Noise pollution is a major environmental and health challenge, yet its strong spatial and temporal variability makes comprehensive mapping highly complex. Current approaches under the European Noise Directive (END) provide only partial coverage and often lack temporal dynamics. The NoiseSphere project, funded by the Austrian Research Promotion Agency FFG, develops an AI-based methodology for dynamic, large-scale noise prediction and mapping. A machine learning model is trained on heterogeneous data sources, including semantically enriched open Sentinel-2 satellite imagery, OpenStreetMap road data and existing noise maps. The model is refined through integration of noise emission data and validated using targeted in-situ measurements. A case study in an urban environment (Graz, Austria) demonstrates the model’s applicability. By combining remote sensing, traffic dynamics, and machine learning, NoiseSphere enables predictive noise mapping even in regions not covered by current

Girstmair, Josef

AI-Based Package Tray Design Method for Controlling Acoustic Modes in a Sedan

2026-01-0716

6/20/2026

In this study, we propose a methodology for predicting the acoustic modes and natural frequencies of a sedan using artificial intelligence and demonstrate the feasibility of controlling its acoustic characteristics by modifying the hole distribution of the package tray. In typical sedan structures, the cabin cavity and trunk cavity are acoustically coupled through holes in the package tray. The distribution of these holes significantly affects the natural acoustic modes and frequencies of the vehicle. However, once the exterior shape of the vehicle is finalized during the design stage, options for structural modifications to mitigate noise issues caused by these modes become extremely limited. To address this challenge efficiently, we develop a deep learning-based neural network model trained on data derived from a simplified acoustic analysis model of a sedan that includes a package tray. Finite element analysis is performed to generate acoustic modes and natural frequencies, which

Lee, Jin Woo, Cho, Jaeho, Nam, Younsic, Han, Yongha

Joint Estimation of Inputs and States for Multi-Axis Vibration Environment Testing

2026-01-0683

6/20/2026

Monitoring inputs and states of a structural dynamic system is often challenging, as direct measurements are costly or even infeasible. A virtual sensing methodology is presented for jointly estimating the input and state of a structure when subjected to multi-directional base excitations. The approach uses a tuned Kalman Filter combined with a model-order reduction of the system model to ensure a low computational cost whilst allowing accurate estimation from a limited number of acceleration measurements. This enables real-time virtual health monitoring strategies and reduction in instrumentation during data acquisition without additional information such as location and direction of application about the inputs. The proposed methodology is validated numerically and experimentally using a notched aluminum beam excited on a multi-directional shaker table, driven simultaneously in two in-plane directions. The study demonstrates accurate full-field estimation of multiple responses along

Salazar Colunga, Rodrigo, Pandiya, Nimish, Dindorf, Christian, Naets, Frank

Best-in-Class NVH Optimisation for Electric Powertrains, Holistic Front-Loading Approaches

2026-01-0689

6/20/2026

Achieving best-in-class Noise, Vibration, and Harshness (NVH) in electric powertrains demands a paradigm shift in development methodology. This paper presents a practice-oriented overview of simulation methods in NVH development methodology for electric drive units. This includes target cascading and multi-objective optimisation, and by attacking NVH at the source using KPIs early in the design cycle, significant reductions in development time and reliance on traditional testbed loops are realised. Machine learning (Neural Network) algorithms are utilized to find the best-in-class design, using multi-objective optimisation as well as refining simulation accuracy by adding tolerance effects while target cascading ensures alignment of system-level performance objectives down to subsystem contributions. Combined, these strategies enable rapid and robust NVH optimisation, using simulation for next-generation electric powertrain development. Several applications and real-life examples

Mehrgou, Mehdi, Garcia de Madinabeitia, Inigo, Graf, Bernhard, Gojo, Josef

Towards Realistic Stick-Slip Testing: Efficiently Mapping Material Behavior across a wide Range of Loading Conditions

2026-01-0713

6/20/2026

To minimize noise caused by interior components rubbing against each other, automotive materials are usually tested in advance with the established stick-slip method according to VDA standard 230-206. This procedure is widely used for soft materials, upholstery and plastics. However, it is limited to constant climatic and selected loading conditions. Contrary, in real application, changing climates and dynamic excitations can nevertheless trigger noise issues even in materials rated as suitable in the prior tests. To address this gap, a new test method has been developed that evaluates the stick-slip behavior of material combinations for a wide range of loading and climatic conditions. Conducted in a climate chamber with a standard stick-slip test bench, the procedure applies sinusoidal excitations, dynamic climatic shifts and advanced data analysis. In addition to the usual results the new method also evaluates realistic scenarios such as starting a vehicle in different seasons or

Fritz, Susanne, Strangfeld, Martin

Automating Component NVH Characterization: A Systematic Approach for Component Test Bench Characterization

2026-01-0704

6/20/2026

Vehicle electrification and accelerated development cycles create a need for virtual Noise, Vibration and Harshness (NVH) development tools which are fast, precise and, seamlessly interchangeable between development sites, suppliers and OEMs. Component-based Transfer Path Analysis (C-TPA), standardized in ISO 20270:2019, enables independent component characterization and integration with virtual models to predict sound and vibration in new assemblies, referred to as Virtual Prototype Assemblies (VPA). However, conventional measurements are labor-intensive, typically restricted to a small number of samples, and overlook production variability. This paper introduces a fully automated, ISO 20270-compliant C-TPA system for non-rigid test benches, featuring a pre-instrumented test fixture with multiple vibration shakers and sensors automatically linked to a data acquisition system for immediate processing. Components can be characterized within minutes, with blocked forces directly

Sturm, Michael, Wienen, Kevin, Brandstetter, Markus, Sorber, Eric, Corbeels, Patrick, Verrecas, Bart, Gonçalves, Vinícius

Unsupervised Feature Frequency Map Extraction for Simple C-EPS Defect Detection

2026-01-0717

6/20/2026

In recent years, the automotive industry has actively explored the application of various AI-based models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Autoencoders, and Transformers to improve defect detection rates at the End-of-Line (EOL) stage. However, implementing these approaches in the Noise, Vibration, and Harshness (NVH) area face several practical challenges: ① extended evaluation times compared to other data types, which limit the quantity of training data and lead to overfitting; ② label imbalance caused by the relatively small amount of defect data; ③ reduced labeling accuracy due to human error; ④ decreased robustness under domain shifts such as changes in jig fixtures, test environments, and signal-to-noise ratio (SNR); ⑤ diminished model reliability when new defect arise during development; and ⑥ constraints imposed by compatibility requirements with existing test equipment. This study proposes a Convolutional Autoencoder (CAE

Park, Jun-Seo, Jo, Hyeon-Choel, Cho, In-Je, Seo, Jae-Yong, Yoo, Seong-Sik

Side Channel and Fault Injection Attack Resistance

J3101-4_202606 (Current)

6/15/2026

This report provides a survey of side channel and fault injection attacks that have an impact on automotive embedded systems. The focus is on side channel attacks that target cryptographic algorithms and hardware security engines, as well as sensitive data leakage. The report also considers fault injection attacks against typical vehicle components that allow bypassing of security controls to compromise the target system security and outlines some countermeasures to detect and/or prevent them. The report provides a list of security countermeasures that can be considered by manufacturers based on their risk tolerance to such attacks. Additionally, it offers the automotive industry supply chain a common language to facilitate the communication of side channel and fault injection mitigation requirements among the various stakeholders.

Vehicle Electrical System Security Committee

Comparison of Concussion Risk Functions Generated from Two Different Datasets

2026-22-0004

6/11/2026

To estimate risk of concussion, risk functions based on injuries occurring in sports are often used. A range of datasets have been used to develop injury risk functions for concussion based on either global kinematics or tissue-level predictors. Two such datasets are one from American football, and another one from Australian football and rugby. These two datasets constitute the largest published collections of video-verified concussive cases in sports with known kinematics suitable for constructing risk functions. The objective of this study was to analyze the differences between two datasets of concussion for injury predictions to better understand the influence on injury risk functions. The kinematics were applied to the KTH head model and risk functions for different kinematic- and tissue-based predictors were developed and compared. The accuracy, sensitivity, specificity, and AUC were also compared. The two datasets evaluated in this study generated different risk curves. The

Fahlstedt, Madelen, Meng, Shiyang, Patton, Declan, McIntosh, Andrew S., Kleiven, Svein

Requirements Extract for AS4159 Specification For An Automated Interchange Of Standards Data

SA-DIG-00004

6/9/2026

This digital standard is a requirements extract of AS4159 Specification For An Automated Interchange Of Standards Data. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC.

An Along-the-Channel Pseudo-2D Approach for Spatially Resolved PEM Fuel Cell Dynamics

2026-37-0010

6/9/2026

The longevity of proton-exchange membrane fuel cells is governed by degradation processes whose rates depend on local operating conditions such as temperature, humidity, liquid-water saturation, and reactant availability. Along-the-channel gradients imposed by the flow field can therefore be relevant when interpreting operating behavior and when formulating models intended to support control and system studies. The AlphaPEM framework provides a dynamic through-plane description of electrochemical and water-management states, but in its baseline form does not resolve how these states vary along the gas channels. This paper presents a pseudo-2D (1+1D) extension of AlphaPEM that couples a discretized along-the-channel gas-channel model to a segment-wise MEA submodel. For each axial segment, the MEA equations are evaluated with local boundary conditions obtained from the channel (e.g., reactant and vapor concentrations), while retaining the key dynamic states of the original formulation

Ringeisen, Björn, Günthner, Michael, Kargl, Pascal

Measurement Framework for Automated Vehicle Systems Interaction

12-09-04-0027

6/9/2026

1Systems level and integration testing are an integral part of the design and development of Automated Vehicles (AVs). Measurement science plays a pivotal role in testing to ensure the safe and efficient operation of AVs. This science establishes a common understanding of the units of measurement, crucial in linking human activities. This article describes the significance of measurement in studying interactions between key system technologies in AVs, including AI for perception, sensing, communications, and cybersecurity. To address the complexities of these interactions, a novel, adaptable, and interactive framework called the System Technology Interaction Model (STIM) is introduced. STIM considers both designed and emergent interactions between these system technologies, allowing AV developers to explore tailored experiments with the flexibility of filtering for focused testing. The framework currently models system interactions statically, not in real-time, to define potential

Griffor, Edward R., Arora, Mahima, Kootbally, Zeid, Nguyen, Vinh

Automotive Engineering: June 2026

26AUTP066/4/2026

New York 2026: diversity on full display New powertrain choices keep popping up on new vehicles from OEMs that debuted at NYIAS this year. Sealing integrity in a Formula 1 limited-slip differential High-temperature hydraulic control in a Formula 1 drivetrain requires dimensional stability, controlled sealing force, and resistance to wear under sustained pressure cycling. Inside the limited-slip differential, the sealing architecture plays a defined mechanical role in maintaining consistent torque management under race conditions. From ADAS to autonomy How engineering thermoplastics can advance sensor-based technologies. Synthetic data and the future of ADAS validation Why ADAS validation can't be solved with more miles alone. Intelligent power distribution will change the way vehicles are designed Electronic fuse (eFuse) technology can create electronic power distribution modules (ePDMs) for architectural flexibility, higher reliability, greater safety, and proactive maintenance

Glaring Vulnerabilities Discovered in Satellite Communications

26AERP06_09

6/1/2026

Researchers discover texts, phone calls, military communication, internal corporate networks all easily eavesdropped on using off-the-shelf equipment. University of California San Diego, La Jolla, CA With $800 of off-the-shelf equipment and months' worth of patience, a team of U.S. computer scientists set out to find out how well geostationary satellite communications are encrypted. And what they found was shocking. Close to half of the communications beamed from satellites to the ground that the researchers were able to listen in on were not encrypted. This included sensitive data including cellular text messages, voice calls, as well as sensitive military information, data from internal corporate and bank networks, and the in-flight online activity of airline passengers.

Commercial Satellite Manufacturing Scales Up as LEO Demand Drives New Production Lines

26AERP06_04

6/1/2026

Space vehicle and satellite development programs are driving demand for new small- and medium-sized satellites across commercial and defense imaging, data collection, and other space-based applications.

Neural Network Enabled Synthetic Air Data System: Development and Validation

2026-26-07206/1/2026

Synthetic Air Data System (SADS) provides a smart solution that can be used to predict critical air data parameters in the absence of conventional air data sensors. Traditional air data sensors, such as pitot-static tubes and vanes, are generally expensive, require regular maintenance, and can fail in harsh weather conditions. In addition, these sensors, along with their processor and computers, add weight to the aircraft. To address these issues, a synthetic air data system is proposed using a Recurrent Neural Network (RNN). Several flight variables were checked for Pearson correlation coefficient with respect to the angle-of-attack and angle-of-sideslip, and thereafter, input features were selected based on the thresholding technique. The proposed neural network has two hidden layers and regularization technique was implemented by adding two dropout layers to each hidden layer to prevent overfitting of the model. The neural network was trained using actual flight test data

Sahu, Sanju, C, Poornima, Kaliyari, Dushyant, TK, Khadeeja Nusrath, Hebbar, Archana

ADS-B in the Age of Cyber Threats: Preserving Safety without Compromising Accessibility

2026-26-0768

6/1/2026

Automatic Dependent Surveillance–Broadcast (ADS-B) has become a cornerstone of modern aviation, revolutionizing Air Traffic Management (ATM) through its ability to continuously transmit real-time flight data—including GPS-derived position, altitude, and velocity. Since its widespread operational deployment over the past decade, ADS-B has significantly enhanced situational awareness, improved safety, extended surveillance coverage into previously unmonitored airspace, and enabled more efficient aircraft routing and separation. However, despite its many advantages, the fundamental design of ADS-B introduces notable security vulnerabilities. Because ADS-B signals are unencrypted and unauthenticated, malicious actors can inject fraudulent broadcasts, creating the illusion of non-existent aircraft. Such spoofing attacks can trigger false cockpit alerts and distract pilots during critical phases of flight. The current ADS-B data format prioritizes simplicity to accommodate a broad range of

Chikkegowda, Kantha, Shetty, Ramesh, Khan, Kalimulla, Sahoo, Subhransu

Hybrid Digital Twin for Aero-Engine Bearing Prognostics: A Physics-Informed Machine Learning Approach to RUL Prediction

2026-26-0718

6/1/2026

Unscheduled maintenance due to the failure of critical components, such as aero-engine rolling element bearings, is a leading cause of costly Aircraft-on-Ground (AOG) events; consequently, current time-based maintenance practices are inefficient and prone to risk. This paper develops a resource-efficient Hybrid Digital Twin (HDT) model for an engine bearing, focusing on the dynamic prediction of spall growth due to Rolling Contact Fatigue (RCF), thereby enabling a condition-based maintenance paradigm. The HDT architecture integrates two core models: (1) a physics-informed model that uses established life and fatigue theory to define initial degradation thresholds, and (2) a data-driven Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM) network, for dynamic degradation rate modeling. The methodology utilizes a Monte Carlo simulation coupled with RCF progression equations to generate a large, high-fidelity synthetic run-to-failure dataset under varying

Mohamed, Abbas

Risk-Driven Program Management Framework for Avionics Development: Integration of Safety, Certification, and Cybersecurity Standards

2026-26-0716

6/1/2026

Modern avionics programs contend with escalating complexity driven by concurrent safety certification, cybersecurity compliance, and multi-standard regulatory demands. Traditional program management approaches treat risk management as a parallel support function rather than a central governance mechanism, resulting in reactive responses that fail to prevent cost and schedule erosion. This paper introduces the Risk-Driven Program Management Framework (RD-PMF), an eight-phase governance model that embeds quantitative risk assessment, standards-risk mapping across DO-178C, DO-326A, ARP4754A, and ARP4761A, real-time digital dashboards, and earned value management within core program decision-making. The framework integrates probabilistic schedule analysis using Monte Carlo simulation with continuous risk exposure monitoring to enable proactive, data-driven governance. RD-PMF is demonstrated through a representative avionics program scenario modelled on a flight control system development

Rahul, Saurabh, Benikireddy, Raghunatha

Effect of Fluid Flow/Time Duration on Static Voltage Generation for Aerospace Hose Assembly

2026-26-0757

6/1/2026

Static electricity is an electrical imbalance on the surface of a material which can interact with other components having same or different materials. Fluid flow within the hose assembly generates static voltage due to friction caused by fluid flow in pipes, that needs to be appropriately quantified and dissipated. Accumulation of such static charge may lead to sudden discharge leading to spark generation. Spark generation around fuel flow might lead to system failure and failure in aircraft engines. Test experiments were conducted to analyze static voltage generated in hose assembly due to fuel flow with the objective that voltage achieved is within the acceptable range to avoid ESD (Electrostatic Discharge) failure. Procedure includes flow rate monitoring and voltage measurement using fuel as test fluid. The testing revealed that the curvature of the hose affects the readings, highlighting the importance of consistent meter alignment. Using a grounding strap is essential to prevent

Waghmare, Shashank

Hybrid Physics-Informed Neural Network and PID Control for Fault-Tolerant Autonomous Multi-Copters

2026-26-0770

6/1/2026

This paper addresses the critical challenge of fault-tolerant control in autonomous multi-copters, particularly under conditions of one or two rotor failures a scenario that often leads to severe instability and a complete loss of directional control due to unbalanced torque and resultant autorotation. Existing advanced control strategies, including optimal approaches such as LQR, typically require precise system modeling and state estimation, which are difficult to achieve in real-world, dynamic failure scenarios. Alternative methods like fuzzy logic, sliding mode control, and gain-scheduling either lack robust generalization or are impractical for enumerating all possible failure cases. In this work, a hybrid control framework integrating Physics Informed Neural Networks (PINN) with a standard PID controller is proposed for fault-tolerant operation of autonomous multi-copters subject to multiple actuator failures. PINNs incorporate governing physical laws as regularization in their

Charapalle, Samruddhi, Venugopalan, Nandagopalan, Nerkundram Muralidharan, Arun, Sundararaj, Laveen

Augmented Reality and Multimodal Interfaces for Astronaut Training and In-Orbit Operations

2026-26-0796

6/1/2026

Augmented Reality (AR) and multimodal human–machine interfaces (MMI)— combining visual overlays, voice, gesture, eye- tracking, and biometric sensing—are maturing into flight-relevant technologies capable of transforming astronaut training and in-orbit operations. These interfaces can reduce task time, lower procedural errors, and mitigate cognitive workload, thereby strengthening crew autonomy and mission safety. Global operational experiences from International Space Station (ISS) augmented- reality trials and related international programs are synthesized to inform the proposed system architecture and validation framework: (i) an overview of India’s current AR/MMI-related ecosystem relevant to human spaceflight, including astronaut training pipelines and research collaborations; (ii) a mission-grade AR/MMI system architecture and multimodal fusion/decision logic suitable for human-rated operations; (iii) algorithms and programming examples for AR-driven finite-state-machine (FSM

Yadav, Anoop Singh

System and Method of Strain Emulation Using a Beam Load Cell Test Setup

2026-26-0743

6/1/2026

This novel method deals with emulation of Strain of a Structural Measurement System which includes software validation, acceptance tests and training. Current methods for simulating strain and force data for developing and verifying data acquisition (DAQ) software typically rely on costly electronic simulators or specialized hardware, making it challenging and expensive for developers, researchers, and small organizations to test their solutions under realistic conditions. To verify DAQ software, multiple specialized hardware solutions are deployed, that include Electronic Simulators, Commercial DAQ Modules and Hydraulic/Pneumatic test rigs. These technologies pose a challenge with limited flexibility and scalability options for small-scale prototyping, especially in budget-constrained scenarios. The sensors on these equipment may or may not be company approved inducing acceptance challenges. Our invention is an inexpensive, scalable, and mechanically simple alternative. Using a 3D

Murthy, Harsha, Bhat Venkatesh, Aditi, K Padmanabhan, Rahul, Madhu, Sheetal, Garag, Naveen

Electrical Harnessing in Satellite Launch Vehicles of ISRO – Methods for Sustainable, Safe and Reliable Systems

2026-26-0779

6/1/2026

The electrical harness system of satellite launch vehicles functions as the backbone of spacecraft avionics; inter connecting subsystems through complex networks of wires and connectors. An electrical harness is a group of wires bunched together and terminated in connectors. The common insulations used for launch vehicle applications include PTFE, Polyimide, ETFE and TKT. The connectors used are of aerospace grade and connectors tailored for space applications. With over 5000 connectors and 200 km of cables constituting nearly 20% of vehicle mass, the design, fabrication, and sustainability of these systems are critical. The insulations of connectors inserts or the wires are critical for the durability of harness elements. Nevertheless, these insulations are non-expendable and pose disposal challenges and some releases toxic gases when burned or due to vacuum outgassing phenomenon. Also, the cadmium plating which is often used for the environmental resistance of connector shells

K S, Nithish, TR, Binny, D S, Praveen Kumar

Design and Run-Time Information Exchange for Health-Ready Components

JA6268_202303 (Current)

5/26/2026

This Surface Vehicle & Aerospace Recommended Practice offers best practices and a methodology by which IVHM functionality relating to components and subsystems should be integrated into vehicle or platform level applications. The intent of the document is to provide practitioners with a structured methodology for specifying, characterizing and exposing the inherent IVHM functionality of a component or subsystem using a common functional reference model, i.e., through the exchange of design-time data and the application of standard vehicle data communications interfaces. This document includes best practices and guidance related to the specification of the information that must be exchanged between the functional layers in the IVHM system or between lower-level components/subsystems and the higher-level control system to enable health monitoring and tracking of system degradation severity. The intent is to provide an IVHM system that can robustly report the degradation of a given

HM-1 Integrated Vehicle Health Management Committee

Reliability Program Standard

JA1000_202108 (Current)

5/26/2026

This SAE standard establishes the requirement for suppliers to plan a reliability program that satisfies the following three requirements: a The supplier shall ascertain customer requirements b The supplier shall meet customer requirements c The supplier shall assure that customer requirements have been met

G-41 Reliability

Effect of EGR Rate on Cyclohexanol-Diesel Dual-fuel Engines

2026-99-1709

5/22/2026

xxxx

Liu, Yuchen, Yang, Chenxi, Fan, Jinyu, Chen, Ke, Ye, Zixiao, Huang, Jialiang

Research on the Construction Scheme of Offshore Approach Embankment Based on Marine Environmental Elements

2026-99-1707

5/22/2026

222

Fei, Chengpeng, Chen, Mingbo, Zhou, Fang, Wang, Shiyue, Zhou, Siyang, Zhang, Fang

Multi-Objective Reactive Power Optimization Method for the Distribution Grid with Distributed Generator

2026-99-1702

5/22/2026

To enhance the economic efficiency and operational security of distribution grids, this paper develops a reactive power optimization model that incorporates distributed power sources. The model aims to minimize the costs of reactive-load compensation equipment, reduce voltage deviations, and lower network losses while satisfying operational constraints. To overcome the common drawbacks of the standard genetic algorithm—such as limited optimization precision and a tendency to converge to local optima—four improvement strategies are introduced. These include an enhanced encoding scheme, an initial population generated via opposition-based learning, an elite retention strategy, and the adaptive adjustment of crossover and mutation rates. Together, these modifications strengthen the algorithm’s global search capability. The proposed approach is validated using the IEEE30 node system. Compared with both the conventional genetic algorithm (GA) and an adaptive genetic algorithm, the improved

Wang, Maoze, Xiao, Wenyu, Liu, Yujia, Xu, Zhengwei, Xia, Yinyong

Assessment of Flexibility Adequacy in Power Systems and Flexibility Resource Allocation

2026-99-1717

5/22/2026

With the introduction of China’s dual-carbon goals (carbon peak and carbon neutrality), renewable energy has experienced rapid development in the country, particularly wind energy, which has established a pivotal role within the new energy sector. However, the inherent fluctuations in wind power generation pose significant challenges to maintaining grid stability and operational reliability. In power systems where the proportion of installed wind power capacity has significantly increased, the allocation of flexible resources becomes crucial. These resources help the system adapt to fluctuations in wind power generation and load demand, avoid wind power curtailment, and reduce costs. In addition, energy storage enhances grid flexibility and stabilizes renewable energy, but is constrained by high costs. Therefore, optimizing energy storage allocation and improving its economic efficiency have become urgent issues. This study focuses on flexibility adequacy assessment and resource

Peng, Jian, Wei, Jinpeng, Zhu, Zhengyin, Hu, Jianmin, Li, Yuxiang, Miao, Gang, Zhang, Huaide

Research on Carbon Emission Reduction of Structural Assembly Construction Technology in Metro Station Engineering

2026-99-1731

5/22/2026

To reduce the carbon emissions during the construction period of metro stations, two structural prefabrication schemes with varying prefabrication rates, based on the top-down construction method, were proposed and analyzed for their ability to study the carbon reduction potential of structural prefabrication construction technology in metro station construction, in comparison to traditional open-cut cast-in-place methods. A BIM model of the envelope and main structure of a metro station under construction in Qingdao was established to analyses the carbon emission impact factors of the metro station in terms of the consumption of materials, personnel, machinery, and transportation of each subcomponent project. The results show that the structural assembly construction technology can greatly reduce the work of support installation and dismantling, formwork installation and dismantling, and reinforced concrete pouring in the enclosure structure. With the prefabrication rate increasing

Gao, Guangyi, Wang, Zheyong, Dong, Silong, Gou, Jiayuan, Li, Yangqing, Zeng, Tiesen

The Spatial-Temporal Evolution of Coupling Coordination Degree Development of Carbon Emission-Passenger-Freight in Five Northwest Provinces

2026-99-1716

5/22/2026

Taking China’s five northwestern provinces as the study area, this paper investigates the spatial-temporal interactions among carbon emissions, passenger transport, and freight transport from 2010 to 2020. An entropy-weighted composite index is constructed for each system and integrated into a coupling coordination degree model to quantify interaction. It is found that (1) the average annual growth of provincial coupling coordination degree is 4.7%, but the gradient difference between regions is significant, and the extreme difference of coupling coordination degree between east and west reaches 4.5 times in 2020; (2) Spatially, it shows a unipolar leading pattern, with Shaanxi achieving a significant decrease in carbon emission intensity and Qinghai achieving a lesser coupling coordination degree of 23% in Shaanxi due to the high proportion of highway freight transport and single energy structure; (3) the driving mechanism analysis shows that the improvement of transport network

Qian, Yongsheng, Li, Shaoyuan, Zeng, Junwei, He, Qingling

SAE TOMORROW TODAY - Can Airports Become Innovation Labs?

135685/21/2026

What if your airport was a living laboratory for innovation? Listen in as we sit down with Brian Cobb, Chief Innovation Officer at Cincinnati/Northern Kentucky International (CVG) Airport, to explore how CVG operates as a "city within a city," deploying emerging technologies to improve passenger experience, strengthen operational resilience, and reimagine the modern airport. Discover how CVG is advancing innovation through real-time TSA wait times, robotics testing, and the growing role of AI, autonomy, and cybersecurity. Get even more insight as Brian takes the stage as keynote speaker at the 25th anniversary of AeroTech(R) this June. He'll explore what's next for aviation innovation, from connected robotics and autonomous workflows to the standards and cross-industry collaboration needed to make it all work safely. To learn more and register, visit sae.org/events/aerotech. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast

Patterson, Lori

Coordinated Stability Control for Active Rear Steering Vehicles Based on Dynamic Stability Region

2026-99-0713

5/15/2026

To improve the handling stability of four-wheel steering/drive vehicles under complex high-speed maneuvers, this study proposes a coordinated control strategy that incorporates Active Rear Steering (ARS) and Direct Yaw Moment Control (DYC) based on a dynamic stability region. Firstly, a four-wheel steering vehicle dynamics model including lateral motion and yaw motion is established, and the ideal values of the control variables are determined. Secondly, combined with the fuzzy control theory and double-line method, the boundary of the dynamic stability region is obtained in the sideslip angle-sideslip angle rate β−β̇ phase plane, and the vehicle state is categorized into stable, unstable, and critical stable region. Then, A hierarchical control architecture is designed based on the stability boundary. The upper controller comprehensively solves the target rear wheel angle and additional yaw moment through feedforward feedback control; the coordinated control layer allocates control

Nie, Kehe, Chen, Jin, Wang, Falong, Li, Ren, Bai, Xianxu

Optimal Power Sharing and Voltage Restoration in DC Microgrids Based on Adaptive Droop Control

2026-99-0707

5/15/2026

The stable operation of islanded DC microgrids is conditioned by two essential objectives. One is to maintain the bus voltage at its nominal value, and this can ensure system stability. The other is to achieve cost-effective power allocation among distributed generation units, which guarantees economic efficiency. These two objectives are often conflicting. Adding droop control to the voltage and current dual closed-loop control can achieve primary current sharing. However, it inevitably introduces steady-state voltage deviations on the DC bus and results in inflexible or not optimal power sharing. To resolve these inherent limitations, this paper proposes a innovative distributed secondary control strategy. The method is designed to meet both requirements within a unified framework. In the primary control layer, it uses adaptive droop gains to optimize power distribution in real time based on changing load requirements which enables distributed generation units to achieve cost

Sun, Wei, She, Dunjun, Yu, Jinzhu, Yuan, Weibo, Peng, Bo, Zheng, Yingchun

Adaptive Speed-Regulated Impedance Control for Robotic Ultrasound Scanning: Reducing Constant-Force Tracking Errors

2026-99-0751

5/15/2026

Robotic ultrasound scanning technology is a research hotspot in the field of medical imaging, and can achieve standardized and high-precision data acquisition. However, large force tracking errors occur during scanning, especially in complex human tissues, which can severely degrade image quality and diagnostic accuracy. Therefore, we propose an adaptive speed-regulated impedance control strategy to address this challenge, which innovatively combines the spline real-time interpolation and impedance control for constant force tracking. Firstly, the discrete ultrasound scanning paths are fitted to generate a smooth and synchronized interpolation trajectory. Then, the speed of the reference trajectory is adjusted in real time based on the Taylor formula to reduce the force tracking error. Experimental verification was conducted, and the results showed that the force tracking error increases with the increase of trajectory speed. In addition, at high speeds (e.g., 10 mm/s), the mean

Min, Kang, Zhang, Le, Shi, Yudong, Fang, Jin, Mo, Hangjie, Li, Xiaojian

Pattern-Guided Implicit Sentiment Analysis: Knowledge-Augmented Prompting with Aspect-Sentiment Joint Extraction for Automotive User Feedback

2026-99-0753

5/15/2026

Implicit sentiment analysis of automotive user feedback is crucial for understanding user opinions. Automotive user feedback often express opinions in an indirect way and are accompanied by a dense array of industry terms. Therefore, without costly fine-tuning, both aspect identification and sentiment analysis are rather difficult. We propose a Pattern-Guided pipeline for implicit sentiment analysis to achieve the joint extraction of aspect and sentiment. This pipeline first performs Pattern Anchoring, mapping colloquial expressions and slang to the standardized vehicle component knowledge system. Then, using Knowledge-Augmented Prompting, these domain rules are injected into well-designed prompt templates. In this pipeline, the large language model (LLM) is applied to output JSON records suitable for comprehending, including aspects, sentiments, confidence levels, and brief reasons. To enhance stability, we employ an improved prompt and consistency-driven confidence fusion to generate

Chang, Gengjia, Deng, Zuxing, Ma, Aonan, Yao, Jiangqi, Li, Xiaojian, Li, Ling

Multi-Objective Parameters Optimization of Centrifugal Fan Based on GA-BP Neural Network and NSGA- II

2026-99-0701

5/15/2026

In order to achieve the research objective of simultaneously improving the air volume and reducing the noise of centrifugal fans, a combination of orthogonal experimental design, BP neural network modelling and multi-objective genetic algorithm (NSGA- II) was used to find the optimal method, and the worm tongue placement angle φ, worm tongue radius R, expansion angle θ and outlet expansion section height L of the worm casing were selected as optimization variables. The air volume and noise of the centrifugal fan under the design working condition were calculated by non-constant and constant calculations, and the air volume and noise were used as the optimization objectives. The results demonstrate that, compared to the initial design, the optimized fan model achieved a noise reduction of 10.99 dB and an airflow increase of 1.76%. Furthermore, the amplitude of the pressure pulsation coefficient at the blade passing frequency was significantly reduced at the monitoring point near the

Huang, Guoxing, Zhang, Weihong, Li, Weichang

Validation of Vehicle-Based Injury Severity Prediction Using Crash Telemetry Data

09-14-01-0009

5/4/2026

Subaru has developed vehicle-based Injury Severity Predictions (ISP) models using data from the National Automotive Sampling System Crashworthiness Data System (NASS-CDS) covering calendar years 1999–2015, for integration into Advanced Automatic Collision Notification (AACN) systems. This study evaluates the accuracy of these ISP models by comparing predictions derived from Subaru vehicle telemetry with actual Injury Severity Scores (ISS) of transported occupants. Two crash databases were utilized: Subaru Telematics Assisted Accident Research (STAAR) data for calendar years 2021–2024, which includes Automatic Collision Notification (ACN) data, police reports, emergency medical services (EMS), and medical records from the medical centers across Michigan; and the Fatality Analysis Reporting System (FARS) data for calendar years 2021–2023, matched with ACN data to supplement serious injury cases. ISS values were obtained from medical records in STAAR, while fatal cases in FARS were

Ejima, Susumu, Zhang, Peng, Cunningham, Kristen, Wang, Stewart

Visual–Language Model–Driven Annotation and Analysis of Maneuver Data for Enhanced Vehicle Safety

09-14-01-0032

5/4/2026

Vehicle maneuver data are essential for perception and planning in advanced driver-assistance systems (ADAS) and automated driving systems (ADS). While high-quality annotations improve machine-learning performance, existing maneuver datasets remain fragmented, labor-intensive to annotate, and inconsistent in semantic richness. Challenges persist in scalability, interpretability, and contextual labeling. This article establishes a structured framework for maneuver data analysis by combining a systematic review of existing resources with the development of a new multimodal dataset. First, we conduct a systematic review of publicly available datasets such as HDD, KITTI, BDD-X, D2CAV, Brain4Cars, DrivingDojo, and the Driving Behavior Database. We further evaluate the data modality and sensor configurations including event data recorders, onboard logging systems, and smartphone sensing. We then propose the Matt3r Data Collection System with modern metadata management, which integrates video

Bai, Ling, Yuan, Chongyu, Osman, Islam, Lin, Zirui, Mirab, Ghazal, Saheb, Amir, Parnian, Neda, Shapiro, Evgeny, Shehata, Mohamed S., Liu, Zheng

Characterization of Lane Keeping and Departure Scenarios Using Large-Scale Naturalistic Driving Data

09-14-01-0022

5/4/2026

Roadway departures remain a major cause of crashes, injuries, and fatalities on U.S. roads. Technologies such as lane keeping assist (LKA) and lane centering assist (LCA) can help mitigate these crashes, but their development involves extensive characterization of the parameter space in which they operate. Lane and road departures (LDs/RDs) and lane changes (LCs) must be systematically described and quantified to distinguish kinematic features, identify contributing factors, and benchmark system influence on lateral control. This study developed a unified pipeline to mine over 36 million miles of naturalistic driving study (NDS) data collected from more than 3800 participants. The pipeline integrates various types of signals to detect roadway boundary crossings, classify LKA-relevant scenarios, and extract roadway, driver, environmental, and assistance-related parameters. Lane keeping epochs with and without LKA were also extracted to quantify system influence on lateral control. In

Ali, Gibran, Terranova, Paolo, Williams, Vicki, Holley, Dustin, Saffy, Joshua, Antona-Makoshi, Jacobo, Kefauver, Kevin, Shull, Emily, Li, Eric, Venegas, Michael

Army Researchers Design 3D-Printed Soldier Portable Autonomous Reconnaissance Transitioning Aircraft

26AERP05_07

5/1/2026

Army researchers recently developed a 3D-printable, easy-to-assemble drone designed to enhance intelligence, surveillance and reconnaissance capabilities. Army Research Laboratory, Adelphi, MD Researchers at the U.S. Army Combat Capabilities Development Command, or DEVCOM, Army Research Laboratory (ARL) harnessed bottom-up Soldier innovation to develop an experimental 3D-printed small unmanned aerial system, or drone, that was demonstrated at the inaugural U.S. Army Best Drone Warfighter Competition in Huntsville, Alabama. Known as the Soldier Portable Autonomous Reconnaissance Transitioning Aircraft, or SPARTA, the drone was developed at DEVCOM ARL in collaboration with Soldiers. By incorporating Soldier feedback early in the design process and leveraging ARL's world-class research facilities, researchers developed a 3D-printable, easy-to-assemble drone designed to enhance intelligence, surveillance and reconnaissance capabilities. ARL is actively working to partner the technology

A Review of Data-Driven Approaches and Datasets for Battery State of Health Estimation

14-15-02-0009

4/22/2026

This article surveys the most recent data-driven methods of lithium-ion (Li-ion) battery state of health (SOH) estimation methods and dataset resources utilized in electrified vehicles (EV) and their potential adoption for automotive battery management systems. These include regression-based models, ensemble learners, deep neural networks, and physics-informed hybrid methods. The review describes estimation methods found in articles published between 2023 and 2025, and investigates their differences in terms of estimation accuracy, data requirement, interpretability, and real-time deployment ability. The article traverses the dataset space, focusing on laboratory aging datasets, vehicle field–based datasets, telematics-derived records, and synthetic or augmented datasets, to underline that model performance in the estimation of SOH cannot be disentangled from the quality of the data, the operating coverage, and the transfer conditions. Apart from the model design, this work reviews the

Nyachionjeka, Kumbirayi, Bayoumi, Ehab H.E.

Thermal Management of Electric Microtruck Motors

2026-01-5026

4/9/2026

This study investigates the gradeability performance of an L7e-class electric micro truck from both vehicle dynamics and thermal perspectives. A 1D simulation model (Amesim) was developed and validated with multiple test results. Using inputs such as motor characteristics, drivetrain configuration, and vehicle mass, the model analyzed vehicle performance on a 20% gradient, calculating the required torque, achievable motor speed, and corresponding vehicle speed. Furthermore, gradeability limits were evaluated, and the effects of gear ratio and airflow rate around the air-cooled motor on both gradeability and thermal behavior were examined. The findings provide practical insights for improving the powertrain and cooling system design of lightweight electric vehicles. The results showed that selecting an appropriate gear ratio can enable the motor to operate more efficiently under demanding driving conditions. A 20% increase in the gear ratio was found to delay motor heating by up to 10

Turan, Azim, Kantaroğlu, Hasan Hüseyin, Akbaba, Mahir, Kasım, Recep Faruk, Yarar, Göktuğ

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