Browse Topic: Simulators
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
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
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
This paper presents an initial handling qualities analysis of an Electric Vertical Take-Off and Landing (eVTOL) hexacopter. The analysis uses the Distributed Electric Propulsion Simulation (DEPSim), developed by Penn State University (PSU) and the Comprehensive Hierarchical Aeromechanics Rotorcraft Model (CHARM), developed by Continuum Dynamics, Inc. (CDI). The study focuses on evaluating a generic AAM hexacopter performing Handling Qualities Task Elements (HQTE) as defined by the DOT / FAA. A trajectory controller was developed to enable simulation of prescribed flight paths, allowing automated simulation of four HQTEs: Heliport Approach, Hovering Turn and Hold, Pirouette, Lateral Reposition and Hold. Design modifications incorporating lateral mast tilt and Direct Side Force Control (DSFC) were implemented to enhance yaw control and ride qualities. Piloted simulations were conducted at the PSU rotorcraft flight simulation facility using DEPSim, employing an Attitude Command Attitude
The automotive industry is rapidly evolving with technologies such as vehicle electrification, autonomous driving, Advanced Driver Assistance Systems (ADAS), and active suspension systems. Testing and validating these technologies under India’s diverse and complex road conditions is a major challenge. Physical testing alone is often impractical due to variability in road surfaces, traffic patterns, and environmental conditions, as well as safety constraints. Virtual testing using high-fidelity digital twins of road corridors offers an effective solution for replicating real-world conditions in a controlled environment. This paper highlights the representation of Indian road corridors as digital twins in ASAM OpenDRIVE and OpenCRG formats, emphasizing the critical elements required for realistic simulation of vehicle, tire, and ADAS performance. The digital twin incorporates detailed 3D road profiles (X-Y-Z coordinates), capturing the geometry and surface variations of Indian roads. The
Driver-in-the-Loop (DIL) simulators have become crucial tools across automotive, aerospace, and maritime industries in enabling the evaluation of design concepts, testing of critical scenarios and provision of effective training in virtual environments. With the diverse applications of DIL simulators highlighting their significance in vehicle dynamics assessment, Advanced Driver Assistance Systems (ADAS) and autonomous vehicle development, testing of complex control systems is crucial for vehicle safety. By examining the current landscape of DIL simulator use cases, this paper critically focuses on Virtual Validation of ADAS algorithms by testing of repeatable scenarios and effect on driver response time through virtual stimuli of acoustic and optical warnings generated during simulation. To receive appropriate feedback from the driver, industrial grade actuators were integrated with a real-time controller, a high-performance workstation and simulation software called Virtual Test
Functional Mock-up Units (FMUs) have become a standard for enabling co-simulation and model exchange in vehicle development. However, traditional FMUs derived from physics-based models can be computationally intensive, especially in scenarios requiring real-time performance. This paper presents a Python-based approach for developing a Neural Network (NN) based FMU using deep learning techniques, aimed at accelerating vehicle simulation while ensuring high fidelity. The neural network was trained on vehicle simulation data and trained using Python frameworks such as TensorFlow. The trained model was then exported into FMU, enabling seamless integration with FMI-compliant platforms. The NN FMU replicates the thermal behavior of a vehicle with high accuracy while offering a significant reduction in computational load. Benchmark comparisons with a physical thermal model demonstrate that the proposed solution provides both efficiency and reliability across various driving conditions. The
The article is devoted to a comprehensive analysis of the digital transformation of education using the example of a project to train engineering personnel for the innovative transport industry in Russia. Special attention is paid to the introduction of hybrid formats, digital platforms, inclusivity, issues of digital inequality, as well as the experience of the National Research Center of the Russian Federation FSUE NAMI and interaction with leading universities in the country. A comparative analysis with foreign initiatives, including modern AI solutions for inclusive education, is presented, as well as the impact of the project to create educational and methodological centers on the professional motivation of teachers.
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