang, Wen-jun, Wu, Xiang-song, Liu, Guang-bo
Johnson-Cook Model Calibration of 10# Steel and Dynamic Mechanical Behavior Analysis under High Strain Rates2026-99-12019/4/2026
As a typical material for fragmentation warheads, the mechanical behavior and ballistic penetration performance of 10# steel are critical for assessing warhead lethality. To characterize the dynamic response of 10# steel, systematic experiments were conducted, including quasi-static tensile tests, split-Hopkinson tensile bar tests, and thermal softening measurements. A = 505.46 MPa, B = 292.84 MPa, n = 0.335, C = 0.0343, and m = 1.213 are the calibrated Johnson–Cook parameters. Bridgman-corrected notched tensile tests determined damage parameters D1 to D4: 0.065, 0.746, −0.646, and 0.031). A study of its constitutive behavior shows that the strength of 10# steel increases with stress triaxiality and strain rate, whereas increasing temperature enhances ductility and reduces strength. Finite element software was updated to include the calibrated parameters to develop a material model for ballistic impact simulation. When compared with the ballistic penetration test results obtained using a 14.5 mm projectile, the simulated residual velocities show less than 5% deviation from the measured values. 3D scanning reveals that fragment sizes in experimental data differ by under 10% from simulation predictions. This work enables precise numerical simulations for warhead fragmentation prediction and lightweight armor design.
Tian, Yumo, Zhang, Longhui, An, Fengjiang, Feng, Bo
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