Browse Topic: Statistical analysis

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A Data-Driven Model for Predicting Casting Solidification Time and Mold Thermal Stress2026-01-50557/31/2026
This study proposes a data-driven surrogate modeling framework for predicting solidification time and mold thermal stress during low-pressure die casting (LPDC) of aluminum alloy wheels. The methodology employed an optimal Latin hypercube design (OLHD) to sample key parameters including cooling channel geometry and process conditions. A sequential simulation methodology combining ProCAST and Abaqus was implemented to generate a comprehensive dataset of solidification times and thermal stress distributions. Based on this dataset, surrogate models were developed using Support Vector Regression, Kriging, and Polynomial Response Surface Methodology, with their hyperparameters automatically tuned through Bayesian Optimization (BO). The optimized models were rigorously evaluated using four statistical metrics: Coefficient of Determination (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The evaluation results show that the BO–SVR model demonstrated superior prediction accuracy for both output responses and exhibited exceptional nonlinear fitting capability. This work establishes an effective modeling approach for simultaneous quality and efficiency optimization in wheel manufacturing.
Fuhao, Fan, Zhan, Yunlang, Zhan, Zhenfei, Yang, Yutong, Xiao, Yong, Huang, Shiyao
Advancing Reliability Centered Maintenance: A Robust Statistical Approach for MTBF Estimation in Aero-Engine Fleets via Subgrouping Strategy and Dual Weibull Modeling2026-99-02137/31/2026
Fleet heterogeneity, from manufacturing variations and diverse operating conditions, complicates reliability analysis by obscuring true failure patterns in aero-engines. This is a critical challenge in an industry as inaccurate Mean Time Between Failures (MTBF) estimates threaten safety and inflate operational costs, by forcing a choice between inefficiently conservative maintenance or the risk of in-service failures. Conventional analysis often fails by pooling all fleet data. To address this, our paper presents an analytical framework that improves predictive accuracy by filtering, rather than aggregating statistical noise. The methodology uses a Randomized Block Design (RBD) and ANOVA hypothesis test to screen a diverse dataset and isolate statistically homogeneous subgroups. This filtration identifies a core fleet with a consistent failure signature, providing a purified dataset for modeling. This refined data is then modeled using both Weibull and the Exponentiated Inverse Weibull distributions to ensure the results are robust and not model-dependent. Applying this framework to a 25-engine dataset that experienced 66 failures, we isolated a stable failure pattern, yielding a primary MTBF of 171.16 hours and a cross-validated MTBF of 176.35 hours. The close 3% convergence between these models validates our approach. By providing a dependable MTBF, this work establishes a stronger foundation for data-driven Reliability Centered Maintenance (RCM). It empowers maintenance planners to move toward evidence-based intervals, safely extending engine time-on-wing, optimizing spare parts inventory, and significantly reducing direct operational costs for airlines.
Jubaid, Mayin Uddin, Bebe, Gibson, Bigyen, Musa Pethuel, Anik, S M Kullul Mehedee, Yasmin, Ashrafi, Sahran, Mohamed Sideek Mohamed
Research and Application of the Requirement Maturity Model in Civil Aircraft2026-99-18547/17/2026
Civil aircraft, as typical complex product systems, exhibit characteristics such as a high concentration of high-tech technologies, strong interdisciplinarity, a high level of system integration, long development cycles, substantial project investments, and complex management. During the R&D process of civil aircraft projects, there are often high risks in performance, cost, and schedule. Delays in the schedule can lead to losses in project manpower and material resources, as well as project failure. A mature objective criteria system for maturity assessment provides a reference basis for determining whether the project has reached its optimal state at a specific stage, thereby reducing project management risks and increasing the probability of project success. This research will adopt a research approach combining theoretical studies with practical case analysis. First, it will conduct extensive and in-depth investigations into various maturity models and their applications across the entire product lifecycle within relevant fields. A requirement maturity model and requirement maturity KPI (Key Performance Indicator) indicators will be established to clarify the maturity status of requirements at different development stages, enabling judgment of whether the project is ready to proceed to the next development phase. Concurrently, by developing a KPI statistical system platform integrating application servers and data processing tools, a scientific and quantitative inspection mechanism will be implemented to visualize project development progress, status, and risk data. This will provide actionable insights for project decision-making and achieve effective project management and control.
Wang, Yi, Huang, Junkai, Zhang, Xinyu
The Chain of Visual Factors, Visual Characteristics, and Driving Behavior at Highway Tunnel Entrances2026-99-05567/10/2026
To explore the impact of guiding and warning visual combination factors at the entrance sections of highway tunnels on drivers’ visual characteristics and driving behavior, this study recruited 16 drivers to conduct on-road vehicle experiments at the entrance sections of the Yunling Tunnel’s left bore (with visual combination factors) and right bore (without visual combination factors). Seven visual characteristics and driving behavior indicators, including pupil diameter and vehicle speed, were collected and statistically analyzed. Representative indicators such as pupil diameter, standard deviation of fixation point position, and vehicle speed were selected to establish a trend surface model of visual characteristics and driving behavior. The results indicate that when driving at the entrance section of the left bore, drivers’ pupil diameter and fixation duration were significantly lower than those at the entrance section of the right bore. With the increase in the sweeping view angle, there was a more dispersed distribution of fixation points. Additionally, there were significant differences in the acceleration and lateral deviation of the driving vehicle, with the range of variation narrowing by 52.5% and 35.7%, respectively. The trend surface model results show that under the influence of visual combination factors, the reduction in drivers’ vehicle speed was smaller, and the impact of pupil diameter and standard deviation of fixation point position on vehicle speed was less pronounced. Overall, under the influence of visual combination factors, drivers’ visual characteristics showed significant changes, with improved speed control and manipulation levels, leading to more stable vehicle operation.
Ma, Yanpeng, Huang, He, Huang, Yong, Yuan, Chen
Who’s Riding? Machine Learning-Powered Rider Identification from E-Bike CAN Data2026-01-07787/1/2026
This study investigates the feasibility of identifying individual e-bike riders based on CAN data using machine learning techniques. Datasets from 12 test riders performing various predefined cycling tasks on a dynamometer test bench are collected and used to ensure controlled and reproducible conditions. The recorded CAN data includes various sensor signals, such as power output, cadence, torque, and the used support mode. After pre-processing, two different methods of feature extraction are tested and compared, one based on snapshots of the data and one based on driving events such as braking and accelerating, measured by calculating statistics of the riding data over sliding windows. A range of machine learning models is employed to classify riders based on their distinct riding patterns using the extracted features. The evaluated models comprise KNN, Random Forest and Naïve Bayes. The findings demonstrate the efficacy of machine learning in differentiating riders, with Random Forest and KNN achieving the highest and most robust accuracy among the tested models. The KNN-model achieves up to 99% accuracy, the Random Forest up to 75%. The paper analyzes the influence of different signals, feature extraction methods and model parameterizations on the results. The results show that machine learning-based rider identification using CAN data is a viable approach for enhancing e-bike security, authentication, and personalization. Potential applications include theft prevention, automatic user recognition for personalized assistance settings, and access control. Future research could include the exploration of the impact of additional sensor data, real-world outdoor conditions, and deep learning approaches, with the aim to further enhance identification accuracy and efficiency.
Simmann, Gabriel, Rauch, Yannick, Beißert, Florian, Kriesten, Reiner
Evaluating the Impact of Fuel Additives on Stochastic Pre-ignition Using Center for Quality Assurance (CQA) Approved Methodology03-19-03-00166/22/2026
Stochastic preignition (SPI) or low-speed preignition (LSPI) is an abnormal combustion phenomenon observed in downsized turbocharged direct-injection spark-ignition engines at highly boosted conditions. SPI results from the ignition of the air-fuel mixture from a fuel or oil droplet or a detached deposit before the spark discharge, and its occurrence can lead to extremely high peak pressures and severe knock, which can cause physical damage to the engine. This phenomenon limits the downsizing and boosting potential of direct-injection spark-ignition engines, thereby constraining the efficiency benefits that can be achieved. The propensity for SPI to occur is impacted by engine operating conditions as well as the properties of the fuel, fuel additives, lubricant, and lubricant additives. To mitigate its occurrence, it is important to understand the factors that impact the frequency of SPI events. As this abnormal combustion phenomenon is relatively recent, there was a lack of a standard procedure to detect the impact of a parameter on SPI frequency. This study details the development and validation of an engine dynamometer test procedure—the TOP TIER™ Standardized Dynamometer Test Method to Evaluate Additized Detergent Gasoline for SPI—approved by the Center for Quality Assurance (CQA), to evaluate gasoline additives for their impact on SPI. In this project, the newly validated SPI test protocol was used to compare the relative SPI tendencies of four TOP TIER™ fuel additives at maximum retail concentration against unadditized SPI test fuel, which served as the baseline. All four fuel additives were tested three times in randomized order. The results revealed that none of the TOP TIER™ additives tested had a statistically significant impact on the SPI rate.
Gopujkar, Siddharth, Davis, Richard, Worm, Jeremy, Tuma, Nic, Shukla, Prajwal, Reilly, Veronica, Chapman, Elana, Ciaravino, Joseph, Seyfried, Philipp
A Contribution to the Probabilistic Road Noise Simulation Using Non-Parametric Variability Modeling Abstract2026-01-07116/20/2026
An application of a Non-Parametric Variability Modeling (NPVM), as introduced by Pr. Soize, of a full vehicle road noise simulation, is an opportunity to highlight some applicative issues of such a stochastic approach. First, the convergence of the stochastic computations is considered by introducing the probabilistic modal density of the considered model as an indicator of the system intrinsic dynamic behavior. Since the probabilistic model induces a spread of modal frequencies, the upper range shows a lack of modes, deviating from the actual system modal density. The study of this deviation leads to the modal truncation criterion required to achieve a relevant probabilistic modal density in a targeted frequency range. The required margin in order to achieve a proper convergence of the probabilistic problems appears larger than expected. Then, using appropriate parameters, road noise simulation is investigated in the framework of the stochastic modeling. After the capability of the NPVM to handle the random loads requested for road noise, an FRF Based Sub-structuring approach is used to investigate different options for the loads application. Indeed, the complex behavior of the rotating tire/wheel system with the road is often handled by computing (or measuring) blocked forces or interface forces of a source sub-system (chassis) applied to a receiving sub-system (trimmed body). Cases where the receiving subsystems variability only, or both subsystems variability are considered, are investigated. It appears that, when variability applies to the full vehicle, using deterministic blocked forces may lead to erroneous results. When the trimmed body only is subject to uncertainties, deterministic interfaces forces lead to a reasonable approximation of probabilistic results. In both parts, theoretical derivations are illustrated by the results obtained from actual industrial models.
Gagliardini, Laurent, Glandier, Christian, Bauer, Eric, Straka, Andreas, Fiedler, Uwe
Unidirectional Carbon Fiber Tape Epoxy Repair Prepreg, Vacuum Curing Part 2 - Qualification Program for Fiber, Carbon Fiber Tape Prepreg, and Film AdhesiveAMS6885/2 (Current)6/10/2026
AMS6885/2 gives specific information about the qualification program for unidirectional carbon fiber tape epoxy repair prepreg capable of curing under vacuum for repair of carbon fiber reinforced epoxy structures. The prepreg system shall include an epoxy film adhesive to be applied in a co-bonding process with the prepreg for solid laminate and sandwich bonding.
AMS CACRC Commercial Aircraft Composite Repair Committee
Automated Flight Manoeuver Recognition for Intelligent Flight Simulators2026-26-07136/1/2026
The rapid growth in the number of aircraft and pilots emphasises the need for an AI-enabled training framework that can offer precise, automated examination of flight manoeuvres. This will be useful in optimising the pilot's training efficiency and minimising iterations of the conduct of flight manoeuvres, thereby reducing the training time of the pilot for a flight. A general framework is developed that can be used for all kinds of flight phases and aircraft types. A pre-trained machine learning model is designed using a supervised learning technique, Random Forest, to recognise different manoeuvres. Various statistical parameters, such as mean, standard deviation, kurtosis, skewness, etc., of several flight parameters were used as the input features to train the Random Forest classifier. In the present work, the classifier is trained using several actual flight test data manoeuvres, and is also supplemented with simulated manoeuvres. The achieved gross accuracy for manoeuvre recognition is approximately 96%. The developed Automatic Flight Manoeuvre Recognition module (AFMR) is integrated into the pilot-in-the-loop simulator for evaluation. This approach can be adopted for various aircraft programs for a wide range of flight manoeuvres to aid pilots in reducing their training time in the simulator as well as actual flights.
Sahu, Akash, C, Poornima, C, Aravindh, Kaliyari, Dushyant, TK, Khadeeja Nusrath
Integrating Human factors and Predictive Modeling for Enhanced Aviation Safety: A Data - Driven Approach to Risk Mitigation and System Design2026-26-07956/1/2026
This study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence modeling, model training, and interpretability analysis. The study demonstrates how this pipeline uncovers hidden relationships among operational errors, environmental influences, maintenance actions, design considerations, and crew behavior. The findings show that the integrated approach improves the accuracy and stability of risk prediction and highlights specific human factors patterns that consistently contribute to elevated risk levels. These insights support targeted mitigation strategies, inform design improvements, and help prioritize safety interventions. The work concludes that a combined human factors and predictive modeling framework enhances the ability of organizations to identify vulnerabilities earlier, allocate resources more effectively, and strengthen system resilience. This approach is adaptable to diverse aviation contexts and offers a practical path for transforming human factors data into actionable safety intelligence.
Valiyaparambil, Praveen
Using a System Reliability Model to Optimize Maintenance Costs A Best Practices GuideJA6097_201905 (Current)5/26/2026
SAE JA6097 (“Using a System Reliability Model to Optimize Maintenance”) shows how to determine which maintenance to perform on a system when that system requires corrective maintenance to achieve the lowest long-term operating cost. While this document may focus on applications to Jet Engines and Aircraft, this methodology could be applied to nearly any type of system. However, it would be most effective for systems that are tightly integrated, where a failure in any part of the system causes the entire system to go off-line, and the process of accessing a failed component can require additional maintenance on other unrelated components.
HM-1 Integrated Vehicle Health Management Committee
Research on the Statistical Method of Real Driving Emission Data of Commercial Vehicle under Low Load2026-99-17215/22/2026
Current emission regulation in China (National VI b) adopts the work-based window (WBW) method to statistically analyze PEMS experimental data. This method cannot fully account for experimental data under low load and cold start conditions. In light of this, this paper proposes a statistical method for low-load condition experimental data. Firstly, the adaptability of the WBW method to low-load condition experimental data is analyzed. Secondly, the representativeness and authenticity of statistical results from different methods are compared. The results indicate that when the power threshold of the WBW method is set at 20%, the effective window qualification rate in six experiments is less than 40%. And as the load decreases, the power threshold required to meet regulatory requirements needs to be further reduced, meaning more low-power data points are discarded. The WBW method eliminates many low output power data points with high CO and NOx emissions from test data on an urban road section with low driving speed, significantly underestimating the CO and NOx emission data under low load conditions, with NOx emissions 56.8% lower than the cumulative averaging (CA) method results. It is recommended to use the CA method for calculating CO and NOx emissions under low load conditions.
Tang, Gangzhi, Liu, Jiajun, Wang, Shuaibin, Du, Baocheng, Deng, Xuefei
Prediction of CO 2 Emissions from Light-Duty Vehicles Based on Highway Alignment2026-99-17135/22/2026
In order to allow for the precise prediction of the CO2 emissions of light-duty vehicles during the road design phase and to methodically examine the effect of road alignment on CO2 emissions, this paper classifies the operating conditions of light-duty vehicles according to Vehicle Specific Power (VSP) and the design speed of different road levels. The test vehicle’s environmental data and operational parameters under various road conditions were gathered using a Portable Emission Measurement System (PEMS). The CO2 emissions of the test vehicle under different operating conditions were statistically analyzed. Based on the road’s horizontal and vertical alignment, the road was separated into analytical units, including straight sections, longitudinal slope sections, horizontal curve sections, and curve-slope combination sections. The indicators of each analysis unit were used to anticipate the speed and acceleration of light-duty vehicles in each unit, and a model for forecasting light-duty vehicle CO2 emissions based on road alignment was developed. The results show that the predicted CO2 emissions based on road alignment have a relatively small error compared to actual emissions, indicating high model accuracy. This model enables relatively accurate predictions of CO2 emissions for light-duty vehicles on target road sections during the road design phase. Among the various road alignment indicators, slope has a greater effect on the test vehicle’s CO2 emissions.
Liang, Yao, Wang, Yixuan, Zhao, Xiaoyan, Cheng, Shenzhen, Wu, Bing, Zeng, Weiyi
Risk Assessment of Bunkering Process for LNG River Powered Ship2026-99-17225/22/2026
At present, with the rapid development of LNG powered ships, China’s LNG powered ships have formed a certain scale, but the speed of infrastructure construction such as bunkering stations restricts the development of LNG powered ships. In this process, “tank truck-to-ship bunkering”(TTS) has become one of the most widely used bunkering methods in China because of its flexible, fast and convenient characteristics, but there are many hidden dangers in the bunkering process. According to the characteristics of TTS, fault tree method is used to identify the risk of bunkering process, and the leakage of pipeline system is listed as the basic risk factor. The leakage probability of different aperture is analyzed by industry statistics. Three different leakage scenarios are selected and the consequences are simulated by PHAST software. The study shows that the failure of the valve and flange can easily lead to the leakage of LNG in the TTS process, and the leakage of the medium aperture and the full aperture will form the liquid pool, so some measures should be taken for the protection.
Dong, Yuanchao
CauExecutor: A Causal Executor for Causal Effect Estimation2026-99-07215/15/2026
Causal inference from observational data, particularly the estimation of a treatment’s causal effect on an outcome, has long been challenging, primarily because it hinges on correctly identifying confounders. This is typically accomplished in two main ways within causal inference frameworks: either by using causal discovery algorithms to recover the underlying causal structure through a causal graph, or by assuming that the relevant confounders are already known. Both approaches have been shown to be unreliable or simply infeasible in practical applications. Although large language models (LLMs) are advancing rapidly, their emerging capabilities in causal inference have only recently begun to receive significant attention. Nevertheless, LLMs currently lack the ability to directly interpret structured tabular data, which is widely used in causal inference. To address this limitation, we introduce a novel framework, CauExecutor, for causal inference. Our framework enables a novel combination of the semantic reasoning strength of LLM with the accurate estimation capacity of off-the-shelf statistical tools to more accurately estimate the causal effect from observational structured data. The CauExecutor first uses the semantic understanding and the reasoning power of LLMs to help find potential mediators and separate them from the confounders. It subsequently leverages off-the-shelf tools to programmatically handle tabular data and estimate causal effects by the optimized adjustment set. On several benchmark datasets, we observe CauExecutor outperforms all other LLM-based methods by correctly identifying more mediators and producing more accurate causal effect estimates. Additional experiments show that CauExecutor’s decision to disqualify mediators from the adjustment set, rather than qualifying any variable that meets the backdoor criterion, is beneficial to successfully minimizing mediator-induced bias and attaining improved estimation performance.
Yang, Jiaoyun, Chen, Jinxi, Yin, Yue, Liu, Li, Li, Lian, An, Ning
Application of the Actuator Line Method Coupled with the Lattice Boltzmann Method for Prediction of eVTOL Downwash/OutwashF-0082-2026-00045/5/2026
This study develops an efficient framework coupling the Lattice Boltzmann Method with the Actuator Line Method to evaluate the unsteady downwash/outwash of eVTOL aircraft. By incorporating a modified Prandtl loss function with geometric smearing correction, the framework accurately predicts velocity profiles and preserves unsteady vortices at lower computational costs than conventional RANS-based simulations. Analyzing three distinct eVTOL configurations sized for identical payload missions reveals that higher disk loading and multi-rotor interactions generate highly asymmetric, localized jet-like outwash structures, contrasting with the symmetric ground-level footprint of single rotor designs. Utilizing a 95th percentile velocity metric, transient peak hazards breach the regulated vertiport Safety Area boundary, extending up to 1.65 times the prescribed baseline limit. These findings demonstrate that time-averaged metrics underestimate physical hazards, highlighting the necessity for future guidelines to mandate configuration specific evaluations utilizing high-resolution unsteady flow data and robust statistical processing.
Lim, Juneyoung, Yee, Kwanjung
Passenger Acceptance of UAM Heave Acceleration in a Virtual Reality Motion-Base SimulatorF-0082-2026-00795/5/2026
For Urban Air Mobility taxis, passengers will experience different levels of heave motion during flight. Researchers at NASA Armstrong Flight Research Center conducted two studies in which passengers were exposed to varying levels of heave motion in the Armstrong Virtual Reality Passenger Ride Quality Laboratory. In the first study, twenty-three volunteers from the Armstrong workforce evaluated the motions on a five-point rating scale and a binary comfort scale; in the second study, fifty volunteers evaluated a flight experience with varying levels of stimuli using a five-point comfort scale and a five-point passenger acceptance scale. This paper combines the results of these two studies to observe the relationship between heave motion and passenger rider quality and acceptance. Both passenger comfort and acceptance were found to decrease with increasing heave acceleration The statistically significant relationship between the magnitude of heave acceleration and passenger comfort for individual studies and combined results are discussed.
Ramia, Saravanakumaar, Tzarnotzky, Uri, Hendrickson, Cory, Ross, Jeremy, Guy, Colette, Hanson, Curtis
Experimental Study Of Fuselage Spacing On The Development Of The Vortex Ring StateF-0082-2026-01685/5/2026
A wind tunnel investigation to assess the impact of rotor-fuselage spacing on the development of the Vortex Ring State and flow topology is presented. Particle Image Velocimetry was utilised to investigate flow mechanisms across a range of rotor-fuselage spacings and descent ratios, which were compared to that of an isolated rotor configuration. Mean flow data was used to identify coherent flow structures, whilst flow unsteadiness was investigated through statistical analysis of the velocity fluctuations. It was found at cases of Vortex Ring State onset, the presence of the fuselage delays the development of the Vortex Ring State for all rotor-fuselage separation distances tested. Furthermore, certain cases of rotor-fuselage spacings display a rotor-fuselage aerodynamic interaction that results in an increased effective descent ratio.
Croke, Alexander, Green, Richard, Watson, Gwilym
Characterization of Pedestrian Automatic Emergency Braking Acceleration in Modern Light Vehicles: Potential Implications for Occupant Safety09-14-01-00075/4/2026
The objective of this study was to characterize and compare pedestrian automatic emergency braking (PAEB) pulses in modern light vehicles to understand the loading environment that vehicle occupants are being exposed to during PAEB maneuvers. PAEB tests (n = 8008) conducted using 2018–2023 vehicle model years were analyzed. Pulse, vehicle, and impact characteristics (e.g., jerk, peak acceleration, pedestrian scenario, etc.) were derived from each PAEB test. Two k-means clustering analyses were used to group PAEB pulses with and without target collisions based on their similarity between characteristics. One-way ANOVA and Kruskal–Wallis tests were performed on the PAEB pulse characteristics to examine differences between clusters (p < 0.05). Two non-collision clusters (NC1 and NC2) were identified for PAEB pulses without collisions: NC1 had a statistically significant lower jerk (0.8 ± 0.4 g/s) and peak acceleration (1.0 ± 0.1 g) compared to NC2 (1.6 ± 0.8 g/s and 0.9 ± 0.1 g, respectively, p < 0.001). NC1 was mostly represented by stationary adult (88.6%), 60 km/h (99.5%), and 40 km/h (62.2%) tests. NC2 was mostly represented by crossing scenarios (child: 92.3%; adult: 70.5%) and 20 km/h (96.2%) tests. Three collision clusters (C1, C2, and C3) were identified for PAEB pulses with collisions. C3 showed a greater jerk (1.5 ± 0.8 g/s) compared to C1 (0.9 ± 0.6 g/s) and C2 (1.1 ± 0.9 g/s, p < 0.001). These results suggest that with successful avoidance, deceleration begins earlier with higher speeds and a stationary pedestrian, resulting in potentially milder loading conditions for vehicle occupants (i.e., lower jerk in NC1 vs NC2). With unsuccessful avoidance, in daytime pedestrian crossing scenarios, lower impact speeds were observed, resulting in potentially non-optimal loading conditions (i.e., higher jerk and peak acceleration in C3) for vehicle occupants. At night with low beams, C2 may result in advantageous loading conditions for vehicle occupants (i.e., lower jerk and peak acceleration), but it may lead to the worst outcome for pedestrians (i.e., greatest impact speed).
Witmer, Maitland, Kidd, David, Graci, Valentina
Increasing the Competency and Confidence of Older Drivers in the Use of Advanced Vehicle Technologies: The MOVETech Randomized Controlled Trial09-14-01-00135/4/2026
Programs that teach older drivers how to confidently and competently use advanced vehicle technologies (AVTs) are limited. The MOVETech study evaluated a training program specifically designed to teach older drivers how to use these technologies. Participants (n = 119) were randomized to the intervention (training program) or control group (brochure). The intervention involved an in-person classroom education session on the use and benefits of AVTs, and an on-road driving session where participants drove along a pre-defined route in a dual-controlled vehicle with instruction on AVT use by a driving instructor. All participants completed in-person and telephone assessments at baseline and 3 months. Driving performance and on-road AVT competence assessments were the primary outcomes. Self-reported driving confidence, competence, and confidence in use of AVT, crashes, citations, and count of vehicle damage were the secondary outcomes. Program fidelity was also evaluated using a checklist. At 3 months, overall driving performance was high (96/100) and similar between groups. The intervention group, however, had slightly higher competence in AVT use (77 vs 73), but the between-group difference was not statistically significant (4.14, 95% CI −4.85 to 13.13). There were no differences in secondary outcomes. Program fidelity was high for all classroom sessions but varied for on-road sessions due to external and environmental factors, which impacted how AVT was demonstrated. The findings indicate AVT competence and confidence may be improved by combining classroom and on-road sessions, and importantly, that this type of program is feasible and very well-accepted among older drivers. Future work could target drivers with new vehicles who are unfamiliar with AVT to determine potential real-world benefits. This study provides evidence for vehicle manufacturers and policymakers to explore efficient ways of providing support to older drivers with AVT.
Nguyen, Helen, Ren, Kerrie, Coxon, Kristy, Neville, Nick, O’Donnell, Joan, Cheal, Beth, Brown, Julie, Keay, Lisa
Regional-Specific Patterns of Abdominal Injury in Belted Male and Female Frontal Crash Occupants: Insights from Redefined Anatomical Mapping09-14-01-00025/4/2026
Research Question/Methods The study examined abdominal injuries of 87 belted occupants in CIREN frontal crashes for sex-based differences in abdominal injury patterns. It introduced a more anatomically detailed method for identifying injury locations in an abdominal-pelvic region that includes skeletal structures. The study introduces and applies a novel Abdominal New Injury Severity Score (AbNISS) to address limitations of traditional AIS coding in capturing sex-based differences in injury patterns. The operative reports/EDR/imaging data in CIREN cases enabled identification of sex-specific crash outcomes. The dominant analytical motif is Bertrand Russell’s knowledge by acquaintance and definite descriptions. Results Females had a higher rate of moderate to severe abdominal injuries than males: Only females sustained AIS 5 injuries, lumbar Chance fractures, posterior pelvic arch injuries, and more AIS 2, 3, and 4 injuries, with more injuries in superior-mid, left-superior, and medial-mid-abdominal zones. Males had more in the medial-inferior zone. 13 females had muscle ruptures. Four had combined muscle and Morel–Lavallée injuries, and four had Morel–Lavallée injuries alone. 13 of 14 males had muscle ruptures only. Pelvic morphology: Statistically significant (p < 0.05) sex-based difference in pelvic aspect ratios: Females: 1.37 ± 0.053, with the male ratio of 1.28 ± 0.079. By incorporating anatomical precision and enhanced injury mapping, it supports the development of more representative ATD/human body models. Discussion and Limitations Limitations of the study include its retrospective design, possible inconsistencies in clinical documentation, and challenges in applying AbNISS coding to non-CIREN datasets due to specificity constraints.
Halloway, Dale, Curry, William, Somasundaram, Karthik, Pintar, Frank
Real Turbulent Flow in Vehicle Aerodynamics: Overview of On-Road Measurements and Turbulence Generation in Wind Tunnels2026-01-50294/22/2026
Flow conditions on the road are quite different from the conditions used to develop vehicle aerodynamics. However, a significant amount of statistical data now exists that describes realistic road conditions. Some of these on-road flow characteristics can be replicated in wind tunnels. This paper reviews technical facilities designed to simulate on-road flow characteristics, such as turbulence intensity, turbulent length scales, and flow angle distribution. Reconstruction of a flow field that matches real road conditions is made possible by using active or passive turbulence generators within the wind tunnel. This review provides a comprehensive overview of these facilities, offering readers key insights into the challenges involved in replicating real-world flow conditions in wind tunnels.
Vondruš, Jan, Vančura, Jan
Temporal Knowledge Distillation for Sparse Stream DETR 3D Object Detection2026-01-00234/7/2026
Sparse Stream DETR 3D object detection has become pivotal in autonomous driving, and previous methods achieve remarkable performance by aggregating temporal information, which also face a balance problem of precision and efficiency. Knowledge distillation offers a promising solution to enhance the efficiency of a small model without incurring computational overhead; however, previous methods lack the exploration of the Temporal Distillation knowledge for the DETR detector. This paper designs a novel Temporal DETR Query Guidance paradigm to impart temporal relation knowledge from a powerful teacher model to enable the student to associate object states across time, leverage historical context. The teacher’s queries grasp the temporal knowledge through self-attention, and the backbone uses the EVA-02 large-scale image model. The student utilizes the teacher's self-attention layer and its own learnable queries to compute the attention as its guidance and mimics the feature interaction pattern within the teacher model by defining a temporal loss. Specifically, the student model uses its queries to aggregate the information in the key-value features of the teacher model to generate ideal queries and optimize the parameters of the student model by minimizing the L1 distance between the queries and the student model's own attention output. Experiments validate the efficacy of distilling temporal knowledge on the nuScenes dataset.
Yan, Yixiong
Optimizing Joint Efficiency in Stainless Steel-Aluminum Alloy FSW Joints through Advanced Parameter Control2026-01-02284/7/2026
The present study investigates optimization of ultimate tensile strength (UTS) in FSW of AA2024-T3 and SS304 in a butt joint configuration. An L18 mixed-level orthogonal array was used to design 18 experiments, varying tool rotational speed (450, 560, and 710 rpm), traverse speed (20, 25, and 40 mm/min), and pin offset (1 and 1.5 mm toward the Al side). The tool rotational speed had the greatest influence on UTS, contributing nearly one-third of the total variance, followed by pin offset and traverse speed. The optimal combination, 450 rpm, 20 mm/min, 1.5 mm offset, yielded a UTS of 344.7 MPa and a joint efficiency of 78.3%. At this setting, peak temperatures reached ~356 °C, ensuring sufficient plasticization and uniform mixing of the Al–SS interface, producing a refined stir zone with an average grain size of 4.2 μm. Fracture analysis revealed ductile failure at the optimal parameters, whereas suboptimal conditions resulted in brittle or mixed fractures due to either insufficient or excessive heat input. These results demonstrate that Taguchi optimization effectively correlates process parameters, thermal profile, material mixing, and mechanical performance, enabling reliable, defect-free dissimilar FSW joints for structural and aerospace applications.
Mir, Fayaz Ahmad, Khan, Noor Zaman, Pali, Harveer Singh
Survivability, Lethality, and Mobility Requirements of Military Vehicles Conducting Future Wet-Gap Crossings2026-01-02644/7/2026
Wet-gap crossings, which involve moving military forces across rivers and other water obstacles, remain among the most difficult operations to plan and execute. These maneuvers are complicated by choke points, fast-flowing water, and the exposure of forces and equipment to enemy fire. Despite these challenges, wet-gap crossings are critical to maintaining operational momentum during large-scale combat operations. This study examines doctrinal approaches to wet-gap crossings and explores the relationship between these operations and observed vehicle losses in the Russia-Ukraine War. Using a mixed-method approach, the analysis integrates daily operational reports from the Institute for the Study of War with visually confirmed equipment loss data from Oryxspioenkop. A custom Wet-Gap Relevance Score (WGRS) was developed using Natural Language Processing techniques to quantify the degree to which each ISW report focused on crossing operations. Statistical analysis shows that pontoon losses cluster within two days of major crossing events, confirming long-standing engineering doctrine regarding the vulnerability of bridging assets. However, the overall correlation between WGRS scores and total daily vehicle losses is weak, suggesting that broader attrition patterns obscure the distinct impact of crossing operations. These findings provide new empirical insight into how doctrinal principles manifest in modern conflict and underscore the design implications for future military vehicles. Effective wet-gap crossings require a diverse fleet: amphibious vehicles to establish bridgeheads, light vehicles that can be rafted to sustain momentum, and heavier vehicles that depend on bridging to continue the assault.
Lynch, Benjamin, Dosan, Logan, Mittal, Vikram
Data-Driven Objective Road-Driving Classification for Durable Design of Vehicles2026-01-01814/7/2026
Design for durability in the automotive industry depends on a clear understanding of how road surfaces and driving characteristics affect structural road loads and fatigue. Traditionally, road surface classification has been subjective (e.g., city, highway, rural), and done through driving instrumented vehicles over a small selection of roads. The variations in driving characteristics that are often consequent to the road surface quality are rarely accounted for in designing vehicle level durability tests. This makes it difficult to establish targets for durability testing that accurately match the wide variations in real-world roads and driving. This paper presents a data-driven approach to objectively classify road surface and driving characteristics using metrics derived from existing road response metrics like Vibration Dose Value (VDV) and statistical estimates of vehicle speed and acceleration. Data collected at the proving grounds on gravel roads, smooth roads, city-like roads, etc., is used to identify classifiers that categorize road-driving combinations into groups correlating with structural fatigue damage. This correlation between fatigue damage and road-driving classification is developed using Wheel Force Transducer (WFT) measurements from instrumented vehicles. This method shows promise to develop structural fatigue estimates directly from telemetry data. The method provides a path to replacing subjective road classification with a vehicle-sensor and signal-based, objective classification for developing durability targets and tests. This method is also scalable in terms of application on vehicle fleet data in uncontrolled environments, to develop an accurate understanding of real-world use of vehicles by customers.
Shaurya, Shubham, Ramakrishnan, Sankaran, Demiri, Albion, Khapane, Prashant
Revisiting Rear Seat Safety: Field Evaluations of Relative Injury Risk Across Seating Positions and the Integration of Advanced Rear Seat Belt Systems2026-01-05614/7/2026
Despite advances in crash avoidance, occupant restraint systems remain crucial in protecting the motoring public. Following decades of improvement in occupant protection, including several supplemental restraint systems for front seat occupants, the safety of rear seat occupants has recently undergone scrutiny. Studies evaluating rear seat occupant injury risk via field crash data have reported reduced relative safety in rear seating positions and alluded to advanced rear seat restraints, such as pretensioners and load limiters, as potential solutions. While the pursuit of novel technologies has historically improved occupant outcomes, evaluation of new systems in both controlled laboratory environments and field crashes is necessary to understand potential consequences of widespread introduction. This study analyzed the prevalence of advanced seat belts (load limiters and pretensioners) in the rear seating positions in the U.S. fleet. Additionally, occupant injury risk was compared across vehicles equipped with conventional and advanced seat belts in the rear seat, as well as between rear and front seat occupants, using data from state crash databases. The proportion of vehicles equipped with advanced rear seat restraints has steadily increased over the past couple of decades, but, as of 2023, remained below 10% of registered vehicles on US roadways. Evaluation of police-reported field crash data indicated that lap-shoulder belted rear seat occupants sustained fatal or incapacitating injury at a lesser rate than front seat occupants. Rear seat occupants have historically been and remain well-protected. Current field data do not consistently demonstrate a statistically significant reduction in rear seat occupant injury or fatality risk attributable to advanced seat belts. However, the relatively low number of crashes involving serious injury or fatality for rear-seated occupants in vehicles equipped with these systems underscores the need for additional data to fully evaluate their effectiveness in real-world crashes.
Rapp van Roden, Elizabeth Ann, Miller, Bruce, Pearson, Joseph, Williamson, James, Brown, Thomas
Effects of Child Anthropometry, Seatback Angle, Seat Rotation Angle, Seatbelt Force Limiting, and Collision Type on Injury Risk of Child Occupants in Highly Automated Vehicles2026-01-05704/7/2026
This study aimed to evaluate the influence of child anthropometry, seating postures (recline and rotation), seatbelt force limiting, and frontal collision scenarios on the kinematic response and injury risk in highly automated vehicles. The TUST IBMs 6YO-O model was conducted the frontal collisions in sled tests. This simulation matrix includes five percentiles six-year-old occupants (P3, P25, P50, P75, and P97), three seatback angles (20°, 30°, and 45°), four seat rotation angles (0°, 90°, 180°, and 270°), three seatbelt force limiting (2.6 kN, 3.6 kN, and 4.6 kN), and three frontal collision types. Injury risks were assessed including the child occupant's head, neck, chest/abdomen, and lumbar region in each simulation (n=540). The results indicate that the child anthropometry, the seatback angle, and the seat rotation angle have a significant influence on the motion responses. Statistically significant differences between all the groups within each independent variable category were observed based on the analysis of variance. As the child dimension increases, the risk of head injury decreases showing by HIC15, while the risk of neck and lumbar injuries increases. As the seatback angle increases, biomechanical parameters of the head show an increasing trend. The risk of upper neck injury decreases, while the risk of lumbar injury decreases and then increases. As the seat rotation angle increases, the risks of head, neck, and chest injuries initially rise and subsequently decrease, while the risk of lumbar injury demonstrates a downward trend. Seatbelt force limiting exhibited a positive correlation with head, neck, and lumbar injury risks. Consequently, small percentile child experiences higher head loads in smart cockpits, with seatback angle and seat rotation angle being key factors contributing to child injuries. These findings highlight the critical need to address the vulnerability of smaller children in smart cockpits by adapting integrated active and passive safety systems to mitigate their injury risk.
Wang, Yanxin, Zhao, Hongqian, Li, Haiyan, He, Lijuan, Cui, Shihai, Lv, Wenle
A Comprehensive Review of Aerodynamics and Safety of Sports Cars10-10-03-00203/31/2026
As motorsports evolve with technological advancements, aerodynamics plays a crucial role in race car performance. This review examines the impact of aerodynamics on car design and its evolution, presenting a statistical analysis of existing sports cars. We highlight key performance factors like engine power, top speed, drag, and weight. The key contribution of this review is the critical synthesis of the safety-performance trade-off, especially linking aerodynamic optimizations to the stability and safety of sports cars. Furthermore, we explore mathematical modeling of vehicle aerodynamics to enhance the understanding of performance aspects such as top speed, acceleration, cornering, and braking. This article also provides a review of recent active and passive aerodynamic devices to assist researchers in selecting designs, with an emphasis on the importance of ground effect. We also present recent numerical methods, particularly 3D simulations. The statistical data can help researchers determine optimal design parameters. Lowering drag enhances top speed, reducing weight improves acceleration, and increasing downforce shortens braking distance. Compared to passive devices, active aerodynamic devices offer greater adaptability, providing enhanced downforce and stability.
Eftekhari, Hesam, Al-Obaidi, Abdulkareem Sh. Mahdi, Eftekhari, Shahrooz
Human-Like Automatic Braking Algorithm Based on Kinematic Cue: Yield Behavior Analysis and Fuzzy Controller Design10-10-03-00192/25/2026
Pedestrians are among the most vulnerable participants in traffic, particularly when crossing the road. Extensive research has been conducted globally on the yielding behavior analysis of vehicle–pedestrian interaction and the design of automatic vehicle braking systems to mitigate pedestrian casualties. However, few studies have comprehensively addressed lateral risks using implicit kinematic cues in pedestrian–vehicle interactions. Moreover, the design of collision avoidance systems has rarely taken into account driving behavior, along with the pedestrian’s kinematics and crossing behavior. This article presents a human-like automatic braking fuzzy control strategy for pedestrian–vehicle collision avoidance, combining the advantages of professional driver emergency braking behavior and kinematic interaction cues. First, a high-fidelity driving simulator is used to investigate the yielding behavior of pedestrian–vehicle interaction when pedestrians cross the road. Second, the intrusion position (XP), as a new lateral risk index, is designed to overcome the limitation of lateral distance in complex pedestrian–vehicle interaction scenarios. Various metrics are considered to analyze driver emergency braking behavior using statistical methods from both lateral and longitudinal aspects. Subsequently, based on driver braking behavior, the human-like automatic braking fuzzy control strategy is proposed. Finally, simulation examples verify the reliability of the analysis results and the proposed controller’s effectiveness. Compared with a conventional automatic braking system, the timing of interventions of the proposed system is on average 2.9 s earlier, and the braking deceleration is reduced by 3.59 m/s2.
Zhang, Wenyan, Huang, Xiaorong, Sun, Shulei, Fu, Kairong, Xiong, Qing, Huang, Haibo
Business Reliability Growth for Automotive Engineering, Volume 4R-5522/17/2026
In a world where every business process is under pressure to perform faster, safer, and more reliably, this book delivers a powerful roadmap for sustained operational excellence. Centered on the proven methodology of Design of Experiments (DOE), it shows how organizations can move beyond reactive problem-solving to systematic reliability growth. From well-defined standard operating practices to management-level decision-making, the book connects strategy, data, and execution to create repeatable, measurable results across the enterprise. Readers are guided through practical, real-world applications of DOE, from selecting the right factors and levels to executing robust experiments, analyzing outcomes, validating solutions, and continuously monitoring performance. Each chapter translates complex statistical and engineering concepts into actionable business value, helping teams improve quality, reduce waste, and increase return on investment. Key capabilities explored in this book include: • Holistic reliability across design, manufacturing, supply chain, and marketing. • Advanced experimental designs, including split-plot and fractional factorial methods. • Fault tree analysis (FTA) and FMEA for failure prediction and prevention. • Supply chain optimization through multivariate process control. • Production and field reliability using design for testability and diagnostics. • Electric vehicle system and battery reliability analysis. • Marketing reliability driven by voice of customer and data-based value analysis. This book is an essential resource for engineers, operations leaders, and technical managers who want to build resilient systems, unlock innovation, and achieve long-term competitive advantage through disciplined, data-driven reliability.
Chiang, Young J.
Thoracic Responses of Rear-Seated Midsized Male Surrogates during Frontal Sled Tests2025-22-00112/13/2026
The purpose of this study was to evaluate the thoracic responses of the 50th-percenitle male Hybrid III, THOR, and post mortem human surrogates (PMHS) in the rear seat during frontal sled tests using conventional and advanced restraints in multiple vehicle environments. Twenty-one sled tests were conducted using the Hybrid III and THOR in seven vehicle bucks, and 12 PMHS sled tests were performed using four vehicle bucks. Trends in chest deflections between vehicles and restraint conditions were compared between surrogates. The Hybrid III and THOR thoracic injury risk predictions were compared to the thoracic skeletal damage observed during the PMHS tests. The Hybrid III chest deflections were statistically significantly greater for vehicles equipped with conventional restraints compared to those equipped with advanced restraints. The THOR chest deflections generally followed this trend, but the differences between restraint types were not statistically significant. Hence, the THOR thoracic response and injury predictions might be less sensitive to the presence of advanced restraints. The PMHS sustained lower chest deflections and less damage, on average for the vehicles equipped with advanced restraints. However, this outcome was confounded by PMHS variability, the effect of submarining on thoracic response, and other design differences between vehicles, leading to no statistically significant difference in chest deflection between vehicles with and without advanced restraints. All PMHS sustained at least nine rib fractures regardless of restraint condition, so it was difficult to assess the accuracy of the THOR and Hybrid III injury risk predictions. However, both ATDs accurately predicted a high injury risk for the vehicle that resulted in the most severe PMHS damage. These results support previous studies that suggest the implementation of advanced restraints may reduce injury risk in the rear seat. However, the results also suggest that other vehicle characteristics, apart from restraint type, influence thoracic injury outcomes in the rear seat.
Albert, Devon L., Bianco, Samuel T., Guettler, Allison J., Boyle, David M., Kemper, Andrew R., Hardy, Warren N.
Thread Inspection PracticesAS5870 (Current)1/29/2026
This document addresses AS8879 thread inspection issues relating to selection, usage and capability of gages. It addresses the selection of calibrated measurement gages, the need for defined quality metrics, the methodology of determining the appropriate guardband factors, and the minimum inspection requirements for single element pitch diameter gages. Users of this document shall apply the information described herein for the evaluation of the capability of their measurements based on the measurement consumer risk. It involves the analysis of the measurement (product) distribution and biases of both the product and measurement system distributions. It protects the consumer from the worst case distribution results. A whitepaper has been developed to provide supporting documentation and the rationale used in the development of this standard. This whitepaper will be published by the SAE as an Aerospace Information Report (AIR6553). This document recommends the use of ASME B1.2 “Gages and Gaging for Unified Inch Screw Threads” or IFI 301 “Gage Calibration Requirements and Procedures for Thread Gages” as guides for establishing the minimum calibration requirements for all types of thread gages.
E-25 General Standards for Aerospace and Propulsion Systems
Comprehnsive Measurement and Evidential Evaluation of Driver Drowsiness and Alertness Warning System2026-26-05201/16/2026
The paper aimed to improve the accurate quantification of driver drowsiness and to provide comprehensive, evidence-based validation for a Vision-Based Driver Drowsiness and Alertness Warning System. Advanced quantification of driver drowsiness is designed to enhance distinction of true positive events from False Positive and False Negative events. Methodology to pursue this included assessing inputs such as facial features, driver visibility, dynamic driving tasks, driving patterns, driving course time and vehicle speed. The system is programmed to actively learn Eye Aspect Ratio (EAR) reference and adapt personalised EAR threshold value to process EAR frames against the learnt threshold value. This method optimized the data frames to enhance the evaluation and processing of essential frames, thereby reducing delays in the processor and the Human-Machine Interface (HMI) warning module. Comprehensive validation is systematically conducted within a controlled test track environment to ensure precise execution of protocols, maintaining inputs closely aligned with real-time scenarios. The test methodology comprised the execution of pre-defined protocols that is steering robot and a technology-neutral procedure. Pre-defined protocols are scenarios created using the aforementioned assessing inputs. Cartesian coordinates of the system’s camera and driver eye point relative to the seating reference point (SgRP) are identified using a coordinate measurement machine (CMM) to measure the driver's position within the camera's field of view and mark the visibility zones. The protocols are executed with precision using a global navigation satellite system (GNSS), visual sensor, audio sensor and data logger. Subsequently, the system is tested with number of drivers trained on the Karolinska Sleepiness Scale (KSS) to conduct technology-neutral method for statistical analysis. Detailed analysis of the tested data, concluded with results and explored future prospects for quantifying driver drowsiness are discussed. The paper also discussed observations and challenges associated with the functionality of conventional systems and protocols currently deployed in the market.
Balasubrahmanyan, Chappagadda, Akbar Badusha, A
Addressing Safety Belt Discomfort among Female Driving Population through Data Collection and Simulation2026-26-00161/16/2026
The number of female drivers in India is increasing alongside the rapid growth of the Indian automotive industry. A driving comfort survey conducted among female drivers revealed that many of them experienced discomfort when wearing safety belts—while driving and as front-seat passengers. This discomfort is primarily due to a phenomenon referred to as “neck cutting.” The root cause of neck cutting is likely related to vehicle design, which is traditionally based on Anthropometric Test Devices (ATD’s) representing the 5th, 50th & 95th percentile (%tile) of the global population. However, a literature review indicated that the anthropometric dimensions of the Indian populations are generally smaller than those of the global for the respective candidate. To validate the neck-cutting issue, various female candidates were asked to sit in the Driver’s seat for physical measurements trials. Accordingly, methodology was developed to quantify neck cutting parameters objectively. A correlation study was performed to align virtual simulation results with physical trials outcomes, to fine-tune the virtual methodology. Based on the findings, few recommendations were suggested which were evaluated against its effect on existing relevant standards.
Kulkarni, Nachiket A, Chitodkar, Vivek V, Eknath Chopade, Santosh, Mahajan, Rahul, Yamgar, Babasaheb S
Use of Statistical Energy Analysis to Predict Articulation Index (AI) in a Vehicle Interior for Real-World Operating Scenarios2026-26-04301/16/2026
Vehicle interior noise is a crucial assessment criterion for automotive NVH. It has a significant effect on customer opinions about the quality of a vehicle. Articulation Index (AI) is one of the key sound metrics used to describe speech intelligibility and quantifies the middle and high frequency spectra associated to the internal noise of vehicle. In reality, Vehicle operating under dynamic condition experiences various air-borne noise sources such as tire rolling noise, powertrain noise, intake-exhaust noise & wind noise along with structure borne excitations such as powertrain vibrations, suspension vibrations. It is very challenging to predict cumulative effect of all these excitations to interior noise level and Articulation Index (AI) of vehicle over complete frequency range. The statistical energy analysis (SEA) is a well-known methodology being used to simulate & predict mid & high frequency noise. Objective of this paper is to present the process of development of a SEA simulation model designed to investigate vehicle interior noise & Articulation index and associated correlation against test measurements for various real- world operating scenarios. The SEA simulation model was meticulously developed with close attention given to structural representation which allowed to consider the structure borne excitations along with air borne noise sources during the analysis. The interior trims & sound insulation pack were also in detailed in the model. Both static & dynamic real-world operating scenarios of vehicle or load cases are demonstrated to validate the model against test measurements. The contribution study was performed to determine dominant noise sources and weaker transfer paths for improvement of Articulation index & interior noise quality of vehicle.
Doijad, Vishwajit Padmakar, Billade, Dayanand, Apte, Sr., Amol Arun, Shewale, Amol, Kothapalli, Brahmananda Reddy
Automated Evaluation of Drive File Simulation Accuracy2026-26-05281/16/2026
In the area of structural durability testing using servo hydraulic actuators, developing drive files for the actuators is a major step. Testing outcomes depend on ensuring the simulation accuracy of each drive file. These drive files are developed in an iterative process for different test track surfaces at different road and load combinations till the time we achieved better correlation. Evaluation of simulation accuracy of the drive files is an extensive manual review process making it time-consuming and resource-intensive. To address this challenge, an application has been develop to automate the comparison of actuator signals with predefined target signal files. This tool enables quick and accurate analysis of each drive file in a test run facilitating a comprehensive review of signal deviations. Each test run is having thousands of drive files based on road-load mix and actuator settings. This application helped us in significantly optimizing the simulation workflow by reducing the manual effort in reviewing thousands of files and statistical evaluation. The developed solution improves productivity and enhances quality in structural durability assessments using servo hydraulic actuators.
Soni, Yash, Katake, Vrishali, Mullapudi, Dattatreyudu, Chaskar, Mithun
Comprehensive Technology Framework for Effective and Early Leveraging of Test Assets2026-26-05271/16/2026
The automotive industry is undergoing a significant technological transformation, which is continually impacting the methods used to test the functionalities, delivered to end consumer. This includes the ever-growing need to embed software-based functions to support more and more end user functionality, while at the same time retaining existing and well-established functions, all within short development timelines. This presents both opportunities and challenges, with greater potential for reuse or leverage of test assets, although the actual percentage of leverage on real world projects is practically less than anticipated for a multitude of reasons. This paper collates the various factors which effect the practical leverage of test assets from one project to another, including various workflows and the interaction across components amongst applications lifecycle management systems. Alongside, it describes the current practices of basis analysis in isolation in combination with components of application lifecycle management (ALM) frameworks and their workflow across various levels of complexity products. During the analysis phase, few anti-patterns in the current approach are identified, leading to a shift in the paper’s focus towards introducing a novel approach that blends the basis analysis with re-defined means in using ALM frameworks. The novelty in this framework lies in applying a combination of various industry-leading concepts on keyword extraction, interaction matrix, blending the use of various mathematical co-efficient for basis similarity vs differences, the statistical evaluation of various combination of those in deriving the best fit for leverage of test assets. The resulting integration culminates in a very nuanced rule-based engine, which would seamlessly scale up from being an assisted framework to a fully automated framework, which enables in consistent and substantial leverage of test assets.
Venkata, Parameswaran, Kulkarni, Apoorva, RAJARAM, Saravanan, Ganesh, Chamarthi
Investigating the Impact of Social Media on Driving Safety: A Case Study in Hong Kong2025-99-025412/23/2025
Focusing on drivers in Hong Kong, this paper analyzes how social media usage contributes to inattentive driving and the associated safety consequences. Data were collected using a questionnaire-based survey and analyzed through chi-square tests, Fisher’s exact tests, and Cramér’s V effect size calculations to examine the relationships between demographic and driving-related factors—including gender, age group, education level, driving experience, and self-rated driving skills—and the level of high-risk perception. The findings reveal that gender, age, experience, and Self-assessed driving ability significantly influence drivers’ perception of high-risk situations. Furthermore, significant interaction effects were observed among these variables, indicating that they do not operate in isolation but rather interact to shape risk perception. For example, middle-aged and older female drivers with higher education levels and extensive driving experience demonstrated a heightened perception of high-risk scenarios, while increased driving experience was associated with improved high-risk perception among younger drivers. This study provides a systematic statistical analysis of how social media usage habits influence risk perception across different demographic groups, offering a theoretical foundation for the development of targeted safety interventions for high-risk populations. The results underscore the significance of accounting for the interaction between demographic and driving-related factors in designing effective strategies to mitigate distracted driving and enhance road safety.
Dong, Jinhai, Ye, Haocheng, Cui, Ziheng, Chen, Yang
A Methodology for Developing Quantitative Satisfaction Curves in Vehicle Design: Bridging Engineering Metrics and Occupant Perception2025-36-020512/18/2025
In vehicle development, occupant-centered design is crucial to ensuring customer satisfaction. Key factors such as visibility, access, interior roominess, driver ergonomics, interior storage and trunk space directly impact the daily experience of vehicle occupants. While automakers rely on engineering metrics to guide architectural decisions, however in some cases doesn’t exist a clear correlation between these quantitative parameters and the subjective satisfaction of end users. This study develops a methodology which addresses that gap by proposing the creation of quantitative satisfaction curves for critical engineering metrics, providing a robust tool to support decision-making during the early stages of vehicle design. Through a combination of clinics, research, and statistical analysis, this project outlines a step-by-step process for developing (dis)satisfaction curves, offering a clearer understanding of how dimensions like headroom, glove box volume, and A-pillar obscuration influence occupant perception. This project highlights the importance of aligning engineering targets with human-centered insights, enabling the delivery of more comfortable, user-focused vehicles. Ultimately, this research contributes to the development of a systematic approach for integrating subjective feedback into objective design criteria, enhancing overall product quality and customer satisfaction.
Santos, Alex Cardoso, Silva, Gustavo, Benevente, Rodrigo, Padua Silva, Antonio, Lourenço, Sergio Ricardo, Andrade, Cecilia Navas, Sobral, Piero
Elevation Data Features Analysis of Vehicle Targets Detected by 4D Millimeter-Wave Radar Based on Statistical Methods2025-99-031212/17/2025
Traditional traffic millimeter-wave radar can obtain the distance, speed, and azimuth angle of the vehicles driving on road plane, while lacking the elevation information of the targets which is an important feature in spatial dimension for vehicle type classification. In this paper, the statistical methods are used to analyze the elevation features of different vehicle types acquired by 4D millimeter-wave radar in actual road scenario. The statistical parameters of the overall elevation data and cross-section elevation data at different horizontal distances are calculated. Besides, the probability distributions and the skewness characteristics are further presented. The data analysis results show that there are significant differences in elevation probability distribution and skewness features between small and large vehicles, providing evidence for classification of different vehicle types using 4D millimeter-wave radar.
Jing, Mengyuan, Liu, Haiqing, Gong, Xiaolong, Guo, Fuyang
Empirical Study on the Evaluation of Natural Loss Standards for Liquid Petroleum2025-99-033612/17/2025
As a crucial part of national strategic resources, petroleum is an important basic material for economic development. However, during the storage, loading and unloading, and transportation of bulk liquid petroleum products, unavoidable natural losses occur due to factors such as process technology and equipment. Therefore, studying the natural loss of liquid petroleum during storage and transportation, and adopting effective countermeasures to minimize the natural loss of liquid petroleum, has become a topic of focus in various fields. This paper uses the “Loss of Bulk Liquid Petroleum Products” approved in 1989 as the analysis standard to explore the natural loss of highway oil transportation, conduct statistical test analysis on oil data such as oil collection registration forms, and propose conclusions and suggestions, thereby providing a reference for the revision of oil loss standards. The experimental results show that the overall oil data meets the national standard for natural loss of bulk liquid petroleum products, and suggests revising three standards, indicating that the standard setting is relatively scientific and reasonable.
Li, Bixin, Li, Jilai, Jin, Shifeng
Traffic Multi-Object Detection Method Based on YOLOv10n2025-99-045912/10/2025
To address the challenges of balancing detection accuracy and real-time performance in complex traffic scenarios for vehicle-mounted embedded platforms and road monitoring, this paper proposes YOLOv10n-FTAS, an optimized lightweight detection framework based on YOLOv10n. The main innovations include: (1) Designing a C2f-Faster-EAMA module in the backbone network that enhances feature representation through channel-spatial cooperative attention mechanisms; (2) Proposing a novel statistics-enhanced attention mechanism (Token Statistics-enhanced PSA, TS-PSA) by integrating Token Statistics Self-Attention; (3) Constructing a Dynamic Sample-Attention Scale Fusion module (DS-ASF) that achieves multi-scale feature fusion through deformable convolution and adaptive sampling strategies; (4) Adopting Shape-IoU loss function with geometric constraints to optimize bounding box regression. Experimental results demonstrate: The improved model reduces parameters and computations to 5.5M and 5.8G respectively, representing 5.17% and 13.4% reductions compared to the baseline. It achieves 90.3% precision, 92.5% mAP@50, and 70.6% mAP@50:95%, showing improvements of 2.15%, 4.52%, and 2.63% respectively. This solution effectively resolves detection deviations in dynamic complex scenarios while providing high real-time performance.
Niu, Jigao, Jin, Kunming
Quantifying Naturalistic Changes in Occupant Postures in Belt-Positioning Booster Seats Utilizing Pressure Mats2025-22-000812/2/2025
Belt-positioning booster seats (BPBs) help promote proper seat belt fit for children in vehicles. The effectiveness of BPBs depends on occupant posture, which can be influenced by BPB design features. This study aimed to quantitatively describe how children's postures naturally change over time in BPBs, using pressure mats. Thirty children aged 5 to 12 participated in two 30-minute trials using randomly assigned seating configurations. Five configurations were studied by installing two backless BPBs in vehicle captain’s chairs, varying booster profile (high, low, or no BPB) and armrest presence (with or without BPB/vehicle seat armrests). TekScan 5250 pressure mats were placed on the seating surfaces. Children began in an ideal reference posture, and center of force (COF) data were collected continuously. Additional observations on posture, behavior, and comfort were periodically collected. Mixed models, including effects of seating configuration, time, and volunteer characteristics, were used to explore changes in COF position from the reference position with time. Children assumed a variety of postures. Over time, children showed a statistically significant forward COF shift of 2.5 cm from the initial posture across all trials (p = 0.003). No significant differences were found in the average COF position or translation between seating configurations in the fore-aft (x) or inboard-outboard (y) directions. However, the maximum and cumulative COF translation in the x-direction was significantly influenced by booster profile, with high-profile configurations resulting in the least amount of translation. Children tended to slouch over time, as evidenced by an average forward COF translation of 2.5 cm over thirty minutes. These findings were supported by video footage and posture data. Trends toward forward COF translation were most apparent in low-profile and no booster configurations. Such changes in booster occupant postures can imply increased injury risk, specifically associated with submarining as evaluated in previous computational investigations. Future research should examine these trends in real-world driving environments and assess how specific BPB design elements may support better long-term posture during vehicle travel.
Connell, Rosalie, Baker, Gretchen H., Mansfield, Julie A.
Enhancing Aircraft Engine Part Inspection Using a Handheld 3D Scanner01-18-03-001311/17/2025
Accurate defect quantification is crucial for ensuring the serviceability of aircraft engine parts. Traditional inspection methods, such as profile projectors and replicating compounds, suffer from inconsistencies, operator dependency, and ergonomic challenges. To address these limitations, the 4D InSpec® handheld 3D scanner was introduced as an advanced solution for defect measurement and analysis. This article evaluates the effectiveness of the 4D InSpec scanner through multiple statistical methods, including Gage Repeatability and Reproducibility (Gage R&R), Isoplot®, Youden plots, and Bland–Altman plots. A new concept of Probability of accurate Measurement (PoaM)© was introduced to capture the accuracy of the defect quantification based on their size. The results demonstrate a significant reduction in measurement variability, with Gage R&R improving from 39.9% (profile projector) to 8.5% (3D scanner), thus meeting the AS13100 Aerospace Quality Standard. Additionally, the 4D InSpec scanner improved detection accuracy, provided automated defect quantification, and eliminated the need for time-consuming replication processes. Beyond performance improvements, the adoption of the 4D InSpec scanner led to a 75% reduction in direct labor time, significant cost savings, and the elimination of ergonomic risks and human error associated with traditional inspection methods, and enhanced defect reporting and data collection. The article closes with implementation requirements and areas for future improvement.
Aust, Jonas, Donskoy, Gene
Comprehensive Warranty Data Analytics: A Blend of Conventional & Advanced Methods2025-28-029911/6/2025
In today’s competitive landscape, industries are relying heavily on the use of warranty data analytics techniques to manage and improve warranty performance. Warranty analytics is important since it provides valuable insights into product quality and reliability. It must be noted here that by systematically looking into warranty claims and related information, industries can identify patterns and trends that indicate potential issues with the products. This analysis helps in early detection of defects, enabling timely corrective actions that improve product performance and customer satisfaction. This paper introduces a comprehensive framework that combines conventional methods with advanced machine learning techniques to provide a multifaceted perspective on warranty data. The methodology leverages historical warranty claims and product usage data to predict failure patterns & identify root causes. By integrating these diverse methods, the framework offers a more accurate and holistic understanding of warranty data, enabling manufacturers to make efficient data-driven decisions that improve product quality and customer satisfaction. Conventional tools, like statistical analysis and basic data mining, provide foundational insights into warranty trends. However, advanced techniques such as machine learning and predictive analytics enable detection of potential issues in a more efficient manner. This approach supports decision-making processes related to product design, manufacturing, and quality control, leading to overall improvements in product reliability. In this paper we will illustrate the application and effectiveness of this hybrid approach using sample data, highlighting significant advantages such as deeper insights into failure mechanisms which in turn helps in more effective warranty management strategies in combination with the conventional analytics techniques. Ultimately, this paper underscores the potential of a comprehensive warranty data analytics system that integrates both traditional and modern techniques, efficiently driving continuous improvement in product development and customer services.
Quadri, Danishuddin S.F., Soma, Nagaraju
Smart Fleet Management for Mechanical Tillage Implements2025-28-031211/6/2025
Tillage, a fundamental agricultural practice involving soil preparation for planting, has traditionally relied on mechanical implements with limited real-time data collection or adjustment capabilities. The lack of real-time data and implement statistics results in fleet managers struggling to track performance, driver behavior, and operational efficiency of the implements. Lack of data on vehicle performance can result in unexpected breakdowns and higher maintenance costs, ensuring compliance with regulations is challenging without proper data tracking, potentially leading to fines and legal issues. Bluetooth-enabled mechanical implements for tillage operations represent an emerging frontier in precision agriculture, combining traditional soil preparation techniques with modern wireless technology. Implement mounted battery powered BLE (Bluetooth Low Energy) modules operated by solar panel based rechargeable batteries to power microcontroller. When Implement is operational turns module active and establishes communication with BLE capable wireless controller to share implement statistics and parameters. Sensors like magnetic pickup rotary shaft speed and accelerometer sensors interfaced with module to acquire implement working shaft speed, depth of implement operation in field. Based on dynamic data collected by module such as hours of usage, Trip hours, Oil change alert, maintenance alerts made available to the fleet managers. BLE module transmits acquired data to the tractor mounted wireless controller which makes data available on cloud, to fleet managers using dedicated applications to track all tillage implement statistics. Also, this solution helps implement manufacturers to track implement usage and avoid false warranty claim issues.
Kaniche, Onkar, Rajurkar, Kartik, Gokhale, Sourabha, Vadnere, Mohan
Predictive Maintenance Solution with Real-Time Insights and Cost Estimation for Machine Health Management2025-28-030211/6/2025
This paper introduces a comprehensive solution for predictive maintenance, utilizing statistical data and analytics. The proposed Service Planner feature offers customers real-time insights into the health of machine or vehicle parts and their replacement schedules. By referencing data from service stations and manufacturer advisories, the Service Planner assesses the current health and estimated lifespan of parts based on metrics such as days, engine hours, kilometers, and statistical data. This approach integrates predictive analytics, cost estimation, and service planning to reduce unplanned downtime and improve maintenance budgeting, aligning with SAE expectations for review-ready manuscripts. The user interface displays current part health, replacement due dates, and estimated replacement costs. For example, if air filter replacement is recommended every six months, the solution uses manufacturer advisories to estimate the remaining life of the air filter in terms of days or engine hours. It also suggests replacement dates, suitable part options, replacement costs, and available service slots through an operator guidance mobile app and portal. The solution features a 360-degree view of the machine or vehicle, providing detailed information on each part and allowing operators to interact with and select parts of interest. An integrated cost estimator offers users estimated service costs and availability at authorized service centers, using a centralized part database. This solution empowers customers to monitor machine health, gain a better understanding of their machines, and receive service advisories to prevent breakdowns and downtime. Additionally, the cost estimation feature aids in better planning and budgeting for maintenance.
Chaudhari, Hemant Ashok
Ensuring The Reliability of Hydraulic Powered Steering System Through Life Data Analysis2025-28-029711/6/2025
The reliability of vehicle steering systems is extremely important to ensure safety, vehicle performance and gain customer satisfaction. Life data analysis conducted to analyze how the steering systems are performing in the field and assess whether the steering systems can meet the reliability target when deployed in the field. This article discusses about the systematic process to conduct the field data analysis of Hydraulic Powered Steering System (HPS) from the warranty claim data, usage of Weibull distribution to derive the life characteristic parameters. Based on the process described in this article, the statistical analysis of the warranty claim data performed and identified that, “the Hydraulic Power Steering Gears demonstrated more than 99% reliability in the field with statistical confidence of 90% and able meet the ZF’s Internal target for the HPS Systems”.
Ravindran, Mohan, Sugumar, Ganesh
Advanced Compliance Risk Assessment Using Big Data from Fleet Intelligence Data2025-28-034311/6/2025
In-Use emission compliance regulations globally mandate that machines meet emission standards in the field, beyond dyno certification. For engine manufacturers, understanding emission compliance risks early is crucial for technology selection, calibration strategies, and validation routines. This study focuses on developing analytical and statistical methods for emission compliance risk assessment using Fleet Intelligence Data, which includes high-frequency telematics data from over 500K machines, reporting more than 1000 measures at 1Hz frequency. Traditional analytical methods are inadequate for handling such big data, necessitating advanced methods. We developed data pipelines to query measures from the Enterprise Data Lake (A Structured Data storage system), address big data challenges, and ensure data quality. Regulatory requirements were translated into software logic and applied to pre-processed data for emission compliance assessment. The resulting reports provide actionable insights on NOx sensor activity, engine warmup operations, high-risk drive cycles, and load profiles across different operation regimes. This approach significantly reduces the reliance on costly and labor-intensive physical testing with Portable Emissions Measurement Systems (PEMS) by integrating advanced analytical methods into the workflow. By leveraging high-frequency telematics data, this method enables engineers to identify failed machines in the field more efficiently. It also provides valuable insights and reasoning behind these failures, facilitating quicker and more informed decision-making. This not only enhances emission compliance monitoring but also optimizes resource allocation and reduces overall regulatory risks. In summary, the developed methods enable effective emission compliance monitoring, reduce regulatory risks, and help optimize calibration strategies by understanding customer usage patterns. These methods are scalable for various emission regulations.
Arya, Satya Prakash, Shekarappa, Kiran
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