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This SAE Aerospace Information Report (AIR) supplies information on trimmable horizontal stabilizer actuator structural load path integrity. It describes the different methods for detecting rupture or disconnection of load paths. It also describes the monitoring principle to compare existing solutions as a reference for its implementation in new aircraft programs.
A-6B3 Electro-Mechanical Actuation Committee
G-3, Aerospace Couplings, Fittings, Hose, Tubing Assemblies
The gear lubricants covered by this standard exceed American Petroleum Institute (API) Service Classification API GL-5 and are intended for automotive units with the primary drive hypoid gears, operating under conditions of high-speed/shock load and low-speed/high-torque. These lubricants may be appropriate for other gear applications where the position of the shafts relative to each other and the type of gear flank contact involve a large percentage of sliding contact. Such applications typically require extreme pressure (EP) additives to prevent the adhesion and subsequent tearing away of material from the loaded gear flanks. These lubricants are not appropriate for the lubrication of worm gears. The information contained within is intended for the demonstration of compliance with the requirements of this standard and for listing on the Qualified Products List (QPL) administered by the Lubricant Review Institute (LRI). A complete listing of qualification submission requirements and
Fuels and Lubricants TC 3 Driveline and Chassis Lubrication
The purpose of this SAE Standard is to establish the specific minimum equipment requirements for recovery/recycling/recharge equipment intended for use with both R-1234yf and R-134a in a common refrigerant circuit that has been directly removed from, and is intended for reuse in, mobile air-conditioning (A/C) systems. This document does not apply to equipment used for R-1234yf and R-134a having a common enclosure with separate circuits for each refrigerant, although some amount of separate circuitry for each refrigerant could be used.
Interior Climate Control Service Committee
The following schematic diagrams reflect various methods of illustrating automotive transmission arrangements. These have been developed to facilitate a clear understanding of the functional interrelations of the gearing, clutches, hydrodynamic drive unit, and other transmission components. Two variations of transmission diagrams are used: in neutral (clutches not applied) and in gear. For illustrative purposes, some typical transmissions are shown.
Automatic Transmission and Transaxle Committee
This article presents a system to incorporate crash risk into navigation routing algorithms, enabling safety-aware path optimization for autonomous and human-driven vehicles alike. Current navigation systems optimize travel time or distance, while our approach adds crash probability as a routing criterion, allowing users to balance efficiency with safety. We transform disparate data sources, including traffic counts, crash reports, and road network data, into standardized risk metrics. Because traffic volume data only exist for a small subset of road segments, we develop a solution to project average daily traffic estimates to an entire road inventory using machine learning, achieving sufficient coverage for practical implementation. The framework computes exposure-normalized crash rates weighted by severity and integrates these metrics into routing cost functions compatible with existing navigation algorithms. The key strength of our solution is its scalability. In addition to the
Skaug, LarsNojoumian, Mehrdad
This study investigated the combustion processes in hydrogen dual-fuel operation using hydrotreated vegetable oil (HVO) and diesel fuel as pilot fuels. The visualizations of hydrogen dual-fuel combustion processes were conducted using hydroxyl radical (OH*) chemiluminescence imaging in an optically accessible rapid compression and expansion machine (RCEM), which can simulate a compression and expansion stroke of a diesel engine. Pilot injection pressures of 40 and 80 MPa and injection quantities of 3, 6 mm3 for diesel fuel and to match the injected energy, 3.14, 6.27 mm3 of HVO were tested. The total excess air ratio was kept constant at 3.0. The RCEM was operated at a constant speed of 900 rpm, with in-cylinder pressure at top dead center (TDC) set to approximately 5.0 MPa. Results demonstrated that using HVO as pilot fuel, compared to diesel fuel, led to shorter ignition delay and combustion duration. OH* chemiluminescence imaging revealed that longer ignition delays observed with
Mukhtar, Ghazian AminUne, NaotoHoribe, NaotoHayashi, JunKawanabe, HiroshiHiraoka, KenjiKoda, Kazuyuki
The fuel management system for a fixed-wing aircraft has been developed and explored with the model-based systems engineering (MBSE) methodology for maintaining the center of gravity (CoG) and analyzing flight safety. The system incorporates high-level modeling abstractions that exploit a mix of behaviors and physical detail resembling real-world components. This approach enables analysis for a multitude of system requirements, verification, and failure scenarios at high simulation speed, which is necessary during system definition. Initially, the CoG is maintained by directly accessing the flight deck valves and pumps in both wings and controlling them through the bang-bang control law. In the refinement phase of the fuel system controller, the manual and individual controls of the valves and pumps are replaced with an autonomous fuel transfer scheme. The autonomous scheme achieves no more than a 20 kg difference in fuel between the wings during normal conditions. In the event of
Zaidi, YaseenMichalek, Ota
One of the biggest goals for companies in the field of artificial intelligence (AI) is developing “agentic” systems. These metaphorical agents can perform tasks without a guiding human hand. This parallels the goals of the emerging urban air mobility industry, which hopes to bring autonomous flying vehicles to cities around the world. One company wants to do both and got a head start with some help from NASA.
This specification covers a coating consisting of finely powdered graphite in a heat-resistant inorganic binder applied to parts.
AMS B Finishes Processes and Fluids Committee
Environmental perception is the base of autonomous driving systems, and it directly affects both operational safety and intelligent decision-making capability. Among the emerging technologies, vision-based 3D occupancy prediction is gaining more attention because of its high cost-effectiveness and high-resolution scene understanding capability. However, existing methods often have too much model complexity and limited inference efficiency, which makes deployment on resource-constrained embedded platforms difficult. To address the limitations, we propose LWMOcc, a lightweight monocular 3D occupancy prediction framework. The main component of LWMOcc is the lightweight Encoder-Decoder module, which is a lightweight fine-grained scene perception module that combines a simplified backbone with an efficient decoding strategy. By performing structural simplification and parameter compression, LWMOcc effectively reduces computational overhead, while retaining high predictive accuracy
Chen, FeiyangLi, JihaoFu, PengyuHu, JinchengLiu, MingLiu, ChengjunHong, YinuoCazorla, MiguelGonzález Serrano, GermánZhang, YuanjianCadini, Francesco
As intelligent cockpit technology continues to evolve, the ways in which information is presented and interacted with within vehicle systems are becoming increasingly diverse, driving the development of driver-machine interaction toward multi-modal integration, proactive sensing, and personalized responses. As the core perception object of the intelligent cockpit, the accuracy of driver state recognition directly impacts the intelligence level of cockpit interaction and driving safety. In response to the increasing trend of task diversity and behavioral response complexity in natural driving scenarios, there is an urgent need to develop a driver multimodal data collection and processing tool with high timeliness, non-intrusiveness, and multi-source synchronization capabilities, serving as the key foundation for driver state modeling and intelligent interaction support. Based on multiple resource theory (MRT) and driver status perception mechanisms, this study designs and develops a
Chen, KeLi, XinyiCheng, JiahaoGuo, GangLi, Wenbo
Vehicle stability is fundamental to the safe operation of intelligent vehicles, and real-time, high-accuracy calculation of the stability domain is crucial for maintaining control across the full range of driving conditions. Because the real stability domain is difficult to parameterize accurately and is shaped by multiple driving factors including vehicle-dynamics parameters and environmental conditions, existing approaches fail to capture the multidimensional couplings between time-varying driving inputs and the resulting stability boundaries. Moreover, these methods remain overly conservative owing to algorithmic limitations and cautious design assumptions, thereby restricting dynamic performance in complex scenarios. To address these limitations, this paper introduces a multidimensional vehicle dynamic stability region calculation framework under time-varying driving conditions and apply it into path tracking controller of intelligent vehicle. Sum-of-squares programming (SOSP) is
Wang, ChengyeZhang, YuHu, XuepengQin, HaipengWang, GuoliQin, Yechen
With the advancement of wireless technology within the automotive industry, vehicle antenna measurement has garnered increasing attention, as antenna system performance exerts critical influences on wireless communication performance. In spherical near-filed (SNF) automotive measurement, the assignment of minimum sphere radius (MSR) is of paramount significance in reducing test duration. Current industrial practice typically presumes the aperture equivalent to the entire vehicle, consequently assigning the minimum sphere to enclose the entire vehicle structure. Such a sampling scheme, however, is often redundant since regions distant from the antenna experience weak illumination and contribute negligibly to radiation, particularly at higher frequencies. Thus, determining the effective aperture becomes essential for MSR reduction and enhanced testing efficiency. To this end, this paper investigated the effective aperture of vehicle-mounted antenna (VMA) to reduce the test duration. The
Yang, XinChen, RuiZhou, LilingTao, Tingting
For driver-automation collaborative driving, accurately monitoring driver state in smart cockpits is crucial for enhancing safety, comfort, and human-computer interactions. However, existing research lacks clarity regarding the relationships among driver states, and there is no consensus on the optimal physiological channels to reliably capture these states. This study examined three critical psychological constructs (i.e., perceived risk, trust in the automated driving system, and driver fatigue) using a 37-participant driving simulation experiment. We manipulated multiple factors to induce distinct driver states among participants and recorded subjective scale ratings, heart rate variability, galvanic skin response, and eye movement data. Subjective scale ratings were adopted as the ground truth to examine the corresponding measurement relationships between different physiological signals and the three targeted dimensions of driver states. Our results proved that perceived risk
Wang, ZhenyuanLi, QingkunWang, WenjunLiu, WeiminSun, ZhaocongCheng, Bo
This study presents a structured evaluation framework for reasonably foreseeable misuse in automated driving systems (ADS), grounded in the ISO 21448 Safety of the Intended Functionality (SOTIF) lifecycle. Although SOTIF emphasizes risks that arise from system limitations and user behavior, the standard lacks concrete guidance for validating misuse scenarios in practice. To address this gap, we propose an end-to-end methodology that integrates four components: (1) hazard modeling via system–theoretic process analysis (STPA), (2) probabilistic risk quantification through numerical simulation, (3) verification using high-fidelity simulation, and (4) empirical validation via driver-in-the-loop system (DILS) experiments. Each component is aligned with specific SOTIF clauses to ensure lifecycle compliance. We apply this framework to a case of driver overreliance on automated emergency braking (AEB) at high speeds—a condition where system intervention is intentionally suppressed. Initial
Kang, Do WookKim, WoojinJang, Eun HyeChang, MiYoon, DaesubJang, Youn-Seon
With the advancement of automated driving system levels, corner scenarios characterized by low probability and high risk have become critical for the safety validation of automated vehicles. However, due to the typical long-tail distribution of such scenarios, data-driven mining approaches face significant challenges in achieving efficient generation. To address this issue, this study proposes a feature-optimized combination-based method for generating corner scenarios in automated driving systems. Key scenario features related to functional failures are first identified using a combined approach of system theoretic process analysis (STPA) and hazard and operability analysis (HAZOP). Based on these features, an adaptive genetic algorithm is employed to optimize feature combinations and generate large numbers of corner scenario types that meet specified constraints. The proposed method is validated using cut-in and pedestrian-crossing scenarios as baseline cases. The results show that
Zhou, ShiyingZhang, DongboZhao, DeyinZhu, BingZhang, Peixing
The VINS-Mono algorithm, which is based on a visual-inertial SLAM framework, faces challenges in extracting feature points in regions with weak or repetitive textures and struggles to achieve accurate localization under unstable lighting conditions. This paper proposes STO-VINS, a robust monocular visual-inertial SLAM algorithm that introduces several key innovations in feature extraction. Key innovations of STO-VINS include: (1) an adaptive multi-scale image preprocessing pipeline that combines image scaling, CLAHE enhancement, and Gaussian filtering, reducing computational complexity by 64% while maintaining feature quality; (2) bidirectional Lucas-Kanade optical flow consistency verification with geometric constraint validation, which significantly reduces false tracking rates by 30-40%; (3) a grid-based multi-feature fusion detection strategy combining Shi-Tomasi corner detection and ORB feature extraction, ensuring uniform spatial distribution of features and feature diversity; (4
Li, JingWu, JingLiu, BoGong, ZeyuanZhang, Guofang