Browse Topic: People and personalities
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
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
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.
Researchers discover texts, phone calls, military communication, internal corporate networks all easily eavesdropped on using off-the-shelf equipment. University of California San Diego, La Jolla, CA With $800 of off-the-shelf equipment and months' worth of patience, a team of U.S. computer scientists set out to find out how well geostationary satellite communications are encrypted. And what they found was shocking. Close to half of the communications beamed from satellites to the ground that the researchers were able to listen in on were not encrypted. This included sensitive data including cellular text messages, voice calls, as well as sensitive military information, data from internal corporate and bank networks, and the in-flight online activity of airline passengers.
Sustainability needs to be practical. That was a point Peter Voorhoeve, president of Volvo Trucks North America, made clear at CONEXPO 2026 in Las Vegas. “We're running a business, so we are focusing a lot on efficiency and uptime,” he said, referencing the up-to-10% improvement in fuel efficiency with the new VNL. “That helps our customers to run their operations at a better pace and a lower cost, but at the same time we have a very positive impact on the climate.” Voorhoeve also teased the launch of a new vocational truck. “We are strong in long haul. We are a leading sleeper manufacturer, very strong in regional haul, and we now have renewed focus on vocational,” he said. “In August we will launch a new truck specifically for the vocational segment that's built on the same platform as the VNL and VNR.” (See page 22 for our feature story on the new VNR.)
Moog Inc. introduced its new adaptive electrification management system (AEMS) at a press conference during CONEXPO 2026 in Las Vegas. Moog states that this system offers a path to electrify, automate and digitalize construction machinery more efficiently and cost-effectively. “End users in the off-highway market are demanding that their machines have higher productivity and a lower total cost of ownership,” said Dr. Nate Keller, Moog strategic business manager. “OEMs are working to solve this problem, and one of the particular ways is through electrification.”
We hear it often at industry events, in keynote speeches and during expert panel discussions: There is no silver bullet. Peter Voorhoeve, president of Volvo Trucks North America, says as much in this issue's Q&A (page 44). “Electric is one solution, but biodiesel is another solution, and hydrogen is, too. So we have these different fuel solutions to get to better sustainability.”
Researchers from CompPair and the European Space Agency have developed a new composite material for spacecraft with an embedded healing agent. European Space Agency, Paris, France Healable spacecraft structures could soon be possible thanks to cutting-edge composite technology. Swiss companies CompPair and CSEM, and Belgian company Com&Sens have partnered with the European Space Agency (ESA) to modify their self-healing carbon fiber product for use in space transportation. Project Cassandra - an abbreviation for Composite Autonomous Sensing and Repair - includes sensors and a heating element within a composite carbon-fiber material, allowing spacecraft to autonomously repair initial stages of damage.
Researchers at Cornell University, working with collaborators, have created an extremely small neural implant that can sit on a grain of salt. Despite its size, the device can wirelessly transmit brain activity data from a living animal for more than a year.
Healable spacecraft structures could soon be possible thanks to cutting-edge composite technology. Swiss companies CompPair and CSEM, and Belgian company Com&Sens have partnered with the European Space Agency (ESA) to modify their self-healing carbon fiber product for use in space transportation.
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
Emergency evacuation slides (EVAC slides) are critical safety devices used on aircraft to enable rapid egress during emergencies. While these slides provide a quick and reliable escape route, communication between separated slides during evacuation remains a challenge. Often, during raft deployment over water, slides may drift apart impeding communication among evacuees and rescue personnel potentially compromising safety. Existing aircraft EVAC systems lack integrated wireless communication relying on visual or voice signals that are unreliable in chaotic conditions. This paper explores the integration of wireless IoT technology into EVAC slide systems to facilitate inter-slide communication and monitor critical parameters such as slide air pressure and the floating weight of stranded passengers through embedded sensors. It proposes the adoption of Long Range (LoRa) modulation technology for wireless communication chosen for its low-power, long-range performance and license-free
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.
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 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
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
In response to the 42nd (2025) Annual VFS Student Design Competition, the Graduate Student Design Team from the University of Maryland introduces Wyvern, a novel hydrogen-powered electric compound rotor-craft engineered for maximum loiter and operational safety. Named after a mythical dragon that defies convention by not breathing fire, Wyvern only breathes water vapor by forgoing hydrocarbon combustion in favor of the quiet and clean power of hydrogen. This design reflects not only an aeronautical solution to an engineering challenge but a greater aspiration to reshaping how practical and clean vertical flight can be achieved.
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