Your Destination for Mobility Engineering Resources

Announcements for SAE Mobilus

Browse All

Recent SAE Edge™ Research Reports

Browse All 177

Recent Books

Browse All 718

Recently Published

Browse All
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
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
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 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
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
To address the issues of multiple background interferences and blurred road boundaries in unstructured scene road segmentation tasks, a lightweight and precise unstructured road segmentation model based on cross-attention (CANet) is proposed. This model constructs an encoder using the lightweight neural network MobileNetV2. By doing so, it ensures light weight while enhancing the feature discrimination ability of unstructured roads, thus achieving efficient feature extraction. The decoder integrates the cross-attention mechanism and a low-level feature fusion branch. The attention mechanism improves the model’s perception of road boundaries by capturing long-distance context information in the feature map, thereby solving the problem of blurred edges. The low-level feature fusion branch enhances the detail accuracy and edge continuity of the segmentation results by incorporating high-resolution information from shallow features. Experimental results show that the proposed model attains
Wang, XueweiCao, GuangyuanLiang, XiaoLi, Shaohua
This paper addresses the scarcity of training and testing data in autonomous driving scenarios. We propose a 3D reconstruction framework for autonomous driving scenes based on Neural Radiance Fields (NeRF). Compared to traditional multi-view geometry methods, NeRF offers superior scene representation and novel view synthesis capabilities but suffers from low training efficiency and limited generalization. To overcome these limitations, we integrate existing NeRF optimization techniques and introduce a scene-specific data reuse strategy tailored for autonomous driving, enabling continuous 3D reconstruction directly from 2D images without requiring elaborate calibration. This approach significantly improves reconstruction efficiency, achieving reliable reconstruction and real-time visualization in complex traffic environments. Furthermore, we develop an interactive scene editing plugin in Unreal Engine 5, supporting the addition, removal, and adjustment of static objects. This extension
Pan, DengZou, JieChen, YuhanMeng, ZhangjieLi, JieLi, Guofa
Ensuring the safe and stable operation of autonomous vehicles under extreme driving conditions requires the capability to approach the vehicle’s dynamic limits. Inspired by the adaptability and trial and error learning ability of expert human drivers, this study proposes a Self-Learning Driver Model (SLDM) that integrates trajectory planning and path tracking control. The framework consists of two core modules: In the trajectory planning stage, an iterative trajectory planning method based on vehicle dynamics constraints is employed to generate dynamically feasible limit trajectories while reducing sensitivity to initial conditions; In the control stage, a neural network enhanced nonlinear model predictive controller (NN-NMPC) is designed, which incorporates a self-learning mechanism to continuously update the internal vehicle model using trial-and-error data on top of mechanistic physical constraints, thereby improving predictive accuracy and path-tracking performance. By combining
Zhang, XinjieXu, LongGuo, KonghuiZhuang, YeHu, TiegangMao, JingGangZeng, Qingqiang
In the testing and validation of autonomous driving systems, scenario-based simulation is crucial to address the high costs and insufficient scene coverage of real-road testing. However, existing simulators rely on handcrafted rules to generate traffic scenarios, failing to capture the complexity of multi-agent interactions and physical rationality in real traffic. This paper proposes STGT-Gen, a data-driven Spatio-Temporal Graph Transformer framework, to generate realistic and diverse multi-vehicle traffic scenarios by integrating spatio-temporal interaction modeling, physical constraints, and high-definition (HD) map information.STGT-Gen adopts an encoder-decoder architecture: The encoder captures temporal dependencies of vehicle trajectories and spatial interactions via a Temporal Transformer and a Spatial Graph Transformer, respectively, while a hierarchical map encoding module fuses lane topologies and traffic rules. The decoder ensures physical feasibility during long-term
Qin, XupengLu, ChaoWei, YangyangFan, SizheSong, ZeGong, Jianwei
Rainfall, as a common trigger condition in the Safety of the Intended Functionality (SOTIF) framework, can impair autonomous driving perception systems, leading to unexpected functional failures. However, studies focusing on sensor performance degradation under natural rainfall conditions are limited, primarily due to the lack of datasets with detailed rainfall information. To address this gap, this study present RainSense, a multi-sensor autonomous driving dataset collected under natural rainfall conditions, featuring fine-grained rainfall intensity annotations. RainSense was recorded at nine representative intersection scenarios in the campus, where a single dummy target was placed at various distances as a detection target. A laser-optical disdrometer was deployed to continuously measure rainfall intensity (mm/h), while camera images, lidar point clouds, and 4D radar data were synchronously collected under different rainfall levels. In total, the dataset comprises 728 cases
Xia, TianYang, XingboChen, TianruiZhang, LonggaoYe, ShaolingfenChen, Junyi
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
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