Browse Topic: Human Factors and Ergonomics

Items (20,842)
Find
Research on Unmanned Aerial Vehicle Fault Detection Method Based on 1DCNN-BiLSTM2026-99-18667/17/2026
In recent years, drone technology has seen widespread application in both civilian and military fields. By 2025, China will introduce supportive policies from multiple dimensions, including industrial development, technological innovation, and application promotion, to significantly increase the number of UAVs in use and their frequency. However, drones are prone to malfunctions due to factors such as bad weather and electromagnetic interference, which may result in serious consequences, including property damage and casualties. Therefore, improving the accuracy of fault detection and the response time of drones is of great significance. Although current research has made progress, there are still deficiencies: First, most of them rely on a single or limited data source, resulting in incomplete information and vulnerability to interference, which leads to low detection accuracy and reliability; Second, traditional methods are mostly based on fixed thresholds or simple rules, lacking real-time dynamic monitoring and adaptive analysis capabilities, making it difficult to issue timely warnings of potential faults. To this end, this study proposes a multi-scale time series prediction model based on multimodal and multi-branch, integrating multimodal data, constructing a dual-branch architecture, and combining deep learning and attention mechanisms to enhance the anomaly detection effect of unmanned aerial vehicles. A dual-branch anomaly detection model based on 1DCNN-BiLSTM and continuous wavelet transform is proposed, including a trajectory prediction difference branch and a full time series data branch. In the dual-branch output stage, the attention gating mechanism is utilized to fuse features and improve the detection performance. The experimental results show that this model performs excellently in both normal trajectory prediction and anomaly detection, providing an effective solution for drone anomaly detection.
Pu, Zhenglin, Zhang, Lin
Experimental Analysis of Human Head Dynamic Response and Injury under High-G Landing Shock2026-99-18617/17/2026
This study looks at how the human head reacts and gets injured during high-G landing impacts in spacecraft return capsules. We used a vertical drop tower system for the experiments. A standard crash test dummy, called the Hybrid III 50th, was used to imitate how astronauts sit during landing. We applied two common safety standards—the Head Injury Criterion (HIC) and the 3 ms cumulative acceleration rule—to measure head response under high-G impacts. The results show several things. First, head acceleration increases linearly as seat acceleration increases. Second, the peak total acceleration of the head is much higher than the seat acceleration. In particular, acceleration in the X and Z directions is much stronger than in the Y direction. Third, when seat acceleration went over 47.71 g, HIC exceeded the safe limit of 700, and the 3 ms head acceleration also passed the 80 g limit. This suggests that 40 g should be considered a safe upper limit for seat acceleration. This work provides experimental support for improving landing systems to protect astronauts’ heads during high-G impacts.
An, Hao, Wang, Yafeng, Guo, Yazhou
Dynamic Characteristics Study of the Deployment Process of Aircraft Folding Wing2026-99-18607/17/2026
Folding wing mechanisms are widely applied in aircraft structural design. This design reduces the size of the aircraft, making it easier to store and transport. Whether the foldable wing can successfully deploy determines the completion of the flight mission. Therefore, it is crucial to study the kinematic and dynamic parameters of the mechanism during the deployment process. The deployment of the folding wing typically occurs within milliseconds. The flow field imposes aerodynamic loads on the mechanism, causing it to move, while the large deformation motion of the mechanism, in turn, affects the aerodynamic loads from the flow field. This is a typical fluid-structure interaction (FSI) process. Traditional CFD methods for solving the deployment process in a decoupled manner often result in large errors and cumbersome procedures. To investigate the aerodynamic loads and deformation of the folding wing mechanism during deployment, the ALE algorithm in LS-DYNA was selected to directly solve the kinematic and dynamic parameters of the mechanism in unsteady flow fields, guiding the design of foldable wing mechanisms.
Wei, Ting, Tong, Zongkai, Li, Naitian
Optimization of the Light Environment for the Remote Control Tower Humancomputer Interaction Interface Based on Ergonomics2026-99-18477/17/2026
The development of remote tower systems in aviation and the resurgence of multi-display interfaces and virtual environments have dramatically influenced ATC, increasing both controllers’ visual demands and their ergonomic needs. This study uses the Visual Ergonomics to study the impact of screen luminance level, along with color temperature, on trainees’ visual performance, fatigue, and physical discomfort in the control rooms of the Remote Tower. By combining a simulated remote control system with spectrometer measurements, PVT alertness tests, VMT (Visual Memory Test) measurements, and subjective evaluations, COST B21 can build up a multi-dimensional ergonomic assessment framework. Eight levels of display luminance (and color temperature) were tested, including two illuminance levels (300 lx and 400 lx) and four color temperature ranges (6000 K–9000 K). Using the Analytic Hierarchy Process (AHP), these parameters were assigned weights to derive a Visual Ergonomics (VE) scoring model, and the ideal visual performance was observed at 400 lx illuminance and 8000 K CCT. The results clearly illustrate the significant impact of display parameters on operational performance in remote tower systems and provide both practical data and a theoretical basis for the human factors design and fatigue reduction research on RTSs.
Zhong, Linfeng, Hu, Ruohui, Luo, Peilin, Zuo, Qinghai, Zhong, Qingwei, Ai, Yi
Evaluation of Civil Aircraft Options Based on Grey Analytic Hierarchy Process2026-99-18087/17/2026
Product options are an important means for civil aircraft manufacturers to meet market demand, increase revenue, and enhance competitiveness. How to achieve a customized configuration of civil aircraft options is the focus of attention for aircraft manufacturers. In order to reduce manufacturing costs and cover more target markets, it is necessary to pay attention to the customized detail design of aircraft products in the early stages of design. At present, academic research on product selection is relatively limited and lacks quantitative evaluation methods. This article selects four elements to form an evaluation indicator system, namely comfort, competitiveness, cost investment, and maintainability; establishes a civil aircraft option evaluation model based on grey correlation analysis, quantifies the degree of correlation between product options and customer needs, and uses the analytic hierarchy process to reflect the weight differences of evaluation indicators. Taking the option list of a wide-body aircraft as an example, the model was used to evaluate and rank the options, verifying the rationality of the model and providing a reference for aircraft manufacturers to make provisions in advance.
Lu, Meihua
ERRATUM: Ergonomic Assessment of Glove-Box Design in Passenger Vehicles for the Indian Scenario15-19-02-0009.17/15/2026
ERRATUM
Jujjavarapu, Sreeram, Rajakumaran, Sriram, Kota, Srinivas, Kotkunde, Nitin, Jasti, Naga Vamsi Krishna
R134a and R1234yf Refrigerant Charge Determination Test MethodJ3023_202607 (Current)7/13/2026
The purpose of this document is to establish guidelines for determining the critical R134a and R1234yf refrigerant charge for off-road, self-propelled work machines as defined in SAE J1116 and agricultural tractors as defined in ANSI/ASAE S390. It will develop a minimum to maximum refrigerant charge range in which the HVAC system can maintain proper operation. Operating conditions and characteristics of the equipment will influence the optimum charge. Since these conditions and characteristics vary greatly from one application to another, careful consideration should be taken to determine the optimum R134a and R1234yf refrigerant charge for the HVAC system.
HFTC6, Operator Accommodation
Human Reliability in Aircraft Maintenance Operations: A Quantitative Analysis Through Evidential Reasoning-Enhanced SPAR-H Methodology2026-99-05947/10/2026
The reliability of aviation maintenance personnel directly impacts flight safety, yet systematic methodologies for the quantitative prediction of human error probability (HEP) in this domain remain lacking. To address this gap, a novel human factors reliability analysis method for aviation maintenance is proposed, extending the SPAR-H model through Evidential Reasoning (ER). This method is implemented as follows: Maintenance tasks are decomposed into subtasks. Subsequently, the eight types of Performance Shaping Factors (PSFs) for each subtask are evaluated by domain experts according to defined PSF levels. Expert judgments are then aggregated using Evidential Reasoning theory, enabling the calculation of aggregated PSF levels. These aggregated levels are interpolated to determine the corresponding impact multipliers. Finally, the HEP for aviation maintenance operations is calculated by integrating the SPAR-H basic error probability model with task series/parallel logic rules. The proposed methodology is validated using an inspection operation case study. This study establishes a methodological framework for human factors reliability analysis in aviation maintenance, providing a theoretical foundation for developing scientifically grounded prevention and control measures to enhance aviation safety levels.
Meng, Meng, Ma, Ning, Guan, Zhongqing, Han, Zuyang, Nan, Wenxue, Cai, Hongbin
Research on Age-Friendly Design Strategies for Autonomous Buses Based on Service Design Concepts2026-99-05367/10/2026
In the context of the accelerating development of an aging society, the inconvenient mobility of the elderly conflicts with the design of existing vehicles. The promotion and development of autonomous vehicles can provide solutions to this conflict to a certain extent. But existing autonomous vehicles lack a systematic age-friendly design. This study is based on a service design idea and employs the KJKANO hybrid model. The KJ method is used to construct a three-tier demand framework of “safety-function-emotion.” The KANO method is applied to identify the priority classification of each demand within the tiered framework. The study derives an aging-friendly design strategy for autonomous buses that prioritizes safety demands as the foundation, with functionality and emotional demands balanced accordingly. These strategies are then implemented in design practice. This study provides a user-centered systematic solution for the age-friendly design of autonomous buses, offering insights for research on age-friendly smart transportation.
Li, Wangyan, Ji, Yuanyuan
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
Analysis and Improvement of Rattle Noise in Manual Transmission2026-99-05277/10/2026
In response to the problem of manual transmission rattle noise in the acceleration process of a truck, the mechanism of the problem is analysed, and the scheme is developed and verified from two aspects: reducing the torsional vibration of the system and reducing the response of the transmission gear. The results show that, on the one hand, reducing the clutch stiffness and optimizing the torsional vibration of the system can reduce the rattle noise of the transmission; On the other hand, it can also reduce the rattle noise of transmission gears by improving the engagement precision of transmission gears and reducing the gear clearance. Considering the improvement effect, cost, and influence on other performance of the two schemes, the appropriate engineering scheme is selected to effectively solve the problem and improve the riding comfort of the product.
Yang, Zhijie, Xu, Binghua
Human-Machine Ergonomics Analysis of Civil Aircraft Electric Pilot Seats2026-99-05707/10/2026
This paper uses a structured evaluation framework to study the ergonomics of electric pilot seats in modern civil aircraft. We have established a multi-level indicator system to examine the adjustability, pressure distribution, dynamic response and, fatigue relief effect of the seat. All experimental data were obtained from a full-scale cockpit simulator environment, where a ground-based mock-up and motion-free simulated cockpit were used to replicate real operational posture, control-reach conditions, and long-duration mission loads. This framework combines experimental measurement and fuzzy evaluation techniques to quantify the quality of human-computer interaction. Test results show that compared with ordinary seats, the prototype seat has a wider adjustment range, a more uniform pressure distribution, and a smoother dynamic response. It is particularly worth mentioning that it can delay the emergence of fatigue during long-term operation, which proves the advantages of the electric adjustment mechanism. The simulated-cockpit test conditions ensure that these results are reproducible and representative of actual cockpit usage scenarios. This findings not only provide theoretical guidance and engineering basis for optimizing the cockpit seat system, but also provide methodological reference for applying fuzzy analysis in aerospace ergonomics research.
Tian, Yanan, Pi, Zhengyang
Impact of Driving Style on Electric Vehicle Energy Consumption2026-99-05077/10/2026
The way we drive has a big effect on how much energy electric cars use, so making better driving habits can help make electric cars use less energy. By utilizing a set of real EV driving data, this paper classifies and analyzes EVs from the perspective of energy consumption, and establishes an intelligent scoring system for EV driving behavior based on a decision tree model. Experimental results show that this method is able to successfully distinguish different driving behaviours and the critical driving behavior factors, such as vehicle speed, accelerator pedal change rate, etc., and braking behavior are identified. Use intelligent scoring to give driver suggestions; this way, they can improve on their driving techniques and lower their energy consumption.
Liang, Yongkai, Zhang, Hao, Liu, Yu, Yu, Hanzhengnan
Research on Predicting Occupant Height Using CNN-Based Pressure and Posture in Zero-Gravity Seats2026-99-05217/10/2026
Zero-gravity seats alleviate prolonged sitting fatigue by optimizing human body pressure distribution, but the correlation mechanism between body size parameters and pressure distribution remains unclear. This study proposes a deep learning model based on multimodal data fusion, combining pressure matrices and postural angle data to construct a convolutional neural network (CNN) with a height prediction error ⩽3 cm. Experiments collected pressure and posture data from 100 participants with diverse anthropometric percentiles. Through the fusion of features and the optimization of the model, the study managed to quantify how height and weight impact pressure gradients. The results indicate that the model achieved a prediction R2 value of 0.73, which confirms that there is a strong correlation between pressure distribution and body size parameters. The findings offer theoretical and technical support for the adaptive adjustment systems within intelligent cabins.
Bi, Tengfei, Nie, Jiacheng, Du, Changjiang, Ji, Yuechen, Wang, Song, Sun, Jiawei
Crashworthiness of WC19 Wheelchairs in Vertical Dynamic Aircraft Seating Tests09-14-02-00047/9/2026
Currently, people who use wheelchairs are not permitted to use their own wheelchairs as seats on commercial aircraft. To advance equitable aircraft travel for these passengers, we need to determine whether wheelchairs would be safe seating for their occupants and not pose a safety hazard for other passengers in case of emergency landing. We hypothesized that wheelchairs meeting the voluntary standards for vehicle crashworthiness (Rehabilitation Engineering Society of North America [RESNA] Section 19 Wheelchairs Used as Seats in Motor Vehicles [WC19]) would be able to pass the Federal Aviation Administration (FAA) vertical crashworthiness standards for aircraft seating. Wheelchairs were secured using surrogate 4-point strap tiedowns using the geometry specified by WC19. The FAA Hybrid III anthropomorphic test device (FH3 ATD) was restrained by both the wheelchair-attached lap belt and a vehicle-mounted lap belt identified as necessary to pass FAA dynamic horizontal test requirements. For the dynamic vertical testing with the wheelchairs oriented 60 degrees relative to horizontal, modeling demonstrated the suitability of using the trapezoidal pulse achieved with the UMTRI sled produced rather than the typical triangular shaped FAA pulse. Of the five manual and three power wheelchairs tested, four had broken components that would not impede emergency exit, four did not have visible damage, and the FH3 remained within the seat in all tests. The three power wheelchairs did not meet lumbar compression requirements. Based on these results, it may be feasible for people to use their own WC19-compliant wheelchairs on aircraft when secured to the aircraft with 4-point strap tiedown systems, supplemented by an occupant lap belt anchored to the aircraft, notwithstanding the lumbar force requirement.
Manary, Miriam A., Orton, Nichole Ritchie, Vallier, Tyler, Boyle, Kyle J., Klinich, Kathleen DeSantis
The Influence of Spatial and Aspatial Factors on the Safety Driving Behavior of Motorcycle Couriers in the Gig-Economy Era: A Case Study of East Jakarta, Indonesia09-14-02-00057/3/2026
This study aims to analyze the impact of spatial and aspatial factors on the safety driving behavior of motorcycle couriers in East Jakarta within the context of the gig economy. Both factors are integrated to clarify how spatial conditions and individual characteristics jointly shape couriers’ safety driving behavior. The Partial Least Squares Structural Equation Modeling (PLS-SEM) method was employed to examine the relationship between spatial and aspatial factors on safety driving behavior. Data were collected through questionnaires from 253 motorcycle couriers operating in three subdistricts in East Jakarta, namely Cakung, Pasar Rebo, and Pulo Gadung. The results show that safety driving behavior is significantly influenced by aspatial factors, particularly socioeconomic characteristics and personality traits. In contrast, spatial factors such as road conditions and daily activity patterns do not directly influence safety driving behavior, but exert indirect effects through the couriers’ personality traits.
Wahyuddin, Yasser, Sitorus, Paldibo Alfriramson, Putri, Kharunia, Maharani, Garnierita
UMV Peoplemover: Towards a Modular Fully Automated Vehicle Research Platform2026-01-07847/1/2026
The UMV Peoplemover 2+2 is part of a modular vehicle family (Urban Modular Vehicle) that includes derivatives for passenger and cargo transport in urban environments. The platform supports automated movers as well as conventionally controlled vehicles with a human driver, ensuring high flexibility across applications. The modular platform enables the extensive use of common parts, allowing the efficient and cost-effective realization of multiple vehicle variants. The increased share of common parts also improves sustainability by reducing derivative-specific parts, material usage, and production complexity. A drivable demonstrator of the UMV Peoplemover 2+2 has already been realized. The vehicle is designed for the automated transport of up to four occupants in a 2+2 vis-à-vis seating arrangement and is targeted at demand-oriented shuttle services. While the drivable demonstrator validated the proof of concept, it lacked the core Level 4 hardware and software stack for automated driving functions. To address this limitation, we deployed a software-defined vehicle architecture to the concept. This paper introduces the novel e/e-architecture and software stack enabling the Peoplemover 2+2 to initiate its first shuttle service at the German Aerospace Center (DLR e.V.) in Stuttgart. We further detail the deployed multi-modal sensor suite, comprising modern solid-state LiDARs and a 4D imaging radar, which were carefully selected to meet the operational design domain requirements while also serving as a versatile research platform for future advanced perception studies. Finally, we analyze the SDV-based modular software stack, which facilitates rapid application development through straightforward switching between commercial, open-source, and in-house software domains, and supports parallel execution of domain-specific functions across all three software sources.
Pohl, Eric, Schmid, Fabian, Münster, Marco, Siefkes, Tjark, Stuebler, Tillmann, Mohammed, Shawan
Mind the Gap: Quantifying Behavioral Fidelity in CARLA Using Naturalistic Drone Data2026-01-07717/1/2026
Rigorous validation of SAE Levels 3 and 4 autonomous systems increasingly relies on simulation. However, the simulation-reality gap remains a challenge for human-in-the-loop assessments. This study empirically quantifies the behavioral fidelity of the Car-Learning-to-Act (CARLA) simulator by recreating specific real-world traffic scenarios using the high-precision exiD drone dataset. Twenty-five participants performed a series of maneuvers, including lane changes and time-critical cut-ins. Their performance was analyzed using Dynamic Time Warping (DTW), driver profiling, and Time-to-Collision (TTC) metrics. The findings reveal a clear distinction between relative and absolute behavioral validity. In strategic decision-making tasks, the simulation demonstrated remarkably high temporal fidelity. DTW analysis explained 94% of the trajectory variance. Participants initiated lane changes with an average lag of -9 frames (0.36 s) compared to naturalistic references. These results indicate that, despite the absence of peripheral optical flow, the simulator successfully elicits temporally correlated decision-making patterns suitable for assessing strategic driver intent. However, physical execution in reactive scenarios revealed significant absolute discrepancies. Although the high Pearson correlation (r ≈ 0.89) in velocity profiles proves that drivers recognize and react to hazards with realistic timing, their physical inputs were exaggerated. Participants displayed digital, over-modulated braking responses and maintained a negative safety bias of -11.26 m, a deviation attributed to the lack of vestibular g-force feedback and geometric minification. Furthermore, distinct driver profiles emerged. Risk-oriented participants exhibited a gaming effect by neglecting safety margins. In conclusion, while CARLA is highly valid for testing the temporal logic of driver interactions, absolute dynamics require calibration functions, such as force-feedback (pedal) tuning and visual deceleration cues like camera shake, to compensate for sensory limitations before it can be used for safety-critical validation.
Rebling, Patrick, Alphan, Metehan, Nenninger, Philipp
Methodological Framework for the Derivation of Objective Criteria for Ride Comfort Assessment2026-01-07657/1/2026
In recent years, the automotive industry has faced increasing pressure to accelerate development cycles and reduce costs. Simultaneously, ride comfort standards have risen due to the ongoing integration of autonomous driving functionalities. Consequently, it has become essential to ensure that ride comfort attains a high degree of maturity at the very early stages of the automotive development process. This necessitates the establishment of objective criteria that enable the reliable estimation of subjective ride comfort, utilizing simulation-based assessment methods. This study introduces a methodological framework designed to systematically translate the manufacturer specific subjective perception and assessment of ride comfort into objective descriptions using a dynamic driving simulator. The framework is conceived as a generic approach, enabling the comprehensive application to a wide spectrum of subjective ride comfort phenomena, while being specifically optimized for the challenges of the automotive industry. Employing this framework facilitates the derivation of highly detailed, objective descriptions of subjective ride comfort evaluations, which promotes the achievement of advanced ride comfort maturity for new vehicles in early development phases and supports the overall enhancement of ride comfort. The exemplary application of the framework to a transient, one-dimensional ride comfort phenomenon demonstrates its capability to derive robust objective models from subjective evaluations conducted with professional test drivers in a dynamic driving simulator environment.
Stroesser, Simon, Zwosta, Tobias, Angrick, Christian, Neubeck, Jens, Wagner, Andreas
Context-Dependent Assessment of Driving Dynamics and User Perception in Automated Driving2026-01-07577/1/2026
As automation advances and occupants transition from active drivers to passive passengers, understanding how automated driving behavior is evaluated becomes increasingly important. While longitudinal and lateral vehicle dynamics are known to influence perceived comfort and safety, it remains unclear to what extent motion–perception relationships remain stable across urban traffic contexts. This study compares two real-world investigations of automated driving: a left-turn maneuver at a signalized intersection on a test track and a roundabout maneuver with a shuttle in public traffic. Both datasets include high-resolution vehicle dynamics and structured subjective ratings. A consistent objectification approach was applied to examine the transferability of motion–perception relationships across contexts. However, differences in vehicle platform, automation level, trajectory characteristics, and study design limit direct comparability and require cautious interpretation. Despite partially overlapping ranges in selected peak-based dynamic parameters, such as longitudinal acceleration, subjective comfort and safety ratings were consistently higher in the roundabout scenario. Furthermore, strong associations were observed between motion parameters and subjective evaluations in the intersection context (adj. R2 up to 0.891), whereas objective parameters showed only limited explanatory power in the roundabout scenario (adj. R2 ≤ 0.06). The results indicate that motion–perception relationships derived within a specific context may not be directly transferable across different traffic scenarios. The findings highlight limitations of globally derived motion-based evaluation models and underline the importance of validating objectification approaches across diverse operational environments.
Panzer, Anna, Strenge, Emma, Iatropoulos, Jannes, Henze, Roman
Estimating Driver Field of View in Active Safety Systems2026-01-07757/1/2026
Driver monitoring systems are an important component of active safety systems, continuously evaluating the driver’s state and issuing real-time warnings. As defined by the SAE Levels of Automation, driving tasks are increasingly transferred from the driver to the vehicle from Level 0 to Level 2, however, the driver remains fully responsible for monitoring the driving environment. Current implementations, such as driver drowsiness and attention warning, assess driver alertness, while advanced driver distraction warning ensures that the driver maintains visual focus. Nevertheless, these systems do not identify the specific objects or regions the driver is observing. This limitation motivates the presented research question: can an in-car monitoring system be integrated with external environment perception sensors to infer the driver’s field of view (FoV)? This paper presents a system consisting of a driver-facing camera and a front-view camera. Facial features, including gaze direction, head pose, and iris offset are extracted using computer vision techniques. These features, together with cropped eye images, are used as inputs to a multi-modal network. Training labels were generated using a driving simulator study with 16 participants who sequentially fixated on visual targets displayed on a front screen. Experimental results show that the proposed system can predict driver visual attention and approximate FoV with a mean pixel error of 35.40 px, enabling identification of the regions of the road scene observed by the driver in real time. This work provides a foundation for explicitly modeling driver perception and its correspondence with vehicle perception systems.
Ji, Dejie, Lausch, Hendryk, Flormann, Maximilian, Henze, Roman
Towards a Smarter E-Bike: Enhancing Energy Efficiency through Adaptive Speed Control2026-01-07367/1/2026
This paper presents the development of a speed controller for e-bikes, designed as part of an energy-adaptive assistance system. The controller provides riders with appropriate support along planned routes, based on the available battery capacity. The control concept is intended for integration into existing commercial e-bikes without requiring extensive modifications to the drive system. Therefore, the rider remains part of the control loop, adjusting the support mode according to instructions from the controller. The speed controller is implemented as a rule-based state machine, enabling comprehensible design and parameterization. Since the rider must manually switch between support modes while riding, the control logic incorporates hysteresis and dead times to ensure stability, prevent oscillations, and avoid frequent mode switching. The user interface is a smartphone application that issues visual and audio instructions for switching support modes. An initial, system-independent version that relied on GPS-based speed measurement was found to be insufficiently accurate for the control task. Furthermore, it was found that detection of the pedaling state was essential for proper operation. To address these issues, a Bluetooth-based hardware adapter was developed to access relevant signals from the e-bike’s CAN bus communication system. These include pedal power, cadence and speed, which are made accessible through reverse engineering of the CAN bus. The proposed concept is evaluated in a chassis dynamometer study with 13 participants on two test profiles: a synthetic gradient profile for assessing control stability and a realistic elevation profile for dynamic evaluation. Additional measurements taken with one of the test riders at different speeds demonstrate the system’s reliability and its potential to improve the energy efficiency. The results show that, with approximately the same power brought in by the rider, only 27% more electrical energy is required to increase the average speed by 45%.
Rauch, Yannick, Simmann, Gabriel, Schneider, Manuel, Goss, Christian, Kriesten, Reiner
Passive Thermal Management of Battery Disconnect Units for Fast-Charging Electric Vehicles: A Comparative Study of Phase Change Material Heat Transfer Configurations2026-01-07597/1/2026
The widespread adoption of electric vehicles is currently hindered by long charging durations and limited infrastructure. While fast-charging technologies address these issues, they impose significant thermal loads on high-voltage components. Within this architecture, the Battery Disconnect Unit plays a critical role as it monitors and controls the connection between the battery, powertrain, and charging system. However, the high currents required for fast-charging often drive these units' temperatures beyond safe operating limits, necessitating advanced thermal solutions that do not require extensive redesigns of the vehicle's electrical layout. To address this challenge, this study proposes a passive thermal management solution using Phase Change Material heat transfer devices to enhance the thermal robustness of the component. The methodology employs a dual approach involving initial experimental testing to pinpoint specific thermal hotspots under high-power conditions, followed by detailed numerical simulations using GT-Power software to predict system behavior. Furthermore, the paper provides a comparative analysis of various configurations, assessing their impact on temperature reduction, response time, and thermal uniformity. The results demonstrate that appropriately designed passive solutions significantly improve thermal performance, effectively enabling higher charging power capabilities while minimizing system complexity and integration effort. This innovation provides a scalable and efficient path for improving overall vehicle performance and safety during rapid energy transfer events.
Salameh, Georges, Goumy, Guillaume, Frecinaux, Anthony, Ratajczack, Christelle, Palluel, Marlène, Noiseau, Pascal, Lardeux, Sébastien
Model-Guided Scenario Expansion for Data-Driven Autonomous-Vehicle Safety Testing: A Feasibility Demonstration12-09-04-00326/29/2026
This article presents a data-driven pipeline for autonomous-vehicle (AV) safety testing. The pipeline integrates real-world traffic observations with model-guided scenario expansion and safety-metric evaluation to enable an end-to-end AV safety testing framework, demonstrated on a canonical highway scenario. The framework enhances test diversity, realism, and coverage by generating statistically informed variants of observed driving behaviors. Key parameters such as vehicle speed, trajectories, and headways are extracted from naturalistic data and used to train a probabilistic model of traffic dynamics. Scenario variants are sampled from this model and encoded as behavior trees (BTs) for modular, simulation-ready execution. Each scenario is simulated using a consistent AV control configuration, and safety metrics such as minimum safe distance violation, minimum safe distance factor, time to collision, and aggressive driving are applied to evaluate safety outcomes independently of system-specific tuning. A case study based on the highD dataset (110,000+ trajectories) demonstrates the framework’s ability to generate realistic and safety-relevant scenarios, providing an initial demonstration of pipeline feasibility and metric-based evaluation. This initial study is intentionally scoped to a single scenario class and a simplified parametric model to isolate and validate the end-to-end integration of the pipeline.
Elshenawy, Mohamed, Aboudina, Aya, Abdelmotaleb, Anhar, Amr, Mariam, El-darieby, Mohamed
Automosphere: Exploring Vehicle Heads-Up Display with Weather-Related Windshield Cues for Passive Fatigue Management12-09-03-00256/26/2026
Passive fatigue can cause accidents with automated and regular vehicles. A proof-of-concept prototype [made with light-emitting diode (LED) matrices and white LED (WLED)] and a preliminary comparative usability test (N = 7) are used to study whether the active manipulation of simulated weather cues can be a potential countermeasure to passive fatigue. Participants rated system suitability, system impression, and their fatigue level similarly when they viewed a weather windshield heads-up display (HUD) versus a speedometer windshield HUD [no significant differences found and relatively small 95% confidence interval (CI) ranges around 0]. Qualitative analysis of interviews found that participants saw the potential value of the weather display and that display placement, dynamic graphics, and user activation were commonly mentioned themes. These results suggest the concept is theoretically possible, though further work is needed to prove the concept in practice.
Ensafjoo, Mohsen, Li, Jamy
BO-NRPPO: A Bayesian Optimization–Tuned Proximal Policy Optimization Framework for Semi-Active Suspension Control10-10-03-00256/25/2026
Semi-active suspension systems enhance ride comfort and handling performance by adaptively modulating damping characteristics. However, conventional model-based controllers often fail to maintain optimal performance under uncertain and time-varying vehicle conditions. This article proposes Bayesian Optimization–Tuned Proximal Policy Optimization with Non-Parametric Rewards (BO-NRPPO), a novel reinforcement learning (RL) framework that integrates Bayesian Optimization (BO) with Proximal Policy Optimization (PPO) and a non-parametric reward function (NRF). The proposed approach enables adaptive self-tuning, data-driven reward shaping, and uncertainty-aware policy learning. Moreover, a Trapezoidal Simple Moving Average (TSMA)–based reward normalization scheme is introduced to accelerate convergence and stabilize training. Simulation results across diverse driving scenarios demonstrate that BO-NRPPO outperforms the passive suspension, the classical Linear Quadratic Regulator (LQR), and PPO with parametric rewards. Specifically, compared to the passive suspension and the LQR baseline, BO-NRPPO achieves up to 6.63% and 5.14% improvements in handling stability, respectively. Concurrently, it delivers maximum enhancements of 46.96% and 42.55% in ride comfort over these two baselines. For real-world vehicle applications, this adaptive self-tuning capability significantly reduces the time-consuming manual calibration efforts typically required in chassis development. Furthermore, Hardware-in-the-loop (HiL) validation confirms its real-time applicability and robustness under uncertain driving conditions, highlighting its immense potential as a scalable intelligent suspension control solution.
Chen, Guoying, Wang, Xinyu, Wang, Jiaqi, Zhan, Xinwang, Bi, Chenxiao, Cong, Shiqi, Hua, Min, Sun, Tianjun, Gao, Zhenhai
Female PMHS Thoracic Biomechanical Response Corridors with Preliminary Consideration of the Influence of Breast Tissue in Frontal Impacts2026-22-00066/24/2026
Thoracic injuries are common for belted occupants in frontal motor vehicle crashes. However, there remains a lack of female post-mortem human subject (PMHS) data in the literature to generate female-specific biomechanical response corridors and evaluate engineering tools such as anthropomorphic test devices (ATDs) and computational human body models (HBMs). Additionally, the effect of breast tissue on thoracic response has not been directly investigated despite female ATDs and HBMs having features representing breasts. As such, this study sought to utilize simplified frontal hub impacts to (1) generate female PMHS thoracic response corridors both with breasts positioned with a bra and without breasts (no bra) and (2) preliminarily explore the influence of breasts on the thoracic responses of female PMHS. Twelve female PMHS (9 small and 3 midsize) were subjected to frontal impacts at mid-sternum with a 14.0 kg circular impactor at 4.3 m/s in conditions with and without breasts. Force versus deflection (FD) response corridors were generated, and comparisons were made between groups and to scaled FD corridors representing female response. Overall, female PMHS with and without breasts displayed differences in FD response compared to scaled corridors in terms of the shape of the initial response and peak force and deflection. Additionally, female PMHS with breasts produced lower peak force and greater peak deflection compared to those without breasts. These results suggest the importance of collection and evaluation of female biomechanical data that can be used for continued evaluation of female-specific safety tools as well as the further reduction of injury risk for all occupants during motor vehicle crashes.
Baker, Gretchen H., Kang, Yun-Seok, Marcallini, Angelo, Lang, Ryan, Hutter, Erin, Moorhouse, Kevin, Agnew, Amanda M.
Reduced Effort Brakes and Reduced Effort Vacuum Powered Brake Backup SystemsJ2671_202606 (Current)6/22/2026
This Information Report relates to a special class of automotive adaptive equipment which consists of modifications to the power brake booster systems provided as original equipment of motor vehicles. These modifications are generically called "Reduced Effort Power Brakes" (REPB) The purpose of the modification is to lower the amount of driver effort required to apply the brakes. Retention of reliability, ease of use and maintainability for disabled drivers, passengers, and the general public is of primary concern. Reduced Effort Power Brake modifications should be qualified by the tests referenced in the Recommended Test Procedure. The tests set forth in that procedure should be applied, and failure of a Reduced Effort Power Brake modification to meet those tests should disqualify the modification from the claim of meeting the specifications of this Information Report. Because this is an Information Report, the numerical values for performance measurements presented in this report and in the accompanying Test Procedure, while based upon the best knowledge available at the time, have not been validated by a testing of the Test Procedure.
Adaptive Devices Standards Committee
Spatial Noise Acquisition at the Driver’s Position in an Electric Vehicle with Increased Accuracy2026-01-07236/20/2026
In vehicles with electrified powertrains, high-frequency tonal noise components have become increasingly prominent and can be perceived as particularly annoying by the driver. While recent advancements in international standardization — such as ECMA-74 [1] and ECMA-418 [2] — have led to powerful new algorithms for tonal noise visualization and analysis, including Tonality-Heatmaps, the measurement side still lacks sensor setups that adequately reflect the spatial sensitivity of noise, especially for tonal components. This challenge is amplified in enclosed vehicle cabins, where room modes create local minima and maxima that become increasingly dense at higher frequencies. As a result, even small head movements can lead to noticeable differences in perceived tonal noise. Current measurement approaches do not sufficiently account for this spatial variability. This contribution addresses the absence of tailored solutions for the driver’s position by introducing an improved microphone arrangement that significantly reduces the uncertainty of measured noise levels. The proposed setup considers spatial variability without compromising comfort or crash safety requirements. By enhancing the precision of tonal noise quantification, this approach provides noise-vibration-harshness (NVH) engineers with a valuable complement to modern software-based tonal analysis methods. The paper discusses the technical implementation constraints and demonstrates the comparability of the new measurement technique with conventional setups.
Schecker, Daniel, Rittenschober, Thomas
Field-Based Evaluation of Physiological Fatigue Reduction in Fuel Cell Refuse Collection Vehicle Using Heart Rate Variability2026-01-06906/20/2026
Understanding the physiological impact of vehicle electrification on operators remains an important but underexplored issue in commercial vehicle research. This study quantitatively evaluates the physiological fatigue of drivers and onboard crew members during real-world operation of commercial refuse-collection vehicles by comparing a diesel-powered vehicle with a fuel cell electric vehicle (FCEV). Both vehicles were operated on the same routes under comparable real-world operating conditions, including similar time periods and operational tasks, during municipal waste collection service. Heart Rate Variability (HRV) metrics were obtained from R-R interval (RRI) data recorded using a Polar heart rate sensor. The Root Mean Square of Successive Differences (RMSSD), a time-domain index reflecting short-term parasympathetic activity, and Poincaré (Lorenz) plot area (LP area), a nonlinear HRV index reflecting overall autonomic nervous system modulation, were calculated. In-cabin vibration and noise levels were also measured as supplementary context to support the interpretation of physiological responses. The results indicate that both RMSSD and LP area were higher during FCEV operation than during diesel vehicle operation. For the driver, RMSSD increased by approximately 61.65% and the LP area by approximately 49.91%. For the onboard crew member, RMSSD increased by approximately 18.79% and the LP area by approximately 46.02%. These findings suggest a consistent association between reduced vibration and noise characteristics in the FCEV and increased HRV indices, indicating reduced physiological fatigue during operation. This study provides quantitative evidence that fuel cell electric commercial vehicles are associated with improved occupational conditions, extending beyond conventional environmental benefits.
Utsumi, Atsuko, Yakoh, Takahiro
Vibro-Acoustic Simulation for Vehicle Audio Design2026-01-06706/20/2026
Acoustic user interfaces and audio experiences are among the leading comfort factors in new vehicle interior designs. OEMs are more and more focusing on loudspeaker design and positioning, to provide the most immersive experience to the customers. The industrial target is to be able to predict the performance of an audio system in early design phases. This paper presents an integrated vibro-acoustic methodology enabling early-stage prediction of loudspeaker performance in real vehicle conditions. The approach combines electromechanical characterization, a hybrid loudspeaker calibrated model valid across the audible range and coupled FEM/BEM/SEA simulations to capture the loudspeaker response in the vehicle’s cabin considering door-installation effects and cabin acoustics. The method is validated experimentally on a rear-door loudspeaker installed in a production vehicle, showing strong correlation with measured SPL. A final application case demonstrates its capability to assess the impact of alternative speaker mounting positions during the design phase.
Zerrad, Mehdi, Errico, Fabrizio, Mordillat, Philippe
Unsupervised Feature Frequency Map Extraction for Simple C-EPS Defect Detection2026-01-07176/20/2026
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) based framework trained on NVH datasets collected from normal and defective Column-type Electric Power Steering (C-EPS) systems. Latent variables at the bottleneck layer are used for dimension reduction, enabling visualization and unsupervised classification using a clustering algorithm. A classification model derived from the encoder is fine-tuned with clustered data, and Gradient-weighted Class Activation Mapping (Grad-CAM), an eXplainable AI (XAI) technique, is applied to extract Feature Frequency Maps (FFM) highlighting defect-related noise and vibration characteristics. The proposed approach does not rely on the deep learning model to directly classify defect. Instead, it utilizes extracted FFM as weights(mask) to detect defect. This method enables quantitative data representation and ensures high applicability with existing EOL equipment. Post-processing within the FFM enables root cause analysis, reducing issue resolution time and supporting integration with conventional signal analysis techniques.
Park, Jun-Seo, Jo, Hyeon-Choel, Cho, In-Je, Seo, Jae-Yong, Yoo, Seong-Sik
Experimental Validation of Frame-Dependent Gyroscopic Frequency Splitting in a Freely Rotating Tire Using Wireless MEMS Accelerometers and Scanning Laser Doppler Vibrometer2026-01-07006/20/2026
Gyroscopic effects split circumferential traveling-wave resonances of rotating structures into forward and backward branches. This work first analyzes the splitting in the co-rotating (Lagrangian) frame to provide physical intuition for the evolution of the two branches with spin speed. A transformation to the inertial (Eulerian) frame is then derived, showing that the observed frequencies are shifted by a kinematic Doppler-like term that acts with opposite sign on the forward and backward waves, leading to different Campbell-diagram slopes depending on the observation frame. The resulting framework is validated experimentally on a freely rotating, unloaded tire using two complementary sensing modalities: wireless on-tire accelerometers (co-rotating view) and a scanning laser Doppler vibrometer (inertial view). A frequency-domain SVD-based identification (FDD/ODS-SVD) is used to extract poles and deformation patterns over a range of spin speeds, enabling Campbell diagrams in both frames. The application of the proposed transformation maps the co-rotating branches onto the inertial observations, yielding consistent forward/backward splitting between the two measurement systems.
del Fresno Zarza, Javier, Naets, Frank
Control Measures for Laser Safety in the Navigable AirspaceARP5572B (Current)6/15/2026
This SAE Aerospace Recommended Practice (ARP) provides information and guidance for the control of hazardous laser exposure in the navigable airspace. This ARP does not address techniques that pilots can apply to mitigate laser illuminations during a critical phase of flight. Such mitigation strategies are described in ARP6378.
G-10T Laser Safety Hazards Committee
Safety Considerations for Lasers Projected in the Navigable AirspaceARP5293B (Current)6/15/2026
This document applies to laser proponents involved with the use of laser systems outdoors. It may be used in conjunction with AS4970, ARP5535, ARP5572, and the ANSI Z136 series of laser safety standards.
G-10T Laser Safety Hazards Committee
Safety Considerations for High-Intensity Lights (HIL) Directed into the Navigable AirspaceARP5560A (Current)6/15/2026
This document applies to regulatory/approving authorities involved with decisions regarding the use of high-intensity light (HIL) directed into the navigable airspace. For the purpose of this document, lights greater than 0.25 million candlepower meet the minimum threshold of an HIL. Lights not directed or reflected into the navigable airspace are not usually considered to interfere with aircraft operations. HILs include laser-derived light sources; other laser systems are beyond the scope of this document. This document addresses adverse effects of HILs on humans, such as visual interference. HIL effects on Unmanned Aircraft Systems (UASs) are beyond the scope of this document.
G-10T Laser Safety Hazards Committee
Ergonomic Assessment of Glove-Box Design in Passenger Vehicles for the Indian Scenario15-19-02-00096/10/2026
Passenger vehicles experience severe packaging constraints around the instrument panel, rendering glove-box operation a critical yet ergonomically underexplored interaction. Although glove-box interaction occurs frequently during routine vehicle use, its potential implications for ergonomic risk remain largely unexamined in existing automotive research. To isolate the influence of driver-side packaging constraints from component-level design effects, this study adopts a comparative evaluation of driver and co-driver glove-box interaction as a built-in control condition. This study introduces a discomfort-based evaluation framework that integrates Digital Human Modeling with India-specific anthropometric datasets. A composite loss-function scoring model is developed to quantify functional usability differences across four glove-box configurations, defined by variations in latch placement (center or side) and storage-bin mechanisms (fixed or rotating). Indians are utilized to assess reachability and visibility during glove-box interaction. Ergonomic performance is analyzed through reach and visibility metrics for both latch actuation and storage-access tasks. For the co-driver, all configurations exhibit 0% loss, confirming that usability remains unaffected. In contrast, the driver assessment reveals pronounced limitations. Center-mounted latches prove inaccessible from a neutral seated posture, reflecting an approximate loss function of 55%. Among the side-latch alternatives, the rotating-bin configuration achieves the lowest discomfort score (41%), supported by more favorable access posture and smoother hand-entry alignment. The findings specify that ergonomic limitations stem primarily from driver-side packaging constraints rather than inherent flaws in the glove box unit. Based on the reach and visibility loss values obtained through the developed framework, the Side-Latch + Rotating-Bin configuration emerges as the most suitable design option for passenger-vehicle layout. The proposed methodology offers a practical decision-support tool for early stage ergonomic evaluation of glove-box configurations in passenger vehicles.
Jujjavarapu, Sreeram, Kota, Srinivas, Kotkunde, Nitin, Jasti, Naga Vamsi Krishna
Can Large Language Models Enable Sustainable Driving Behavior? A Study on Large Language Model-Based Eco-Driving13-07-02-00066/9/2026
Large language models (LLMs) have shown remarkable capabilities for perceiving driving environments and making interpretable, logical decisions for autonomous driving. However, their potential for more comprehensive driving strategies, especially concerning energy efficiency, remains underexplored. Most existing studies primarily focus on driving safety, which may inadvertently increase energy consumption. To address this issue, this study explores the use of LLMs as high-level controllers to jointly optimize driving safety and energy efficiency. A textual prompt is designed for the LLM, incorporating few-shot examples that describe scenarios, states, and actions. The LLM processes the scenario and state prompts describing the surrounding traffic environment. It generates a high-level control signal, which is then translated into low-level vehicle motion commands in a high-fidelity traffic simulator with realistic physics, vehicle dynamics, road slopes, and network topology. Experiments in campus-scale digital twin car-following scenarios demonstrate that the proposed LLM-based framework achieves an average reduction of 4.16% in energy consumption compared to the reinforcement learning paradigm, while maintaining driving safety and providing interpretable high-level decision-making. This study highlights the potential of LLMs for longitudinal eco-driving applications under the evaluated simulation settings, extending previous LLM-based autonomous driving research that primarily focused on safety to also consider energy efficiency.
Wang, Haoyu, Li, Zhenning, Wang, Siying, Zhou, Zijing, Zhang, Xiang, Yang, Zhifeng, Ou, Shiqi (Shawn), Qi, Hao
Autonomous Aircraft Docking: A Vision-Based Approach to Aircraft Parking Systems2026-26-07866/1/2026
Automated aircraft parking systems enhance airport ground operations by enabling precise and autonomous docking of aircraft at gates. These systems reduce turnaround time, minimize human error, and optimize apron space through real-time object detection, obstacle avoidance, and dynamic path planning. Unlike fixed guided-path methods, the proposed system adapts to congestion and environmental conditions such as low visibility, ensuring safety and efficient maneuvering. Validation through simulation demonstrates the system’s potential to improve operational resilience and support scalable automation in future airport infrastructure.
Penugonda, Navya Sunaina, Ediga, Venkatadiwakar Goud
Adaptive HVAC Strategies for Enhanced Cabin Thermal Comfort, Air Quality and Energy Efficiency2026-26-07916/1/2026
Passenger comfort within vehicles and aerospace cabins relies on finely tuned management of temperature, air quality, and energy use. This paper proposes an integrated HVAC framework that combines zonal climate control, intelligent airflow distribution, and real-time sensor data to maintain thermal balance across different cabin zones. Leveraging predictive thermal load modelling and machine learning, the system anticipates environmental changes—such as sudden shifts in external temperature or passenger load—and proactively adjusts heating and cooling outputs. Simultaneously, air quality is enhanced through a multistage filtration system, active air purification technologies, and dynamic CO₂ concentration monitoring. Comfort assessment integrates PMV (Predicted Mean Vote) and PPD (Predicted Percentage Dissatisfied) indices to adapting environmental conditions. Simulations and early-stage prototypes improve energy savings and improve occupant comfort and air quality. The proposed HVAC approach is a promising avenue for enhancing passenger experience and operational efficiency in both ground and air mobility platforms.
Mudavath, Lehitha Sai, Patil, Ashish, Saha, Sudipta
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
A GenAI-Based Approach for Real-Time Multilingual In-Flight Communication Systems2026-26-07846/1/2026
In today’s global aviation industry, passenger experience is strongly influenced by effective communication. In-flight announcements, often limited to English and a single local language, can create confusion and stress for international travelers who may not be fluent in either. This communication gap not only impacts passenger comfort but also poses potential risks in conveying time-sensitive or safety-critical information. Recent advances in Generative Artificial Intelligence (GenAI), particularly in speech recognition, neural machine translation, and naturalistic text-to-speech, provide a pathway to overcome these challenges. This paper explores the concept of real-time multilingual in-flight announcements delivered in each passenger’s preferred language through connected headphones or personal devices. The proposed system architecture integrates speech-to-text conversion, language translation, and speech synthesis with aircraft infotainment platforms. Potential applications range from pre-generated multilingual safety messages to long-term visions of fully personalized, real-time translations with minimal latency. Benefits include improved inclusivity, accessibility for hearing-impaired passengers, and enhanced brand differentiation for airlines. Challenges such as regulatory certification, translation accuracy, latency constraints, and hardware integration must be addressed. Beyond aerospace, this capability has cross-domain relevance in automotive, railways, and public services, making it a promising area for future customer experience innovations.
Mishra, Ashwini, Kature, Kartik, Patil, Ashish
Zero Waste Manufacturing in Aerospace: Circular Design Approaches from Concept to End-of-Life2026-26-07756/1/2026
Achieving zero-waste manufacturing in aerospace requires a shift from end-of-pipe waste mitigation toward circular design principles embedded early in product development. This paper presents a practical framework for integrating circularity into aerospace systems through five design pillars: design for modularity and disassembly, material substitution to enhance recyclability, waste segregation and characterization, component-level circularity readiness scoring, and collaborative supplier engagement. To operationalize this approach, a Circularity Readiness Assessment Tool (CRAT) is developed to evaluate design alternatives against criteria such as disassembly ease, material recyclability, manufacturing waste potential, end-of-life recovery pathways, and supplier take-back mechanisms. The framework supports multi-criteria decision-making by complementing traditional aerospace design drivers including weight, performance, cost, and safety. The methodology is demonstrated through a case study of an aircraft seating system. Scenario-based analysis indicates that targeted circular design interventions can reduce material waste and lifecycle carbon emissions while maintaining functional and regulatory requirements. Emphasizing practical engineering workflows rather than exhaustive lifecycle modeling, this work provides a scalable foundation for embedding circular design into aerospace product development and advancing zero-waste manufacturing objectives.
S, Chaitra
Non-Intrusive Fatigue Detection for Pilots2026-26-07936/1/2026
Pilot fatigue represents a critical concern in aviation safety, as it can significantly impair cognitive functions, decision-making abilities, and reaction times. In addition to decreasing performance, in-flight chronic fatigue has negative long-term health effects. Possible causes of fatigue include sleep loss, extended time awake, circadian phase irregularities and workload. Conventionally, the risk due to fatigue in aerospace is reduced by flight time limits and controlled rest requirements. Despite regulations limiting flight time and enabling optimal rostering, fatigue cannot be prevented completely. Hence, there is need to detect pilot fatigue in real time. There is ongoing research to detect pilot fatigue using devices that can capture Electroencephalogram (EEG) and Electrocardiogram (ECG). Though these devices have high fidelity, they are intrusive and can limit pilot activity. This limitation could potentially be overcome by non-intrusive devices such as a smart watch/wrist band/goggles which can measure physiological parameters that provide insights into pilot’s mental health. Heart rate variability (HRV) is one such physiological marker of interest for detecting pilot fatigue in real time. HRV can be effectively derived by processing raw Photoplethysmography (PPG) signals to gain insights into the autonomic nervous system, enabling the assessment of physiological state. Wearable devices such as a wristwatch are used in the current study to measure PPG data. Time and frequency domain analysis were performed to evaluate the potential of HRV indices. The analysis of R-R intervals and the Low Frequency / High Frequency (LF/HF) ratio plots, derived from HRV signals, revealed distinct characteristics that differentiate between an alert and a fatigued pilot. This study demonstrates a reliable non-intrusive method for detecting pilot fatigue and enhancing flight safety.
Nyamagoudar, Vinayak, P R, Namratha, Ramachandran, Venkataramani
AI-Led Sustainability Strategy: Driving Product Value from Birth to Disposal2026-26-07816/1/2026
Aerospace products operate within highly complex, safety-critical environments and endure extended lifecycles, often spanning decades. Sustaining their operational value requires rigorous management of Safety, Reliability, and Availability (SRA), while global Environmental, Social, and Governance (ESG) mandates demand parallel progress toward sustainability goals. This paper introduces an AI-driven strategy that integrates these dual imperatives—Sustenance Management and Sustainability Management—within a unified Product Lifecycle (PLC) framework. The proposed approach leverages Artificial Intelligence across five PLC phases: Generative Design, Detailed Design & Verification, Manufacturing & Industrialization, Operations & Maintenance, and End-of-Life Circularity. Anchored by a certified Digital Thread, this framework ensures seamless, auditable data flow from concept to disposal. Using Life-Limiting Parts (LLPs)—such as high-stress turbine discs—as a case study, the paper demonstrates how AI interventions enhance operational efficiency while reducing embedded carbon emissions. For example, Generative AI optimizes component geometry for performance and material efficiency, Physics-Informed Machine Learning (PIML) improves Remaining Useful Life (RUL) predictions for certification readiness, and predictive analytics extend Time-on-Wing (ToW), deferring Scope 3 emissions from replacement manufacturing. At end-of-life, AI-guided valuation of Used Serviceable Material (USM) enables circularity and compliance with ISO 14067 and ISO 14040/14044 standards. The paper also discusses sustainability metrics such as Design Simulation Energy Intensity (DSEI) and the Sustainable AI Quotient (SAIQ) [25], to address the AI-energy paradox, ensuring that digital transformation remains net-positive for environmental stewardship. By positioning sustenance as the most immediate lever for sustainability, this AI-led framework delivers measurable improvements in lifecycle cost, operational resilience, and carbon footprint reduction. The discussion concludes with challenges in data governance, regulatory compliance, and model explainability, offering mitigation strategies for safe and scalable adoption.
Srinivasan, Karthik, G.V.V., Ravi Kumar, Vaderahobli, Devaraja Holla, Bhate, Ujwal, Veluri, Sastry
Mitigating Cracking in COP Optics through Annealing and Step-Torquing2026-26-07646/1/2026
Polymeric optical materials such as Cyclo Olefin Polymer (COP) are adopted in aerospace lighting systems due to their excellent optical clarity, dimensional stability, moldability and weight saving advantages over glass. However, their relatively low toughness and the presence of residual molding stress make them prone to crack initiation during mechanical fastening. During its installation, crack formation was consistently observed around self-tapping screw interfaces, raising concerns over reliability, maintainability, and compliance with durability requirements. A structured Design of Experiments (DOE) was performed to identify root causes and evaluate potential mitigation methods. The investigation revealed that residual stresses in the COP material, combined with localized stress concentrations during screw tightening, were the primary drivers of crack initiation. Two complementary process improvements were identified and validated as part of mitigation plan: (i) annealing of the optics prior to assembly to relieve internal stress, and (ii) using step-torquing method to fasten the screws, to gradually distribute applied loads and reduce localized stress peaks. Post-assembly observation over three days confirmed a significant reduction in crack initiation. The combined annealing and step-torquing approach demonstrated a substantial reduction in crack generation probability, providing a practical and repeatable process for enhancing the robustness of polymeric optic assemblies. This work contributes a generalizable methodology for mitigating assembly-induced failures in advanced polymer materials and supports broader adoption of lightweight, high-performance optics in aerospace applications.
S, Nikhil, Singh, Abhimanyu Kumar, Katageri, Praveen, SP, Pradeep, Chandra, Praveen
Automating DO-178C Verification and Security Compliance in DevSecOps CI/CD Pipelines for Safety Critical Avionics Software2026-26-07176/1/2026
This paper presents an automated framework for security compliance and quality assurance in DevSecOps CI/CD pipelines, specifically designed for safety-critical avionics software. The framework integrates regulatory compliance checks, security validation, and robust verification directly into the software development lifecycle, supporting continuous integration and delivery for aerospace applications. Automated processes such as code compilation, coding standards compliance, Cyclomatic Complexity Measurement, Sources Line of Code and CRC validation on target hardware are seamlessly orchestrated to maintain consistency and reliability. The system generates comprehensive compliance reports, highlights coding standard violations and security issues, and notifies relevant stakeholders to facilitate timely resolution and corrective actions. As new code is checked in, the framework automatically initiates all verification and compliance tasks, ensuring that every software update is thoroughly validated without manual intervention. Daily automated testing and coding standards checks are performed to maintain ongoing software quality and compliance. By automating key verification and compliance activities, the framework minimizes human error and supports efficient regulatory compliance throughout the development process. Integration of these capabilities within DevSecOps pipelines enables rapid, repeatable, and auditable software releases, significantly reducing manual effort and accelerating delivery of high-quality builds to customers. The framework enhances digital verification, validation, and certification readiness by providing comprehensive evidence required for regulatory audits, ultimately improving overall project assurance and reducing technical debt for aerospace software teams. These automation techniques collectively help organizations achieve verification processes of DO-178C standards more effectively, ensuring that safety-critical software meets stringent industry requirements while streamlining the certification process, reducing time-to-market, and enabling faster deployment of reliable solutions to end users and stakeholders.
Bhagwat, Shashank Ravi, Changappa, Naveen Kumar, Nath, Sunny
Augmented Reality and Multimodal Interfaces for Astronaut Training and In-Orbit Operations2026-26-07966/1/2026
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) procedures and workload-sensitive adaptation; and (iv) simulation-backed datasets across representative procedures indicating approximately 20 to 30 percent task-time reduction and approximately 40 to 50 percent error- rate reduction under controlled conditions (based on ten procedures and twenty-four simulated sessions for workload analysis). The findings reinforce that AR/MMI deployment can improve training throughput, reduce crew fatigue, and increase safety margins when designed with evidence gating, conservative confidence thresholds, and robust fallback modes. Recommendations include establishing a Human Space Flight Centre (HSFC) AR/MMI laboratory, conducting structured A/B validation trials, and committing resources for progressive demonstrations aligned with future in-orbit operations.
Yadav, Anoop Singh
Hitting the road IN THE ALL-NEW VNR26TOFHP06_016/1/2026
Volvo Trucks' revised VNR brings updated safety tech, improved fuel economy and driver comfort features to the regional haul segment. Volvo Trucks has continued its rollout of new models for every sector of the commercial truck market. The redesigned VNR is the latest model to see the spotlight. The new VNR naturally carries all of Volvo's latest safety tech, but also prioritizes maneuverability, fuel efficiency and configurability for a wide variety of fleet uses. “The VNR is an incredibly versatile truck,” said Maddie Sullivan, product marketing manager. “There are so many different configurations to meet our customer's needs. We offer four different cab sizes, three different axle configurations and two different chassis configurations.”
Wolfe, Matt
Kenworth's new C580 clocks in for work26TOFHP06_116/1/2026
Kenworth's new C580 vocational truck made its debut at CONEXPO 2026. The C580 is the replacement for the long-serving C500 and aims to build on that truck's legacy thanks to new tech, more muscle and improved interior amenities. According to Kenworth, the C580 rides on the C500 platform, but has been endowed with Kenworth's latest cab, which brings modern comfort and technology features. Truck & Off-Highway Engineering was in attendance for Kenworth's introductory press conference for the C580 in Las Vegas.
Wolfe, Matt
Items per page:
1 – 50 of 20842