Browse Topic: People and personalities
KPIT experts address challenges of maintaining legacy architectures while introducing centralized compute, OTA, new energy platforms and AI layers - driving integration complexity and validation effort. KPIT Technologies is providing the executive leadership for this year's SAE COMVEC, a forum for global leaders in trucking, construction equipment, agricultural machinery and defense vehicles to address the technologies, regulations and innovations impacting transportation today and in the coming years. The theme for COMVEC 2026 (www.sae.org/events/comvec), which takes place in Schaumburg, Illinois, from September 29 to October 1, is “Resolving Current Challenges While Reimagining the Future.” “For commercial and off-highway, this theme captures a structural contradiction the industry lives with every day: transform the entire product architecture while continuing to deliver near-zero downtime, tight margins and proven reliability,” Satish Kumar, senior VP at KPIT, said in a pre-event interview with Truck & Off-Highway Engineering.
It's near that time of year again when early-career and well-established engineers alike gather in Schaumburg, Illinois, for the annual SAE COMVEC conference (www.sae.org/events/comvec). From September 29 to October 1, attendees will listen to experts on a range of topics - from “Right Sizing Hybrid Powertrains” to “AI for Efficient Engineering Development” - walk the exhibit floor to witness the latest technologies and of course converse with one another over coffee or other beverages. KPIT Technologies is providing the executive leadership for this year's COMVEC and has a significant presence on the agenda, including the opening keynote by Omkar Panse, KPIT's chief technology officer (CTO). Panse and Satish Kumar, senior VP at KPIT, offered their insights on the engineering challenges and opportunities that next-generation commercial vehicles present in this issue's feature story on page 16.
At the ACT Expo in Las Vegas, Rivian CEO RJ Scaringe said that the coming R2 pickup would help pay for the company's massive R&D budget. He said that budget was the result of a conscious decision to design and build the majority of items for its trucks and SUVs in-house. Scaringe joined Erik Neandross, president of TRC's Clean Transportation Solutions Group, for a fireside chat reflecting on Rivian's journey and its future.
Mehdi Ferhan said optimizing existing technology was a challenge but ultimately the right solution for Volvo to satisfy new NOx regulations. And he says he has never been bored a single day in his career as an engineer. SAE Media senior editor Chris Clonts had an exclusive opportunity at ACT 2026 to sit down with Volvo Group's senior vice president of powertrain technology, Mehdi Ferhan. The talk of the day was EPA 2027 and its new NOx emission standard. Other topics in the free-ranging conversation included BEVs, hydrogen and current opportunities for engineers. The following was edited for length and clarity.
New technologies, advanced materials, evolving mission profiles and fast-changing requirements are forcing the aerospace and defense (A&D) industry to dramatically increase the speed of engineering. Companies must design, validate and bring more complex products to market faster than ever, even as software, electronics and autonomy continue to reshape what aircraft, spacecraft and defense systems can do. At the same time, a growing production challenge is emerging. Workforce shortages, supply chain disruption and pressure to reduce cost and cycle time are converging with new demands for greater volume and flexibility. Defense programs are seeing increasing need for larger quantities of lower-cost systems such as drones, while commercial aerospace companies continue to work through backlogs and reinforce their fleets. To keep pace, the industry must accelerate innovation while also scaling production with greater speed, resilience and adaptability.
3D printing could change how we build parts for jet engines and power plants, but the process leaves microscopic holes that cause the materials to shatter. Published in International Journal of Extreme Manufacturing, Professor Fangyong Niu’s team in Dalian University of Technology have fixed the problem by doing something unconventional: They added a microwave.
Innovators at NASA Johnson Space Center, in collaboration with innovators at American Oxygen, have developed a solid-state system and process that separates oxygen from ambient air and compresses the resulting purified oxygen — with a significant reduction in power consumption compared to prior state-of-the-art. It is based upon a proven solid oxide electrochemical oxygen separation and compression technique that derives purified oxygen from ambient air and compresses it using an electrochemical pumping method.
A team of researchers at the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart developed a biohybrid micro swimmer covered with magnetic material, whose swimming ability is largely unaffected by the coating. The team from the Physical Intelligence Department at MPI-IS published their work in the journal Matter, which covers a wide range of materials science research.
A research team led by Professor Lin Gui at the Institute of Physics and Chemistry, Chinese Academy of Sciences, reports the first fabrication of multi-layer flexible batteries using a combination of liquid metal microfluidic perfusion and plasma-based reversible bonding techniques.
The inconsistency in bearing data distributions under diverse conditions often affects the representations of the faulty data and leads to indistinct decision boundaries and even negative transfer resulted from overlapping class distributions, greatly limiting the accuracy of the diagnosis model. To cope with the challenge, a pseudo-label-guided dual-supervised alignment (PDSA) method is developed for bearing fault diagnosis across diverse operating scenarios in this paper. To address the fixed alignment strategy issue, an adaptive distribution alignment layer is incorporated to ResNet18 to achieve dynamic data distribution alignment under varying condition, To enhance classification performances, a dual-supervised mechanism, comprising shallow-layer supervised contrastive learning is introduced through target domain pseudo-labels in target domain and deep-layer regularization class consistency. Experiments on two publicly available bearing datasets demonstrated this model realizes refined class-level alignment, strengthens fault states representation, and shows notable superiority in both accuracy and robustness.
Regarding the external sling load system of heavy-lift helicopters, the influence of the law of lifting point position on flight control stability characteristics has not been distinctly explained. To address this challenge, this paper constructs a sling load flight simulation model based on multi-body dynamics. Overall, the proposed model consists of four parts, including the rotor aeroelastic coupling model, the fuselage rigid body dynamics model, the flexible sling model, and the slung object rigid body model. Furthermore, through the hub six-degree-of-freedom rigid model and the flexible sling model, this paper realizes the dynamic coupling between the components. On this basis, taking the CH-53E heavy-lift helicopter as the research object, this paper utilizes real flight test data to validate the multi-body dynamic model. Subsequently, this paper systematically analyzes the influence of different lifting points’ lateral position, sling load mode, load-mass ratio, and forward flying speed on helicopter control stability characteristics. Simulation results indicate that the lifting point location exerts a significant impact on the helicopter’s trim attitude angles and dynamic stability. Of them, the lifting point location of the front center of gravity is the optimal in terms of trim characteristics and eigenvalue distribution. Furthermore, within a certain flight speed range, the lifting point of the front center of gravity demonstrates superior speed adaptability and system robustness. Apart from providing a solid theoretical basis for the lifting point layout design of the external sling load system of heavy-lift helicopters, the research results have important engineering application value for improving the safety of sling load flight of heavy-lift helicopters.
This paper takes a 3D Printer proposed by the project team in the early stage as the research object, constructs a digital twin entity including 3D models and data models in order to develop a digital twin interactive software. By activating the real-time correlation between 3D models and data models, valuable data exchange can be achieved between the digital twin entity and the physical entity, and valuable data can be used to drive both to refresh their operating status.
When quadrotor unmanned aerial vehicles (UAVs) operate in urban low-altitude airspace, especially within complex environments, their sensor perception signals are highly susceptible to blockages, deviations, and the inclusion of high-frequency noise. These factors, in turn, induce nonlinear variations in the UAVs’ flight mechanical properties, giving rise to abnormal flight stability issues such as attitude jitter, altitude fluctuations, and trajectory deviations. To address these challenges, this paper puts forward a method aimed at enhancing the positional accuracy of quadrotor UAVs, which is based on Extended Kalman Filter (EKF) multi-sensor fusion. In conjunction with the redundant configuration of sensors, a proportional-integral controller is specifically designed to allow optical flow sensors to compensate for the speed data generated by inertial sensors. Building on the EKF method, a comprehensive data fusion model is established, encompassing both position and speed states. Leveraging the MATLAB platform, trajectory flight simulations are conducted, utilizing multi-sensor data fused via EKF, with the sensor suite including GPS, IMU, Optical Flow sensors, and Barometers. The simulation results demonstrate that this proposed method can effectively mitigate the adverse impacts of environmental interference and sensor noise on the positional accuracy of quadrotors. By continuously correcting position information and accurately estimating position states, it significantly improves the UAVs’ flight position accuracy. This research outcome lays a robust and theoretically sound foundation for in-depth investigations on critical issues related to general aviation applications, such as the safe and efficient autonomous flight, adaptive and reliable intelligent navigation, and ultra-precise and mission-critical operations of quadrotor UAVs, thereby significantly contributing to the sustained and innovative advancement of the field.
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.
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.
Labor shortages and supply chain volatility are putting additional pressure on warehouse operations to be faster and more adaptable. “In this environment, real-time visibility becomes foundational. Physical AI enables warehouses to operate with a continuously updated understanding of their environment, allowing them to respond quickly to disruptions and optimize performance,” Joseph Mirabile, Vice President of Operations at Gather AI, a Pittsburgh-based startup developing drone-powered inventory solutions.
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 evaluated, including inverse FRF filtering, energy-based matching, and manual frequency-domain equalisation. The results indicate that single-input single-output (SISO) compensation approaches can achieve reasonable performance under independent single-axis excitation when compared to alternative methods. The study presents a comparative evaluation of these methodologies and highlights their respective challenges and limitations.
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.
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.”
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.
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 operation in emergency evacuation scenarios. In addition, the usage of this proposed technology can be further extended to locate the aircraft when other existing locating mechanisms fail.
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