Browse Topic: Education and training
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
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
Realistic seat vibration reproduction is essential for delivering authentic haptic cues and enhancing driver immersion in driving simulators. Unlike direct playback of road recordings, simulator applications require vibration synthesis that responds interactively to driver inputs and vehicle dynamics. Reproducing these vibrations at the seat is often complicated by actuator bandwidth limitations and the dynamic behaviour of the seat structure itself, which can alter the intended target response. This work presents vibration synthesis and seat dynamics compensation strategies implemented on a single-axis seat vibration reproduction system equipped with a vertical actuator. Frequency Response Functions (FRFs) were measured to characterise the system dynamics under single-axis excitation. Run-up and coast-down tests were conducted on the seat and compared to target responses measured on an actual vehicle under operational conditions. Several seat dynamics compensation strategies were
This digital standard is a requirements extract of AS13001A Delegated Product Release Verification Training Requirements. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC.
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
This standard establishes the common requirements for training of DPRV personnel for use at all levels of the aerospace engine supply chain. This standard shall apply when an organization elects to delegate product release verification by contractual flow down to its suppliers (reference 9100 and 9110 standards) and to perform product acceptance on its behalf. It is intended that organizations specify their DPRV requirements through the application of AS9117. While the delegating organization will use the AS13001 standard as the baseline for establishing DPRV process and product training, it may include additional contractual training requirements to meet its specific needs. The DPRV training material was primarily developed for aerospace engine supply chain requirements. However, this standard may also be used in other aerospace industry sectors where a DPRV process requiring specific training can be of benefit.
Flight simulations are critical for aerial firefighting training, but realistic modelling of aircraft-atmosphere interactions within fire scenarios is particularly challenging. To this end, a two-way-coupled flight simulation system, the Daedalus I framework, has been developed at the University of Glasgow for helicopter firefighting research applications. This paper presents the initial results from flight experiments conducted with different coupling schemes between the rotorcraft model and the GPU-accelerated Lattice Boltzmann atmosphere model within the system. The two-way coupling scheme was first validated using an isolated, transient rotor case. To quantify differences in pilot control and strategy between the two-way, fully-coupled rotor-atmosphere method and two (2) one-way, superposition-based coupling methods, a series of flight experiments were conducted using the bimodal modification of the McRuer pilot model representing human pilot controls, in conjunction with objective
Historical rotor designs for Earth and Mars have typically landed at thrust-weighted solidities of ∼0.1-0.15 as a best compromise of performance and weight. Comprehensive analysis predicts that high solidity rotor designs of more than twice this range have the potential to significantly increase the lift capability of future Mars explorers severely limited by packaging and weight. However, there is limited existing experimental data of high solidity rotor designs at representative densities to quantify the efficiency impact and verify models of the aerodynamic environment. Therefore, the Mars Exploration Program (MEP) funded a joint test campaign between NASA's Jet Propulsion Laboratory, NASA Ames Research Center, and AeroVironment, Inc.to validate performance predictions for low- and high- solidity rotor variants at Mars pressures. Experimental setup, test matrix, data processing, data quality, and performance results for the High Solidity Test (HST) campaign are presented and
The FAA VR-HeliSTART (Virtual Reality-Helicopter Simulator Training for Airplane to Rotorcraft Transition) is a 15-week study conducted at Marshall University (WV) to determine the effectiveness of an H125 VR reduced-motion platform simulator in training fixed-wing pilots to fly helicopters. 11 students received three four-week blocks of instruction from certified flight instructors in the flight simulator, each followed by evaluations in both the simulator and an actual H125 helicopter, covering 36 maneuvers drawn from the commercial helicopter Airman Certification Standards. A mixed-methods approach combined objective flight parameter analysis with subjective assessments from evaluators, instructors, and students. Results indicate broadly positive transfer of training, with students demonstrating at least private pilot level performance on 70% or more of maneuvers on their first helicopter flight, and consistent improvement across subsequent evaluations. However, specific areas of
This paper introduces a robust supervised machine learning framework for estimating helicopter gross weight during the takeoff phase. The methodology leverages high-fidelity datasets from Airbus's global in-service fleet to ensure a reliable training foundation. At the core of the approach is a long short-term memory recurrent neural network, supported by a patented data-curation pipeline designed to maintain high data integrity. To align with rigorous aviation safety standards, the study outlines a learning assurance process compliant with EASA guidelines, specifically addressing safety assessment objectives for machine learning. A central innovation is the characterization and monitoring of the model's operational design domain through multidimensional functional principal component analysis. By projecting high-dimensional, non-linear sensor data into a manageable tabular subspace, this approach enables the definition of safety envelopes using explainable and efficient classical
This paper presents a spatio-temporal graph neural network (STGNN) centric approach to enable heterogeneous agents to collaborate and cooperate for different types of missions. The STGNN-centric approach and corresponding autonomy are encapsulated in the Advanced Graph-enabled Network Technology for Collaborative Autonomous Agents (AGENTCA) technology. Various decentralized and distributed control architectures are reported in the literature, but in some instances these approaches do not leverage the inherent graph network which can increase scalability to larger teams and algorithmic efficiency. Specifically, in this paper advances in artificial intelligence are leveraged to parameterize and encode optimal, or nearly optimal, swarm control techniques. For this work, the team focused on developing a diffusion-based STGNN swarm controller using imitation learning. An expert, centralized swarm control law was used to guide the STGNN during the learning process. The STGNN controller
The U.S. ARMY Primary Helicopter Center/School, USAPHC/S, was activated at Fort Wolters on September 26, 1956. Located in north-central Texas, the school would train over 40,000 helicopter pilots during 17 years of operation, through the end of the Vietnam War in 1973. Approximately 95 percent of all helicopter pilots who flew in Vietnam would pass through Wolters. Students included active-duty Army Officers, Warrant Officer Candidates, and Officers representing 33 allied countries. They trained for 16 weeks at Wolters and then another 16 weeks of advanced training at Fort Rucker, Alabama before earning army aviator wings. At the peak of activity in 1968, Wolters was sending 608 pilots per month to Fort Rucker. Students flew a total of 1,285 piston-powered OH-13, OH-23D, and TH-55A training helicopters departing out of three different heliports. It is a mystical place that still lives in the history of Army Aviation through the helicopter pilots who trained there. This is their story.
The rapid expansion of electric aviation and eVTOL operations introduces tightly coupled challenges related to energy‑constrained aircraft design, battery and thermal management, mission planning, and the generation of certification‑relevant evidence. This paper presents an integrated simulation workflow developed by AVL, Unisphere, and blueflite that combines high‑fidelity electric powertrain and battery models with a guidance‑level, digital‑twin‑based 4‑D trajectory simulation driven by historical weather and operational constraints. At each mission time step, the trajectory layer provides time‑resolved environmental and routing conditions, while the system‑level models compute instantaneous power demand, state‑of‑charge evolution, and thermal response, enabling mission feasibility assessment under realistic wind, temperature, and airspace effects. The workflow is calibrated and validated using flight telemetry from blueflite's active eVTOL cargo aircraft development, ensuring
Pilot compensation — the effort required to maintain task performance in the face of deficient vehicle characteristics, as rated on the Cooper–Harper Handling Quality Rating (HQR) scale – is the task-performance-anchored measure of workload. While it has traditionally been inferred from control activity alone, recent work shows that eye-movement activity carries complementary information: as compensation rises, control inputs increase while visual scanning narrows, so neither channel alone captures the full picture. This paper proposes the pilot action metric, which combines control-stick and eye-movement activity rates so that both channel responses reinforce the compensation signal. A shared-slope regression model with per-pilot intercepts is evaluated via leave-one-out cross-validation on 16 simulator runs flown by three military test pilots across four mission task elements. The combined metric succeeds where either channel alone fails, reproducing 94% of ratings to within ±1 HQR
Prior work demonstrated that acceleration washout in motion simulators produces decay-rate sensing ambiguity within the vestibular system, forcing pilots to rely on visual cues for control. While Pilot Induced Oscillation Ratings (PIORs) for flight and simulation have been matched using different sensing thresholds, a quantitative basis for the 50% reduction in the visual decay-rate threshold has remained elusive. This paper provides evidence that pilots perceive decay rate proprioceptively through stick force during both flight and simulation, rather than through vestibular or visual channels. The residues of the stick-force sensitivity transfer function reflect the amplification or attenuation of neighboring zeros and poles; when these residues fall outside the human's 30 dB tactile sensory window, the resulting decay rate becomes imperceptible. Modeling reveals that stabilization via the visual channel in simulators produces dominant mode characteristics - decay rates, frequencies
This study evaluates the operational impact of multiple concurrent spatialized auditory cues during high-workload rotorcraft missions. A controlled, within-subject flight simulation experiment was conducted in which military-qualified rotorcraft pilots completed continuous multi-objective missions including formation flying, visual asset detection, collision avoidance, and emergency landing tasks. Each mission was flown under spatialized (3D) and non-spatialized (2D) audio rendering conditions while cue composition remained constant. Preliminary results indicate that under complex, formation-dominant workload conditions, pilots consistently prioritized visually anchored tasks and largely deprioritized auditory cue information regardless of spatial rendering. Collision avoidance cues did not produce observable evasive responses, and reported cue trust remained low without prior training. Although limited performance improvements were observed in isolated conditions, participants
This study investigates the post-failure flight dynamics of a 1200 lb classical octocopter under single motor inoperative condition using nonlinear time-domain simulations with a baseline feedback controller. A physics based propulsion sizing strategy is developed using IEC duty cycle definitions where continuous requirements are derived from nominal hover with margin and short time capability is used to accommodate elevated post failure loads. The selected motor satisfies both regimes and enables transient overdrive without excessive weight penalty. Simulation results in hover and forward flight at the best range speed showing that the vehicle can recover from any single motor failure and retrim using inherent redundancy without fault identification. However, recovery involves significant transient attitude excursions and altitude loss, and requires substantial increases in motor power, with multiple motors exceeding S1 power limits. Post-failure maneuver simulations indicate retained
The FAA VR-HeliSTART (Virtual Reality-Helicopter Simulator Training for Airplane to Rotorcraft Transition) is a 15-week study conducted at Marshall University (WV) to determine the effectiveness of an H125 VR reduced-motion platform simulator in training fixed-wing pilots to fly helicopters. Eleven students received three four-week blocks of instruction in the flight simulator, each followed by a simulator evaluation and a helicopter evaluation. This paper presents results for eleven hovering maneuvers trained and evaluated in the study. The evaluation of the students relied on both an objective and a subjective evaluation: a flight parameter analysis against Airman Certification Standards criteria, and an assessment by certified flight instructors. A key finding is that simulator training enabled all pilots to perform most hover maneuvers on their first helicopter flight without intervention, although sometimes below standards. Overall, results also suggest that while the simulator
This paper investigates the use of full-body vibrotactile cueing to augment operator perception during swarm teleoperation tasks. Piloted simulations are conducted in a virtual reality (VR) flight simulation environment using a quadcopter swarm model and a nonlinear dynamic inversion (NDI) flight control architecture. A scaled version of the ADS-33 slalom Mission Task Element (MTE) is implemented to evaluate swarm formation maintenance and obstacle avoidance under four experimental conditions: Good Visual Environment (GVE), Degraded Visual Environment (DVE), and each of these conditions augmented with haptic feedback. Haptic cues are delivered through vibrotactile vests and sleeves to convey information on formation deformation and gate proximity. Experimental results involving human participants indicate that haptic feedback improves formation maintenance and increases operators’ situational awareness of follower drone positions without increasing perceived mental workload. While
Electrified powertrains—such as Power Splits, Series Hybrids, and EVs with Disconnect Actuators—enable flexible management of actuator acceleration and torque from shared power sources. In power-limited or high-demand conditions, the Hybrid Supervisor must balance available power to sustain performance and drivability; poor coordination can cause control imbalance, reduced actuator performance, and unintended motion. Conventional methods often favor a single control objective, compromising overall system efficiency. This paper introduces FLAIR (Fuzzy Learning Adaptive Integral Response) Control, a supervisory strategy for actuator speed profiling and driver demand tracking in single-input multi-output (SIMO) systems. FLAIR integrates an integral of tracking error with fuzzy inferencing to dynamically weigh multiple control goals, adapting acceleration limits in real time while preserving driver power demand tracking. It enables bi-directional power-flow decisions—allocating system
Foam material models for automotive structural analysis typically require tensile and compressive data at multiple strain rates. The testing is costly and may require a long time to complete. For many applications, foams of similar chemistry are used and the foam structural responses, such as stiffness and compression force deflection, are controlled by the foam density. In such cases, Machine Learning (ML) lends itself as an ideal tool to detect the trends in material response based on density and strain rate. In this paper, two sets of polyurethane (PU) foams of different densities were tested at four strain rates ranging from 0.01/s to 100/s. ML models capable of predicting compressive stress-strain response for a range of densities were developed. The models demonstrated good prediction capability for intermediate strain rates at all foam densities and in extrapolating stress-strain curves at higher densities at all strain rates. The strain rate trends for density outside of the
Items per page:
50
1 – 50 of 6487