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Model-Based Flight Test Methodology Research for Transport Category Aircraft Stall2026-99-2340To be published on 10/30/2026
Model-based approaches have brought about a qualitative leap in enhancing flight test safety, improving the efficiency of concurrent engineering, and enabling knowledge reuse and sharing. Focusing closely on the engineering application of model-based methods in the field of stall flight testing, this paper systematically reviews the stall airworthiness certification requirements for transport-category aircraft. It proposes a stall flight test process model based on functional flow models and investigates a model-based design method for stall flight test point matrices. Furthermore, an improved practical approach for multi-disciplinary collaborative modeling, simulation, and compliance data analysis in stall flight testing is presented. Taking a specific aircraft type's stall flight test as an example, this paper provides a detailed description of model-based application cases across the pre-flight, in-flight, and post-flight phases.
Liu, Xu, Ding, Junliang
An Integrated Fault Diagnosis and Prediction Method for Planetary Gears Based on Ensemble Learning2026-99-294410/19/2026
Planetary gears are key transmission components in spacecraft drive systems and are prone to failures under long-term operation in harsh environments. To address the problem that fault diagnosis and life prediction are treated separately in existing research, this paper proposes an integrated diagnosis-prediction method. First, order tracking and time synchronous averaging are used to preprocess vibration signals, eliminating speed fluctuations and noise interference. Then, fault features are extracted via VMD/EEMD decomposition, and the gear health condition is identified through a deep convolutional neural network. Finally, ensemble learning prediction models are established separately for different fault states to realize life prediction. Experimental results show that the VMD+CNN method achieves higher diagnostic accuracy than traditional methods for gears in healthy, cracked, spalled, and pitted conditions. The ensemble prediction models matched with different states can further reduce the life prediction error. This method provides effective technical support for condition-based maintenance of spacecraft planetary gears.
Liu, Yanyan, Gao, Tongxin, Zhu, Yangsong
Research on Network Attack and Defense Technology in LEO Large-Scale Constellation Networking2026-99-298710/19/2026
With the rapid development of LEO large-scale constellations, space networks evolved into a critical domain of cyber confrontation. The heterogeneous architecture, open inter- satellite links and limited on-board processing capability introduce serious security risks. Aiming at advanced persistent threat (APT), data poisoning and spear phishing, this paper analyzes new attack mechanisms in LEO constellation networking, satellite-ground collaboration and communication - computation convergence scenarios. A satellite-ground-link collaborative active defense model is proposed to improve the security and stability of LEO constellation systems.
Guan, Mengmeng, Xia, Ming, Fang, Liang, Li, Ruxue, Lv, Xing
Low-Cost Ground Station Network Siting for Geostationary Space-to-Ground Laser Communication2026-99-295110/19/2026
The deployment of multi-link ground station networks for geostationary space-to-ground laser communication is constrained not only by local weather conditions but also by infrastructure accessibility and engineering cost. Existing ground station siting studies have mainly emphasized cloud-related meteorological indicators and site diversity, while giving insufficient attention to deployment-oriented cost factors. As a result, meteorologically favorable solutions may still be expensive to implement in practice. To address this issue, this paper investigates the low-cost siting problem of a ground station network under a target network availability constraint and proposes a two-stage method for a multi-link, single-telescope scenario. Under the adopted assumption, each ground station can provide at most one available link at any given time, so the number of available network links is equal to the number of clear-sky available stations. The network availability is therefore defined as the probability that at least a required number of links are simultaneously available. Using half-hourly cloud mask data from the Himawari 8 geostationary meteorological satellite for the year 2023, site-neighborhood cloud metrics and inter-site weather correlation metrics are constructed. A data-center clustering and distance-decay model is then introduced to build an infrastructure impact cost field. On this basis, nationwide optimization is carried out through candidate construction and final subset selection. A baseline method without data-center factors is also evaluated under the same total-cost definition. The results show that, under the same target network availability requirement, the proposed method can effectively reduce total deployment cost. This indicates that incorporating data-center factors during siting improves the engineering tradeoff of multi-link geostationary laser communication ground station networks and provides a reproducible route for low-cost planning under service availability constraints.
Zhang, Chi, Zhao, Juntong, Li, Wenfeng, Zhao, Kanglian
A Dynamic Primary/Backup Path Algorithm Based on Multi-Level Feedback Queue for Differentiated Services in LEO Satellite Networks2026-99-297710/19/2026
With the accelerated deployment of Low Earth Orbit (LEO) mega-constellations, such as Starlink and OneWeb, satellite networks are transitioning from providing best-effort connectivity to supporting stringent, mission-critical services, including autonomous driving coordination, ultra-high-definition video streaming, and real-time financial transactions. This paradigm shift imposes highly heterogeneous Quality of Service (QoS) requirements regarding latency, bandwidth, and reliability. However, traditional unified scheduling and static routing mechanisms are inadequate for handling the dynamic topologies and bursty traffic inherent in LEO environments, often leading to service starvation or excessive packet loss. To address these challenges, this paper proposes an integrated primary-backup path guarantee framework that tightly couples a Multi-Level Feedback Queue (MLFQ)-based dynamic scheduling algorithm with a Dynamic Weight Adaptive Routing Algorithm (DWARA). The MLFQ scheduler employs an aging-based priority promotion mechanism to dynamically adjust time slices, effectively preventing low-priority traffic starvation while ensuring ultra-low latency for Class A services. Concurrently, DWARA models QoS as a three-dimensional vector (Throughput, Delay, Reliability) and utilizes the eigenvector method to derive service-aware weights for path selection. To validate the efficacy of the proposed scheme, comprehensive simulations were conducted using STK and Python, emulating a 64-node polar constellation under varying fault conditions. The results demonstrate that the integrated framework significantly outperforms baseline algorithms. Specifically, it reduces the average blocking rate by up to 37% under scenarios with 10 faulty satellites compared to Dijkstra, while lowering end-to-end latency for delay-sensitive services to approximately 28 ms at a traffic arrival rate of 500. Furthermore, the framework achieves a 92% bandwidth satisfaction rate for high-throughput services and maintains near-zero packet loss for high-reliability transmissions, offering a scalable and robust QoS assurance solution for next-generation space-air-ground integrated networks.
Zhang, Hongyuan, Zhang, Pengfei, Zhou, Qihao, Gao, Zhao
Robust Cross-Modal UAV Detection via Bidirectional Collaborative Energy Field under Weak Calibration2026-99-299210/19/2026
The precise detection and localization of 'low-slow-small' (LSS) unmanned aerial vehicles (UAVs) pose significant challenges in terminal defense scenarios. These non-cooperative targets have extremely sparse representations in radar point clouds because of their minimal radar cross-sections and appear as textureless, blurred patches in visual images. Furthermore, traditional multi-modal fusion paradigms rely heavily on rigid geometric projection matrices, which frequently fail under the mechanical vibrations, thermal deformations, and extrinsic perturbations inherent in rapid deployment environments, which often results in severe feature misalignment and contamination. To overcome these critical bottlenecks, we present an end-to-end cross-modal detection framework built on Bidirectional Collaborative Energy Fields (Co-Field). By redefining the 3D object detection task as an energy field reconstruction process within a latent multimodal feature space, we entirely remove the need for absolutely precise extrinsic parameters. Our framework uses a novel bidirectional mutual-guided interaction mechanism. Specifically, a vision-guided radar branch employs deformable cross-attention to adaptively aggregate highly discrete radar geometric features around visual semantic centers, achieving a feature-level soft alignment that implicitly compensates for calibration drift. Concurrently, a radar-corrected vision branch utilizes radar-derived geometric occupancy probabilities as depth anchors to gate and calibrate visual features, effectively suppressing the inherent divergence of monocular depth estimation. Ultimately, the framework regresses the target's precise 3D states and fine-grained categories directly from the extrema of the reconstructed collaborative energy field, circumventing complex heuristic post-processing. Experiments on the curated MMAUD dataset show that the proposed Co-Field method achieves better robustness. It remains highly effective even under severe calibration errors up to 10 degrees and achieves a 74.2% recall rate at extreme sparse distances ranging from 70 to 100 meters, and consistently outperforms state-of-the-art rigid and soft fusion baselines.
Peng, Chao, Shao, Peng, Ding, Qiao, Chen, Xiaoqing, Han, Aojia, Xu, Yuelei
Collaborative Design of Low Earth Orbit Multi-Layer Constellations for Coverage and Networking Performance2026-99-290710/19/2026
With the growth of global communication demand, multi-layer Low Earth Orbit (LEO) satellite constellation has become an important part of the next generation network. The multi-layer hybrid constellation can effectively improve the coverage performance and network connectivity through the cooperative work of satellites with different orbital heights. In this paper, a dynamic model of constellation topology is established for multi-layer LEO constellations. The index system of coverage and networking is constructed by using the strategy of fixed neighbor inter-satellite link (ISL) in the same layer and the nearest neighbor dynamic inter-layer link (ILL) between layers. Through simulation, the performance of single-layer and multi-layer constellation configurations is compared. The influence of design parameters such as orbital altitude, number of ILLs and inter-layer satellite resource allocation on the indexes of multi-layer constellation is analyzed. The simulation results show that the multi-layer constellation is superior to the single-layer constellation in terms of coverage continuity and network capacity with a fixed total number of satellites. As the number of ILLs increases from 1 to 5, the average shortest path length (ASPL) and the average inter-satellite propagation delay decrease, and the network capacity is significantly improved. Reasonable inter-layer resource allocation and link configuration can effectively improve the networking performance of multi-layer constellations.
Wu, Kejie, Chen, Quan, Li, Linhua, Liu, Erjiang
2026 International Symposium on Aerospace Information and Industry Innovation2026-99-290010/19/2026
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Tobolski, Sue
Global Task Graph-Based Multi-Agent Offloading for Dependency-Aware Satellite Edge Computing2026-99-296310/19/2026
Satellite edge computing (SEC) enables low-latency services by leveraging Low Earth Orbit (LEO) satellites. However, emerging applications increasingly generate dependency-aware tasks with complex precedence constraints, leading to challenging multi-DAG (directed acyclic graph) offloading under dynamic satellite environments. To address this issue, we propose a global task graph (GTG)-based multi-agent offloading frame-work named GTG-MARL, for dependency-aware task execution in regional satellite-ground collaborative networks. Specifically, we construct a GTG by merging multiple DAGs to capture both intra-task dependencies and inter-task coupling, thereby achieving unified dependency-aware modeling. Then, a graph neural network (GNN) is lev-eraged to encode the GTG into dependency-aware subtask embeddings. To handle dependency-aware offloading in dy-namic environments, we develop a multi-agent reinforce-ment learning framework based on generalized proximal policy optimization, where each LEO satellite acts as an agent for decision-making while a centralized critic stabilizes offline learning to eliminate real-time on-orbit communication overhead. During the execution phase, only the light-weight trained actors are deployed on satellites for on-orbit inference. Extensive experiments demonstrate that the pro-posed method significantly reduces task completion delay compared to state-of-the-art baselines, validating the practi-cal efficiency and low synchronization cost of the proposed framework for regional SEC deployment.
Geng, Zeao, Zhao, Lu, Jin, Huiying, Jiang, Ping, Cai, Hui, Zhou, Jian
PPO-Based HAP Deployment for Throughput Maximization in SAGIN2026-99-292810/19/2026
Space-Air-Ground Integrated Networks (SAGINs) have attracted extensive attention as an effective solution for providing worldwide communication services and supporting diverse transmission requirements. Nevertheless, the highly dynamic wireless environment and fluctuating user distribution introduce considerable difficulties for efficient deployment of High-Altitude Platforms (HAPs). This work studies a throughput enhancement problem in a SAGIN architecture composed of a Low Earth Orbit (LEO) satellite, multiple HAPs, and terrestrial users. Considering practical communication constraints such as beam scheduling and subchannel assignment, the objective is to improve the overall data transmission capability through optimized HAP positioning. By interacting with varying network states, the proposed learning framework agent continuously learns suitable deployment policies for HAPs, thereby improving transmission performance and spectrum utilization efficiency. Numerical evaluations indicate that the policy optimization strategy achieves superior throughput performance and communication efficiency compared with traditional fixed deployment approaches.
Wang, Yin, Zhou, Keyan, Shao, Shuai, Zhang, Bin, Chen, Peifu, Wang, Jianfeng
Edge-Intelligent Real-Time Evaluation System for Aerospace Manufacturing Skill Training2026-99-292010/19/2026
The evaluation of practical skills in aerospace manufacturing, maintenance, repair, and overhaul (MRO) conventionally relies on examinations or observations from instructors. However, these methods deliver slow feedback, reduce the efficiency of skill acquisition and require substantial instructional workload. To overcome these challenges, this study proposes an edge-intelligent real-time evaluation system for skill training in the field of aerospace manufacturing. Specifically, the system comprises five sensing modalities, which are then processed through a hierarchical three-layer structure. Then, the neural architecture search (NAS) is introduced to identify the optimal network structure and the quantization-aware training (QAT) approach is employed to improve the inference speed. In addition, temporal modeling is used to realize real-time inference and multi-level evaluation. Moreover, a conflict-aware adaptive fusion algorithm is introduced to ensure robust scoring under harsh workshop conditions and deliver interpretable feedback. Finally, federated learning and the differential privacy mechanism are used for cross-institutional model improvement without raw data exposure. Empirical results on turbofan engine MRO assembly, airframe structural component CNC manufacturing, and aviation maintenance human factors training show that the system achieves low latency, high expert correlation, improved learning efficiency and strong robustness, which offers a scalable and privacy-aware solution for competency-based vocational training in the aerospace manufacturing field.
Li, Xin, Li, Zhaokang, Yu, Lina
Adaptive Slicing and High-Reliability Transmission Method for Spacecraft On-Orbit Data2026-99-292210/19/2026
Spacecraft On-orbit data are characterized by large volumes, stringent timeliness requirements, and heterogeneous data values, posing significant challenges to traditional satellite-ground transmission systems. To improve transmission efficiency and reliability under dynamic link conditions, this paper proposes an adaptive data slicing and collaborative reliability enhancement method. First, a joint evaluation model integrating Data Impact and Link Health is developed to evaluate data importance and transmission conditions. Based on the evaluation results, the slice size, priority allocation, and redundancy configuration are dynamically optimized. Second, a distributed collaborative verification mechanism combining Reed–Solomon erasure coding and multi-node arbitration is introduced to enhance data integrity and recovery capability. Furthermore, a mission-driven elastic scheduling strategy is designed to facilitate efficient resource allocation in multi-user transmission scenarios. Experiments were conducted on 100 telemetry files with a total volume of 1.9 GB in a simulated satellite-ground communication environment. Experimental results demonstrate that the proposed method improves average throughput by 25.3% and achieves a reconstruction success rate of 99.7% across 1,000 packet-loss reconstruction trials.
Li, Cheng, Liu, Peng, Chen, Xi, Cui, Yan, Li, Han, Li, Xian, Wang, Zhihui, Yi, Xiu
Satellite Autonomous Task Decision-Making Method Based on Attention Pointer Network2026-99-296510/19/2026
Large-scale satellite constellation on-orbit missions are characterized by multiple objectives, multiple constraints, and strong dynamism. Traditional ground control and management modes have become difficult to meet the requirements for autonomous real-time decision-making by satellites. This paper addresses the need for autonomous and rapid decision-making capabilities on-board satellites, and proposes a satellite autonomous task planning and decision-making method based on an attention pointer network, addressing issues such as limited on-orbit computing resources and insufficient generalization of planning methods. Firstly, by analyzing and mathematically describing the characteristics and constraints of satellite tasks, a Markov model for satellite autonomous task planning problems is constructed. Secondly, the pointer network is combined with a reinforcement learning framework, and an attention mechanism is introduced to perform probabilistic learning on the execution selection of satellite tasks, generating an optimized sequence of satellite task planning results. Finally, simulation experiments show that after training, this method exhibits certain generalization capabilities and significantly superior computational efficiency compared to traditional intelligent optimization algorithms such as genetic algorithms. It can adapt to the single-satellite autonomous dynamic planning requirements under large-scale constellation control and management, providing technical support for autonomous operation and online intelligent task planning of satellites in orbit.
Feng, Xiaoen, Li, Yuqing, Qiu, Jianbin, Li, Meicheng
2025–2026 Reviewers10-10-04-003810/1/2026
2025–2026 Reviewers
Xu, Peijun
Stuck-Assist: A Large Multimodal Model-Based Stuck Recovery Assist for Driving on Off-Road Deformable Terrains2026-01-75139/22/2026
Recent advancements in off-road autonomy have shown significant progress in perception, planning, and control frameworks, including end-to-end learning approaches. Comprehensive results have been demonstrated in both simulation and real-world experiments; however, there are significant challenges in critical cases that need further evaluation. One such challenge is the immobilization of autonomous ground vehicles (AGVs) in unstructured off-road environments, which can significantly impact agriculture, space exploration, military operations, and search and rescue missions. Addressing this problem requires recovery strategies that are context-sensitive, adaptable to terrain and vehicle conditions, and effective in integrating multimodal inputs. To this end, this paper investigates the use of a large multimodal model (LMM) providing higher-level planning assistance with human-in-the-loop evaluations for vehicle recovery after immobilization in unstructured off-road terrain. The experimental simulation platform developed was based on the Algoryx (AGX) Dynamics engine for high-fidelity terramechanics interaction and vehicle physics combined with Unreal Engine 5. This platform was further integrated with a driving simulator equipped with steering wheel and pedal interfaces for human-in-the-loop experiments. We evaluated ten representative unstuck scenarios across two deformable terrains (loose sand and compact sand) under two modes: an unskilled baseline, where participants attempted recovery unaided, and a co-intelligence mode, where participants used LMM advisory instructions. The results show that LMM assistance improved stuck recovery rates by 70% compared to unaided and unskilled human driving.
Bhosale, Mayuresh, Whitson, Jordan A., Vahidi, Ardalan, Jia, Yunyi
Optimizing Data Delivery for Ground Vehicle Teams2026-01-75399/22/2026
The radio-based wide area network (WAN) that forms the command-and-control backbone for deployments of multiple ground vehicles is a classic DIL (disconnected, intermittent, limited) communications infrastructure for bandwidth-intensive services like video streams, situational awareness feeds, and command-and-control messages. This paper describes an inter-vehicle network architecture that leverages the radio-aware routing features of an existing vehicle Ethernet switch/router to provide mission-optimized, fault-tolerant data delivery while minimizing both network overhead and vehicle SWaP requirements.
Al-Gharaibeh, Jafar, Bonney, Jordan
Human Digital Twin Architecture for Ground Vehicle Scenario Integration2026-01-75059/22/2026
Ground vehicle commanders operate in scenarios which bare high cognitive load. They must be reactive to time-critical events where attention is divided between a variety of sensors, crew members, the physical world, and digital displays, which can result in missed situational cues. This paper presents a Human Digital Twin (HDT) architecture which provides real-time, embodied AI assistance to commanders in a military ground vehicle simulation scenario. The system integrates a data pipeline for combining a MetaHuman avatar in Unreal Engine with multi-modal data ingestion and a large language model (LLM). In addition, a retrieval-augmented generation approach grounds the LLM with mission-specific context, and a Big Five personality framework for prompt design constructs a consistent agent persona throughout the scenario. The architecture is demonstrated with a prisoner of war camp scouting mission, in which the HDT selectively intervenes when needed to alert the commander to critical events when missed. A system latency evaluation is provided to demonstrate viability for real-time integration. Results show the potential of integrated HDT systems to improve situational awareness and decision support in high stakes ground vehicle operations.
McCarthy, Martin, Mohammed, Abdul Mannan, Gallagher, Reese, Neumann, Carsten, Bruder, Gerd, Reiners, Dirk, Cruz-Neira, Carolina, Paul, Victor
The Impact of Observability Constraints on the Planning for Employment of Robotic and Autonomous Systems2026-01-75149/22/2026
Employment of Robotic and Autonomous Systems requires a different paradigm of mission planning. The GTRI Missioneer for Organic Collaborative Kill-Chains (MOCKS) effort was developed to mature the concepts of mission planning in light of autonomy, where the decision making is distributed across the battlespace and each individual entity has limited awareness of global state. The paper presents some initial characterizations of the impact of constraints on periods out of communication, or no-comms windows, on the number of resources required for missions of a certain operational area. This metric, along with the foundational metric of remaining effective range margin are foundational to robust a priori planning.
Spratley, Michael
Multimodal Digital-Twin Simulations for Off-Road Autonomous Vehicles2026-01-75509/22/2026
Research in autonomous driving has largely focused on structured, on-road environments in densely populated urban areas, leaving off-road autonomy comparatively underexplored despite its importance for applications such as search and rescue, agriculture, and defense. Progress in developing solutions for off-road autonomy is heavily constrained by the high cost, safety risks, and limited coverage of real-world off-road data collection, particularly for rare terrain-induced failure cases. To address these challenges, we introduce a large-scale simulation platform for off-road autonomous driving generated in the Unity3D engine. Our simulator provides indefinite data from multiple RGB cameras, LiDAR, GPS, and IMU sensors to support research in long-duration applications. Our platform also facilitates research in risk-aware planning through dynamic weather, lighting, and annotations for sequence-level failures like collisions and weather-induced sensor blockages. By combining long-duration temporal coverage, multimodal sensing, and procedurally generated terrain diversity, our simulator facilitates progress for systematic evaluation of learning-based autonomous driving models in unstructured, safety-critical environments.
Ross, Timothy, Boone, Julia, Afghah, Fatemeh
Comparative Evaluation of Cad Systems as Engineering Design Architectures2026-01-75469/22/2026
There is a need for advanced collaboration tools to meet the needs of increasingly complex engineering design challenges and of global design teams. Specifically, virtual design reviews are a means by which teams can obtain a better understanding of a product’s design. Many CAD systems have integrated cloud features, which can be utilized to perform design reviews within these systems. The focus of this paper is to evaluate CAD systems in three CAD architectures: traditional, cloud-enabled, and cloud-native, to assess their capabilities and highlight areas of improvement for CAD developers. Using a list of previously generated requirements, CAD systems were evaluated on how well they met each requirement to determine how they can foster collaborative work. While cloud-native CAD systems performed the best against the requirements, no CAD system was able to fully meet all requirements, showcasing multiple areas for future development.
Hughes, Katelynn, Petty, Emily, Mocko, Gregory M.
Analyzing Open-Source Data to Inform Military Vehicle Design2026-01-75599/22/2026
The modern battlefield is increasingly transparent, generating large volumes of open-source data on the use, damage, and loss of military vehicles. This paper presents a structured methodology to exploit such data for deriving operational requirements for future vehicles. It uses a mixed-method framework combining qualitative reporting with quantitatively verified loss data. Daily battlefield reports are analyzed with large language models to extract operational context, employment patterns, and tactical conditions. These insights are cross-referenced with loss data to assess how operational factors affect vehicle survivability, with the findings being used to prioritize requirements that improve vehicle performance. The approach is demonstrated through a case study of Leopard tanks in the Russia-Ukraine war, using Institute for the Study of War reports and Oryxspioenkop loss data. Results show how open-source intelligence can systematically inform survivability, mobility, and combat effectiveness in modern vehicle design.
Lynch, Benjamin, Mittal, Vikram
Motion Approach for Turret System Physical Simulation2026-01-75539/22/2026
Physical simulation permits government and contractor engineers to characterize, validate and test a complex weapon system’s many components and subsystems. This is particularly important when new sub-systems and components are in the prototype development stage and integrated into a full weapon system for the first time. This paper presents a comprehensive methodology in creating a motion environment to adequately test a functional turret system in a lab environment using the GVSC’s Crew Station Turret Motion Base Simulator. The simulator is a high-capacity, 6-degrees-of-freedom test device that utilizes computer-controlled hydraulic actuators and a platform to reproduce dynamic conditions encountered by a combat vehicle turret system traversing off-road terrain. The paper presents an iterative technique using the simulator’s measured frequency response functions to achieve a targeted response. Platform error and repeatability are presented which is key for “baseline vs. modified” studies.
Paul, Victor, Hoelscher, Andrew, Tiguert, Ahmed, Zywiol, Harry
Simulation-Based Development and Evaluation of Heat-Quantity-Based Thermal Operating Strategies for Automotive Applications2026-24-00129/21/2026
Thermal management of hybrid electric vehicle (HEV) powertrains requires the simultaneous conditioning of multiple components operating at fundamentally different temperature levels. For thermal management systems, which directly couple the thermal circuits of the internal combustion engine (ICE), electric motor and inverter (EMINV), and traction battery (BAT) for example via controllable three-way valves and a ring-circuit, the decision of when and which components to couple has a direct impact on overall powertrain efficiency. Existing thermal operating strategies rely on empirically defined temperature thresholds and fixed component priority rankings, without quantifying the actual efficiency benefit associated with each coupling decision. This paper presents the development and simulation-based evaluation of a heat-quantity-based thermal operating strategy for a prototype HEV at TU Darmstadt. The strategy introduces three new computational modules — a Q-Indicator quantifying the thermal surplus or deficit of each component, an η-Indicator evaluating real-time component efficiencies as a function of temperature and operating point, and a Δη module computing the combined efficiency gain of each potential coupling pair prior to actuation. Coupling is executed only when the combined efficiency delta is positive, replacing empirical prioritization with a quantitative, efficiency-driven decision mechanism. The strategy is evaluated against an uncoupled baseline (REF-0) and a temperature-threshold-based predecessor strategy (REF-1) across a representative commuter cycle at ambient temperatures of −10 °C, 0 °C, and +30 °C using a co-simulation environment comprising a 1D ring-circuit fluid model in AVL Cruise M and a backward-facing 0D drivetrain model in MATLAB/Simulink. The results demonstrate measurable improvements in battery preconditioning and system efficiency at cold and moderate ambient temperatures. The heat-quantity-based strategy achieves comparable or superior thermal outcomes to the threshold-based approach while activating ring-circuit coupling more selectively. At warm ambient conditions, the strategy correctly withholds intervention based on a negative efficiency delta evaluation, confirming robust scenario-adaptive behavior. The findings highlight the potential of efficiency-driven coupling logic as a generalized and physically grounded basis for thermal operating strategy development in electrified powertrains.
Stenger, Erik, Fiore, Luis, Weimer, Niko, Beidl, Christian
Physics-Informed Machine Learning Digital Twin of Electro-Mechanical Brakes for Clamp-Force and Friction Estimation2026-01-08069/14/2026
Recently, there has been a drastic shift in the industry towards wire architectures like steer-by-wire and brake-by-wire. For safe and accurate force control, diagnostics, and consistent performance over the operating envelope, accurate plant modeling of the Electro-Mechanical Brake (EMB) is important. Classical approaches involved linearized dynamic EMB models and the use of the characteristic stiffness curve for calibration at the operating points. These methods often perform poorly over regions where hysteresis, compliance, and friction are strongly nonlinear. Prior research on state or force estimation for EMB has focused on pad contact detection, thermal adaptation, and hysteresis-aware clamp force estimation. However, there are still accuracy gaps in practical applications during transients and under shifting friction regimes. In this work, a digital twin based on Physics-Informed Machine Learning is introduced, following the governing dynamics of the actuator-caliper assembly of EMB while learning (i) a physically significant parameter—system damping (Bsys) and (ii) a non-linear friction term constrained as a function of the actuator motion states and operating conditions. Non-linear friction is captured through gray-box friction formulation and learning unmodeled residual dynamics such as hysteresis and backlash. An EMB test stand is used to collect steps, ramps, holds/engagements, APRBS, and swept-sine excitations, with signals including time-aligned force command, motor torque/current, actuator position/velocity, and pad force measurement from a force sensor for model training. Results demonstrate a decrease in pad-force prediction error, along with non-linear and residual friction estimation. The resulting digital twin can enable sensor-less force estimation, friction compensation design, predictive analytics, and health monitoring through tracking parameter drift and friction signatures.
Rai, Prakhar, Gadhvi, Tirth
Design and Analysis on a Traction-type Power-assisted Follow-up Vehicle for Emergency Rescue2026-99-15479/11/2026
To address problems in China’s emergency rescue scenarios—such as limited functionality, insufficient mobility, poor adaptability to complex terrain, the labor-intensive nature of manual carrying, and the lack of flexibility of fully automatic carts—a traction-type emergency rescue power-assisted follow-up vehicle was designed and developed. With the core design goals of “lightweight, high mobility, and human-machine collaboration”, this power-assisted follow-up vehicle has multiple advantages. At the structural level, it supports rapid folding and unfolding, enabling convenient operation and adaptation to transportation needs in various emergency rescue scenarios. In terms of material selection, it balances strength and lightweight properties, and its key components possess anti-cutting and flame-retardant capabilities, allowing adaptation to the harsh environment of emergency rescue. The power system adopts modular replaceable batteries and is equipped with a high-performance control unit, motor, and shock-absorbing suspension design. This enables normal operation in a variety of complex terrains. The control system is centered on human-machine collaboration. It features simple operation and automatic adjustment of operating status, effectively reducing the operational burden and physical exertion of rescuers. Meanwhile, it supports the master-slave expansion function, allowing flexible switching from a two-wheel structure to a four-wheel structure to meet diverse rescue needs such as material transportation and casualty transfer. This power-assisted follow-up vehicle can effectively solve the material transportation problem in the “last few kilometers” of emergency rescue.
Xu, Jiang, Hou, Yumeng, Yang, Han
2025 International Conference on Intelligent Equipment, Vehicle Engineering and Automation Control2026-99-11009/11/2026
TOC
Tobolski, Sue
3D Dynamic Object Detection with Multi-Modal Temporal Fusion2026-99-11179/11/2026
This research aims to address the critical challenge of accurately detecting and estimating the state of dynamic objects in autonomous driving. Traditional 3D object detection methods often struggle with motion perception, particularly in velocity estimation, due to the lack of information in single frame perception. We propose a novel framework that enhances the BEV representation with temporal modeling. The core of our method is a two-stage temporal fusion process. First, we align historical BEV features to the current coordinate frame to eliminate the interference of ego-motion. Subsequently, a dedicated temporal fusion encoder, architected with residual connections and a Feature Pyramid Network, refines the aligned multi-frame BEV features to capture complex motion patterns and improve multi-scale object representation. This approach directly tackles the problem of motion decoupling. By aligning features, we disentangle object motion from ego-motion. The temporal fusion encoder then mitigates the positional ambiguity of moving objects in the fused BEV space, a common issue in simple feature concatenation, leading to more robust detection. We built a dataset following the structure of the nuScenes dataset, using data collected from an autonomous driving simulation platform. The evaluation results on our simulation dataset demonstrate that the proposed temporal module achieves a 13.0% improvement in NDS score and a substantial 29.7% reduction in velocity error (mAVE). These results demonstrate that our temporal fusion strategy effectively enhances 3D detection accuracy in dynamic scenarios.
Shao, Mengjia, Li, Wei, Bai, Jie, Zhu, Shaoxiong, Xu, Chenjie
Effect Evaluation of Emergency Barrier-free Blocking Methods in Highway Tunnels Based on Visual/Driving Behavior2026-99-15319/11/2026
To address the lack of safe and effective on-site vehicle blocking and control methods in the event of fires or other emergencies in extra-long tunnels — which can significantly reduce traffic safety risks and prevent secondary accidents — this study proposes a novel barrier-free light–smoke curtain interception method. The method integrates conventional traffic safety warning facilities (gantry-mounted variable message signs and audio–visual alarms) with two light–smoke curtain interception images to form a composite early-warning and interception system. Driving simulation experiments were conducted to comprehensively evaluate its warning effectiveness, interception performance, and operational safety in comparison with methods employing only traditional warning facilities or light curtain images. Furthermore, field drills were performed to validate its real-world applicability and interception effectiveness under both daytime and nighttime conditions. The main findings are as follows: 1) The fixation ratio and interception success rate associated with the proposed method were significantly higher than those of the other two methods, demonstrating enhanced visual attention and superior warning and interception performance. 2) The maximum deceleration observed with the proposed method was lower than that of the light curtain–only method and did not trigger emergency braking, thereby indicating high operational stability and driver comfort. 3) In field drills, after activation of the interception equipment, only one and two vehicles entered the tunnel under daytime and nighttime conditions, respectively, and full control of on-site vehicles was achieved within two minutes without any traffic accidents, verifying the system’s rapid response and effective safety assurance.
Shi, Mingjun, Li, Shicao, Wang, Haohuan, He, Qifei, Che, Zhengzhang, Li, Yanbo
2025 International Conference on Aerospace and Electronic Information2026-99-14009/11/2026
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Tobolski, Sue
Linkages and Propagation Mechanisms of Risk Sources during Electric Vehicles Transportation2026-99-15289/11/2026
This study addresses safety issues in three representative logistics scenarios for electric vehicles (EVs) as cargo-car carriers, roll-on/roll-off (Ro-Ro) vessels, and containers. To address the heterogeneity across these modes, we develop an integrated “process–spatiotemporal load–risk factor” framework that embeds operational steps and confinement conditions into the indicator system, overcoming the limitations of single-scenario or single-factor studies in explaining chain-type propagation. Building on process mapping and spatiotemporal load characteristics, we develop a risk indicator system spanning “person-equipment-transported object-operation & environment-system management.” Expert judgments are then analyzed using an integrated DEMATEL-ISM approach to quantify inter-factor linkages and transmission pathways. The results indicate that regulatory oversight and carrier-side emergency equipment constitute the deep root causes of thermal runaway. The most hazardous transmission route is “regulatory oversight, procedural compliance and skill-experience match”, while “battery type, road/sea conditions and hoisting impacts” forms the shortest path. These findings reveal weaknesses in management and equipment that are amplified by operational execution and limited personnel capability, ultimately precipitating severe transportation incidents.
Yuan, Libo, Jiang, Huifu, Qin, Xiao
DisReID: Single Domain Generalizable Person Re-ID Method for Highway Construction & Operation2026-99-15309/11/2026
With the advancement of computer vision technologies and the widespread deployment of video surveillance systems, traffic safety and the development of intelligent highways have been significantly enhanced. As a key component of the intelligent video analysis module in smart highways, person re-identification (re-ID) addresses critical challenges, including cross-segment tracking of pedestrians illegally using emergency lanes, multi-camera joint searches for lost persons in service areas, and trajectory tracing of individuals involved in traffic accidents. These functions directly support the core goals of "safety assurance and efficient service" for smart highways. However, due to the complexity of the application scene, its generalization to unseen environments remains a core challenge. This problem is formally studied under the setting of Single-Domain Generalizable Person Re-identification (SDG re-ID), which aims to train a model on a single source domain that can perform well on arbitrary unseen target domains. To handle this issue, this paper proposes a novel Disentangled Augmentation re-ID Framework (DisReID) that disentangles and augments both structure and style. Specifically, DisReID consists of two modules: Structure-aware Viewpoint Simulation (SVS), a novel pre-processing technique that simulates cross-camera perspective changes by perspective transformation, diversifying geometric structure without harming identity semantics; and Style-Dominant Frequency Perturbation (SFP), which selectively focuses on the style-dominant frequencies and applies perturbation to enable controllable style augmentation while preserving structure cues. Furthermore, to alleviate the BN-induced domain bias, we introduce a simple yet effective test-time adaptation strategy, termed Cluster Fine-tuning (CF), that performs unsupervised clustering on target-domain features to assign pseudo-labels and subsequently fine-tunes the model, enhancing adaptability to unseen domains. Extensive experimental results on four public datasets demonstrate that our DisReID achieves superior generalization performance compared to the state-of-the-art methods. This work provides key technical support for the large-scale application of re-ID in smart highways, advancing the goal of "full-domain perception and intelligent collaboration".
Pan, Hong, Yu, Fangying
ASTDGCN: An Adaptive Spatial-Temporal Diffusion Graph Convolutional Network for Traffic Forecasting2026-99-15839/11/2026
Precise traffic flow prediction functions as the fundamental cornerstone for the efficient, safe, and reliable operation of intelligent transportation systems (ITS). It not only provides data-driven support for key applications, for instance, real-time traffic signal regulation, proactive congestion mitigation, and personalized route optimization, but also exerts a critical effect on reducing traffic accidents and improving overall urban travel efficiency. However, the traffic system belongs to a complex system, with spatio-temporal dynamics that are both intricate and variable, ranging from predictable fluctuations during morning and evening peak hours to localized propagation effects caused by accidents, as well as seasonal variations and significant nonlinear characteristics. These factors collectively pose substantial challenges to building accurate and reliable prediction models, creating a long-standing technical bottleneck in this field. With the aim of solving the dilemma that existing methods are hardly able to capture traffic flow’s spatio-temporal dependence effectively, we advance an adaptive spatial–temporal diffusion graph convolutional network (ASTD-GCN) for a traffic flow prediction model that integrates adaptive graph learning, diffusion convolution, and bi-directional long short-term memory network (Bi-LSTM) with attention mechanism. The model dynamically constructs the correlation between the nodes of the transportation network through the adaptive graph learning module and accurately describes the spatial topology. The diffusion convolution module realizes multi-order spatial information diffusion based on graph structure, which realizes the effective extraction of the traffic flow’s spatial dependence features. The Bi-LSTM module incorporating the attention mechanism captures the historical and future context information of traffic flow simultaneously through the bidirectional loop structure and the temporal attention mechanism, and strengthens the key time step features. Experimental results on -world traffic datasets PEMS03, PEMS04, PEMS07, and PEMS08 indicate that our proposed model exhibits better predictive precision in traffic flow forecasting tasks than baseline counterparts.
Li, Sumin, Gao, Yina, Zhu, Hongnian
Dimensional Adaptability and Traffic Organization Analysis of Ships in Group Lockage2026-99-15109/11/2026
Aiming at the technology of electric intelligent working boat towing 4 ships in group lockage during the construction period of the Gezhouba Shipping Capacity Expansion Project, this paper, starting from ship dimensions, conducts an adaptability analysis on electric intelligent working boat towing ships in group lockage for all ships passing through Gezhouba No. 1 and No. 2 Ship Locks throughout 2024, based on the two-dimensional packing model, ship lock chamber scheduling model and algorithms, navigation scheduling rules and safety management regulations, the results show that ships unsuitable for being towed in group lockage through Gezhouba No. 1 Ship Lock account for approximately 26% of the total number of ships passing through it, while those unsuitable for being towed in group lockage through Gezhouba No. 2 Ship Lock account for approximately 57% of its total passing ships. Meanwhile, ships passing through the three Gezhouba ship locks on a specific day are selected, and an adaptability analysis on the dimensional suitability of electric intelligent working boat towing these ships in group lockage is carried out under the scenario where these ships only pass through Gezhouba No. 1 and No. 2 Ship Locks. The results show that the number of ships with suitable dimensions for group lockage accounts for the majority of the ships passing through the locks on a selected specific day. On this basis, combined with the operation modes of Gezhouba No. 1 and No. 2 Ship Locks, an analysis is carried out on the traffic organization, potential risks, corresponding countermeasures, and the required quantity of electric intelligent working boats for towing ships in group lockage, targeting the three main collaborative operation modes. The results can provide a basis for the future practical application of electric intelligent working boat towing ships in group lockage during the construction and operation periods of the Gezhouba Shipping Capacity Expansion Project.
Huang, Shaowen, Yang, Xi, Wang, Jian
Analysis of Latency-Influencing Factors in Vehicle-Road-Cloud Integrated Systems Based on Fault Tree Analysis and Analytic Hierarchy Process2026-99-15439/11/2026
Vehicle–road–cloud integrated systems have great potential in terms of improving traffic efficiency and achieving intelligent automatic driving through the integration of on–board terminals, roadside facilities, and cloud computing. However, their operational capabilities are heavily reliant on ultra-low-latency collaborative communication. This paper constructs a latency fault tree model to comprehensively analyze multi-source triggering paths of computation delay and reveals the formation mechanism of the delay path from “germination–induction–evolution”. On this basis, the Analytic Hierarchy Process is used to construct a three-level evaluation framework, and the influencing factors are quantitatively evaluated using NS-3 simulation data of the 004-V2X Communication Performance Testing Dataset. The result shows that the weight value of the network communication layer is the largest, 0.498, which shows that the bottleneck of performance in network communication is the wireless link quality. The cloud processing layer is second 0.327, which is dominated by the computational complexity and resource allocation policy. The impact of the onboard terminal layer is the smallest, 0.175. The FTA–AHP framework supported by empirical data can find the key factors affecting delay, which can help engineering optimization. It is noted that the AHP consistency check (CR) just checks the inner transitivity of expert judgment (i.e., the matrix consistency), while it cannot assure the objectivity and the bias elimination. We reduce the subjectivity by combining multiple experts, anchoring judgment with the simulation data, and performing a sensitivity check on the perturbation of the weights.
Xu, Yunchuan, Wang, Xiaomeng, Wang, Yan
Research on the Synergetic Coupling Evaluation between the Yangtze River Delta Airport Cluster and the Regional Economy2026-99-15429/11/2026
To explore the coordinated development status between the Yangtze River Delta (YRD) airport cluster and the regional economy, this study takes the period from 2015 to 2023 as the research timeframe. It constructs an evaluation index system covering two dimensions: regional economy (including scale, structure, and benefit) and airport cluster development (including transportation scale, operation efficiency, among others). The Gini coefficient method and Pearson correlation coefficient method are used to screen indicators, while the entropy weight-standard deviation combined weighting method is adopted to calculate weights. Additionally, the coupling coordination model and geographical detector are integrated for in-depth analysis. The results show that the coupling coordination degree of the Yangtze River Delta region as a whole and its internal provinces and cities has rapidly recovered from the severe imbalance during the COVID-19 pandemic, featuring an inherent characteristic of “gradient catch-up and coordinated upgrading”. Factors such as the growth rate of passenger throughput and local fiscal general budget revenue have been identified as core influencing factors, and the interaction among these factors presents trends of two-factor enhancement and nonlinear enhancement. This study provides a theoretical basis and practical reference for promoting the integrated and coordinated development of the Yangtze River Delta airport cluster and the regional economy.
You, Zihao, Li, Yanwei
Assessment Model for Fitness for Work of Construction Personnel in Expressway Bridge Engineering Projects2026-99-15099/11/2026
To mitigate safety risks inherent in highway bridge construction, this research establishes a practical framework for assessing workers’ fitness for work. Using grounded theory, we analyzed interview records and documented accident cases through systematic coding, identifying critical indicators spanning physiological states, safety training effectiveness, and atypical behavioral markers. Rather than relying on single-method approaches, we combined Delphi expert consultation with entropy weighting to capture both professional judgment and data-driven variance, thereby reducing bias while preserving information richness. The resulting assessment protocol enables quantifiable classification of workers into distinct risk tiers. Implementation at the Zhangjinggao Yangtze River Bridge demonstrated the system's discriminatory power through field data collection and direct behavioral monitoring, successfully segmenting the workforce into low-, medium-, and high-risk categories. Results suggest the tool functions effectively as a pre-employment screening mechanism, allowing project managers to intercept potentially unfit workers before they enter hazardous work zones, consequently lowering the incidence of human-factor accidents.
Wu, Zhongguang, Dai, Junping, Ruan, Jing, Shi, Yonglong, Yuan, Zhenzhong, Hao, Jiatian
Optimization of Order Picking in an AMR-Assisted Picker-to-Parts System2026-99-15679/11/2026
With global retail sales expanding and same-day delivery demand on the rise, efficient order picking operations in warehouses have become critical to success. To improve order picking processes, warehouse managers increasingly rely on autonomous mobile robots (AMRs), which improve the performance of traditional picker-to-parts systems. This paper investigates an AMR-assisted picker-to-parts system in which a set of customer orders must be fulfilled. The orders are first batched, and the resulting batches are assigned to individual pickers. Each picker works in a batch-by-batch manner, manually retrieving items from picking aisles and handing over the completed batch to an AMR waiting at the cross aisle. After receiving a full batch, the AMR transports it to the designated depot before returning to serve the next batch. The objective is the minimization of the total tardiness of all orders. The problem is formulated as a mixed-integer programming (MIP) model, and several effective heuristic algorithms are developed. Extensive computational experiments are conducted to evaluate the performance of the proposed algorithms and compare them with a commercial MIP solver.
Jin, Bo, Peng, Jianxin
SAE TOMORROW TODAY - Liability in the Age of Autonomous Vehicles135839/8/2026
When AI is driving, who's responsible? Listen in as we sit down with Jennifer Dukarski, Emerging Tech, Media, and Intellectual Property Shareholder at Butzel, to unpack the rapidly evolving legal landscape surrounding SDVs and AVs. From AI decision-making and over-the-air updates to data retention, cybersecurity, consumer education, and the growing complexity of liability, we explore how existing legal frameworks are being tested by next-gen mobility. Whether you're developing autonomous technology, managing risk, or shaping industry standards, this conversation offers an insightful look at the legal challenges that will define the future of transportation. Have your own thoughts on this topic? We'd love to hear from you! Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
Research on Multi-Condition Topology Optimization of Transport Ship Pillar Structure2026-99-12289/4/2026
Structural optimization in shipbuilding represents a significant research focus within the fields of naval architecture and marine engineering. This study investigates multi-condition topological optimization for the deck pillar region of a transport ship's sectional structure. A mechanical model incorporating six typical load conditions was developed, and the Analytical Hierarchy Process (AHP) was employed to quantify the weighting coefficients for each condition. This enabled multi-condition collaborative topological optimization of the pillar layout. The optimized configuration underwent model reconstruction and finite element verification. Results demonstrate that the proposed multi-condition collaborative topology optimization method effectively balances structural performance and weight reduction requirements while satisfying strength specifications. This method yields optimal pillar layouts meeting multi-condition constraints, providing a reference for multi-condition topology optimization studies in ship structures.
Pei, Zihao, Wei, Yi, Feng, Ruge, Liu, Kun
Automotive Engineering: September 202626AUTP099/3/2026
Bespoke at scale: How generative AI is rewriting the economics of manufacturing automation The economic logic behind factory software upgrades is breaking down. Navigating an increasingly complex engine oil landscape As the impending ILSAC GF-8, dexos 1 Gen 4, and a hybrid specification continue to obscure the North American landscape, passenger car engine oils need additive solutions that can help simplify blending operations and optimize product portfolios. High-temperature joint reliability Why preload retention matters more than fastener grade. Designing out corrosion at the wheel The case for advanced powder coatings. Editorial Where to? The challenges of automated driving around the world The Navigator Is China really our biggest automotive security threat? Better safety through collaboration at Toyota research center ATS 2026: automated driving is changing The software-defined technician: Manufacturing's next human capital revolution Aumovio says system can BAC with non-invasive finger touch How conveyors have carved a role in mastering manufacturing 2027 Mercedes-Benz S-Class: Same as ever, which is very good 2026 Lucid Gravity: Highly efficient luxury hauler Product Briefs Spotlight: Adhesives and coatings; sustainable materials Q&A How an automotive startup uses and doesn't use customer data
THE NAVIGATOR26AUTP09_069/1/2026
In the past two decades, the global automotive industry has been flipped on its head. Even before I started writing professionally about this business, Chinese-made cars were something of a joke and European and North American automakers were dominating the still-nascent market for consumer vehicles in China. China is now the largest automotive market in the world by a wide margin, Chinese-brand vehicles are generally highly respected, and American politicians are lying about why consumers here can't get access to them. In the late 2010s, we began to see a shift in the Chinese market that has come to fruition in the past five years. When I attended the 2007 North American International Auto Show in Detroit as a member of the media for the first time, the first Chinese vehicles were being displayed on the show floor. Let's just say no one was impressed. Much of what was coming from Chinese brands at the time were either weird, outdated machines or the automotive equivalent of the Rolex knock-offs you could find on Canal St. in New York.
683 Guidance for Electronic Shop Reports in Aviation MaintenanceARINC683 (Current)8/4/2026
The purpose of this standard is to provide guidance for the content, format and two-way electronic exchange of data between a Service Provider and the Customer. The standard is focused on the production of an electronic Shop Findings Report by the Service Provider, and to promote automated electronic data exchange.
Aviation Maintenance Committee
Engineering for Uptime: A Blueprint for Commercial Vehicle Transformation26TOFHP08_018/1/2026
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.
Gehm, Ryan
Q&A with Volvo's Powertrain chief26TOFHP08_038/1/2026
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.
Clonts, Chris
EDITORIAL: Needed: Engineers “at the intersections”26TOFHP08_048/1/2026
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.
Gehm, Ryan
Rivian's Scaringe says R2 will help pay huge R&D costs26TOFHP08_088/1/2026
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.
Clonts, Chris
Prototype Reflectarray Antenna Demonstrates Low-SWaP Anti-Jam Tactical SATCOM26AERP08_108/1/2026
This prototype array uses a small, lightweight, low-power, and low-cost aperture. Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA Preventing adversaries from interfering with communications is crucial to national security. Tactical satellite communications (SATCOM) focus on securing reliable communications channels against adversaries in contested environments. In support of this mission, a team from MIT Lincoln Laboratory is building a prototype antenna characterized by low size, weight, power, and cost (SWaP-C). Threats in contested environments - specifically proliferated low-Earth orbit (pLEO), where satellites must be as low-SWaP as possible because of the high volume of satellites present - are signal jamming and signals intelligence. Mitigating these threats through methods such as changing the shape of antenna beams in real time so that the ground user's signals can't be interfered with and preparing for future advanced capabilities are key to ensuring that satellites stay in communication with users on the ground.
Solid-State System Efficiently Generates Purified OxygenTBMG-555398/1/2026
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.
Microwaves Enable 3D Printing of High-Performance CeramicsTBMG-555448/1/2026
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.
A Biohybrid Micro Swimmer Unaffected by CoatingTBMG-555368/1/2026
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.
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