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Important Update – Accessibility Enhancements on SAE Mobilus

We’re pleased to share that SAE Mobilus has recently completed a comprehensive set of accessibility improvements across the platform. As a result, Mobilus is now aligned with WCAG 2.2 Level AA standards in most key categories. These updates are part of our ongoing commitment to ensuring the platform is accessible to all users. Enhancements include improvements to keyboard navigation, screen reader compatibility, visual contrast, error handling, and overall usability across core areas of the site. An updated Voluntary Product Accessibility Template (VPAT v2.5 – March 2026) is now available, providing a detailed overview of our accessibility conformance. You can access the VPAT directly within the Help section. Accessibility is a continuous effort. We will continue to evaluate and improve the platform to better support all users and meet evolving standards. If you have any feedback or experience any accessibility issues, we welcome your input. - The SAE Mobilus Team

SAE AI Chat — Update and What's Next

* Important Update - SAE AI chatbot Beta program * The SAE AI Chatbot Beta program - Phase 1 will end on March 31, 2026. Following this date, the AI chatbot capabilities will be temporarily unavailable as we begin development of a significantly enhanced version. We sincerely thank all Phase 1 Beta participants for their engagement and valuable feedback - your insights have played an important role in shaping the next phase of this experience. The upcoming version of the SAE AI Chatbot will expand content coverage beyond current standards, incorporating additional critical resources from our digital library. It will also be made available to a broader audience across Mobilus. Want to be part of what's next? Register your interest here to receive updates and be considered for early access. For additional information, visit the FAQs. — The Mobilus Team

SAE International Digitization and AI Policy

SAE International prohibits the entry of SAE Content into any form of Artificial Intelligence (AI) tools, such as ChatGPT, OpenAI, or Microsoft Copilot. Additionally, creating derivatives of SAE Content using AI is also prohibited without express written permission from SAE International. In the case of such use, SAE International will suspend access to SAE Content, and further legal action will be considered.

Welcome to the new SAE Mobilus®

Welcome to the new era of SAE Mobilus®, redesigned with a modern, intuitive web interface featuring enhanced search capabilities to help you stay informed and easily discover content. Login today: To access content within your subscription and make the most of the platform, please log into your subscription and your personal My Library (sign up is free) located in the top right corner of the screen. Key Features Intuitive User Interface: We've listened to our customers and tied this to the best practices in the industry to offer an improved, intuitive, and desirable website experience for our users. Improved In-app Discoverability: Enhanced search and expanded menu featuring search suggestions that allow for quick discovery of content. Announcements: Stay up to date on new features through a dedicated announcements page. Most recent announcements also get featured on the homepage. Enhanced Browse capabilities: Browse through over 200,000 publications that are now classified into categories commonly used across the industry. Deep-dive further into any topic or sub-topic of interest to make the most for your corporate projects or research. Smart Automation: "Meta Tags" on each result will help you find content related to your search. My Library: My dashboard is rebranded as My Library with powerful features to help you make the most out of the platform Mobile Friendly: Upgraded navigation on mobile devices Training Available SAE’s Customer Success team is dedicated to delivering the best platform experience possible. Through monthly trainings and short, on-demand video tutorials, find answers to your questions here: SAE Mobilus Spotlight training - Available monthly, hosted live by SAE’s Customer Success team Mobilus Minute – 2-minute videos, available on-demand Need some troubleshooting help? Access our FAQs by visiting our Help section: https://saemobilus.sae.org/help/home Resources to help you navigate the new platform are available here, where you can find links to quick start guides and video tutorials on features like My Library, Announcements, Saved Search, Login and new features. Have feedback that you'd like to share with us? Please submit your feedback to the Mobilus team through the blue Knowledge bot in the bottom right hand corner of the new platform.

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Recent SAE Edge™ Research Reports

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Wing-in-Ground Effect Vehicles: From Modern Military and Commercial Development to Global Disaster Response

EPR202501711/24/2025

The wing-in-ground effect (WIG) vehicle represents a significant advancement in aerodynamics and vehicle design, leveraging the ground effect phenomenon to enhance lift and reduce drag when flying close to the surface. This unique capability allows WIG vehicles to achieve higher payloads, longer range, and greater fuel efficiency compared to traditional aircraft, making them an attractive option for modern military and global disaster response applications. Wing-in-Ground Effect Vehicles: From Modern Military and Commercial Development to Global Disaster Response discusses future disaster response, logistics, and military applications for WIG vehicles, including the ongoing development of aerospace and transportation technology. Relavant advancements in materials and propulsion systems holds promise for further enhancing WIG performance and operational range. Additionally, cost-effective and powerful flight computers with various types of mission-enabling sensor suites from the

Next-gen AI for Aerospace Engineering

EPR20250169/22/2025

Despite growing investments, the widespread adoption and scalable deployment of generative artificial intelligence (AI) remains a challenge due to data trustworthiness, regulatory uncertainty, interpretability, and ethical governance. The need to accelerate automation and maintain the human-in-the-loop demonstrates broader questions of responsibility and transparency. Next-gen AI for Aerospace Engineering investigates the transformative role of GenAI within aerospace engineering, examining its shift from conventional workflows toward more AI-driven solutions in design, manufacturing, and maintenance. It emphasizes GenAI’s emerging ability to automate repetitive mundane tasks, reduce design complexity, and optimize engineering pipelines. The report underscores the need for validation methods that must align AI-generated outputs with physics-informed models, integration with legacy engineering tools (e.g., computational fluid dynamics, finite element analysis, digital twins), and

Sustainable Circular Future Mobility: Environmental Impact of Next-gen Vehicles

EPR20250159/22/2025

The next generation of mobility, driven by shared, driverless, connected, and electrified vehicles, holds strong potential to advance sustainability through lower emissions and improved resource efficiency. However, critical questions remain regarding their true environmental impact, including battery lifecycle management, material consumption, and circular manufacturing practices. Sustainable Circular Future Mobility: Environmental Impact of Next-gen Vehicles explores these unresolved issues, focusing on the shift from internal combustion to electric vehicles, supply chain challenges, regulatory gaps, and the operational realities of sustainable productization. It also critically examines the risks of greenwashing, the need for consistent standards, and the role of intersectoral collaboration—with energy, urban planning, information and communications technologies, and waste management sectors—in building resilient, scalable solutions. The report provides strategic recommendations and

Small, Mobile, and Autonomous Agricultural Robots

EPR20250128/13/2025

The automation of labor-intensive picking and planting operations is having an immediate impact in the agricultural indutry. In its simplest form, robotic automation can reduce the labor and soil disturbance while enabling organic soil cover and increasing species diversification through precision approaches to planting, weeding, and spraying. With this, pesticides and fertilizers can be applied in a more targeted way, and with machinery visiting fields more frequently, earlier and more targeted intervention can occur before pests become established. Small, Mobile, and Autonomous Agricultural Robots identifies issues that need to be resolved fo for this technology to thrive, including improving methods of acquiring and labeling training data to facilitate more accurate models for specific applications. It also discusses concepts such as general-purpose mechanical platforms for use as carriers of agricultural automation systems with high stability, positional accuracy, and variable

Safety Impacts of Active Safety and Driving Automation Features

EPR20250138/13/2025

Advanced technologies that assist the human driver or reduce (or even eliminate) the human driver’s role are becoming increasingly prevalent in new light-duty vehicles used by the general public. These technologies are divided between Active Safety features that monitor the human driver and vehicle motion and act intermittently to mitigate and avoid crashes, and Driving Automation features that assume some or all of the dynamic driving task from the human driver. Both types of technologies have the potential to reduce injuries and save lives by reducing the frequency and/or severity of crashes. Safety Impacts of Active Safety and Driving Automation Features addresses the current capabilities and future potential for Active Safety and Driving Automation features to reduce crash frequency and severity and provides an overview of the state of the industry for both types of features, including current deployments, trends, and anticipated rollouts. Gaps in knowledge, unsettled issues, and

Hybrid Data- and Physical Model-driven Safe and Intelligent Motion Planning and Control for Autonomous Vehicles

EPR20250147/18/2025

Autonomous vehicle motion planning and control are vital components of next-generation intelligent transportation systems. Recent advances in both data- and physical model-driven methods have improved driving performance, yet current technologies still fall short of achieving human-level driving in complex, dynamic traffic scenarios. Key challenges include developing safe, efficient, and human-like motion planning strategies that can adapt to unpredictable environments. Data-driven approaches leverage deep neural networks to learn from extensive datasets, offering promising avenues for intelligent decision-making. However, these methods face issues such as covariate shift in imitation learning and difficulties in designing robust reward functions. In contrast, conventional physical model-driven techniques use rigorous mathematical formulations to generate optimal trajectories and handle dynamic constraints. Hybrid Data- and Physical Model-Driven Safe and Intelligent Motion Planning and

Product Assurance in the Age of Artificial Intelligence

EPR20250115/21/2025

Driven by the vast consumer marketplace, the electronics megatrend has reshaped nearly every sector of society. The advancements in semiconductors and software, originally built to serve consumer demand, are now delivering significant value to non-consumer industries. Today, electronics are making inroads into traditionally conservative, safety-critical sectors such as automotive and aerospace. In doing so, electronics—now further propelled by artificial intelligence—are disrupting the functional safety architectures of these cyber-physical systems. Electronics have created the world of cyber-physical systems, raising broader concerns about the broader category of product assurance. Product Assurance in the Age of Artificial Intelligence continues the work of previous SAE Edge Research Reports in examining open research challenges arising from this shift, particularly in automotive systems, as core electronic technologies (e.g., the combination of software and communications) have even

Leveraging Advanced Data Technologies for Smart Traffic Management

EPR20250085/20/2025

The transportation industry is transforming with the integration of advanced data technologies, edge devices, and artificial intelligence (AI). Intelligent transportation systems (ITS) are pivotal in optimizing traffic flow and safety. Central to this are transportation management centers, which manage transportation systems, traffic flow, and incident responses. Leveraging Advanced Data Technologies for Smart Traffic Management explores emerging trends in transportation data, focusing on data collection, aggregation, and sharing. Effective data management, AI application, and secure data sharing are crucial for optimizing operations. Integrating edge devices with existing systems presents challenges impacting security, cost, and efficiency. Ultimately, AI in transportation offers significant opportunities to predict and manage traffic conditions. AI-driven tools analyze historical data and current conditions to forecast future events. The importance of multidisciplinary approaches and

Edge Artificial Intelligence in Connected, Cooperative and Automated Mobility

EPR20250095/20/2025

With many stakeholders involved, and major investments supporting it, the advancements in automated driving (AD) are undoubtedly there. Generally speaking, the motivation for advancing AD is driver convenience and road safety. Regarding the development of AD, original equipment manufacturers, technology start-ups, and AD systems developers have taken different approaches for automated vehicles (AVs). Some manufacturers are on the path toward stand-alone vehicles, mostly relying on onboard sensors and intelligence. On the other hand, the connected, cooperative, and automated mobility (CCAM) approach relies on additional communication and information exchange to ensure safe and secure operation. CCAM holds great potential to improve traffic management, road safety, equity, and convenience. In both approaches, there are increasingly large amounts of data generated and used for AD functions in perception, situational awareness, path prediction, and decision-making. The use of artificial

Space-based Solar Power for Instantaneously Dispatchable Renewable Power on Earth

EPR20250054/24/2025

Recent advances are reducing the cost of space launch, high specific power solar cells, and the production of satellite systems. Modular architectures with no moving parts and distributed power systems would minimize assembly and maintenance costs. Together, this may enable space-based solar power to provide decarbonized dispatchable power at a lower cost than equivalent technologies such as nuclear power stations. Space-based Solar Power for Instantaneously Dispatchable Renewable Power on Earth discusses the advances in emerging technologies, like thin film solar cells, reusable launch vehicles, and mass-produced modular satellite systems that would make economic space power feasible. Click here to access the full SAE EDGETM Research Report portfolio.

Latest Journal Issues

Browse All 16

Passenger Vehicle Systems

Electrified Vehicles

Advances and Current Practices in Mobility

Sustainable Transportation, Energy, Environment, & Policy

Connected and Automated Vehicles

Transportation Cybersecurity and Privacy

Vehicle Dynamics, Stability, and NVH

Transportation Safety

Alternative Powertrains

Commercial Vehicles

Engines

Materials and Manufacturing

Fuels and Lubricants

Aerospace

Passenger Cars - Mechanical Systems

Recent Books

Browse All 720

Aerospace Standards Index - 2026

ASIN20262/25/2026

This valuable resource lists all Aerospace Standards (AS), Aerospace Recommended Practices (ARP), Aerospace Information Reports (AIR), and Aerospace Resource Documents (ARD) published by SAE. Each listing includes title, subject, document number, key words, new and revised documents, and DODISS-adopted documents. AMS Index - Now Available!

Business Reliability Growth for Automotive Engineering, Volume 4

R-5522/17/2026

In a world where every business process is under pressure to perform faster, safer, and more reliably, this book delivers a powerful roadmap for sustained operational excellence. Centered on the proven methodology of Design of Experiments (DOE), it shows how organizations can move beyond reactive problem-solving to systematic reliability growth. From well-defined standard operating practices to management-level decision-making, the book connects strategy, data, and execution to create repeatable, measurable results across the enterprise. Readers are guided through practical, real-world applications of DOE, from selecting the right factors and levels to executing robust experiments, analyzing outcomes, validating solutions, and continuously monitoring performance. Each chapter translates complex statistical and engineering concepts into actionable business value, helping teams improve quality, reduce waste, and increase return on investment. Key capabilities explored in this book

January 2026 AMS Index

AMSJAN-20261/24/2026

Fundamentals of Sustainable Aviation Fuels

R-5471/22/2026

As global energy demands rise and environmental challenges intensify, the aviation industry faces a critical need to redefine how it powers the skies. Fundamentals of Sustainable Aviation Fuels presents a comprehensive, research-driven exploration of the transition from fossil-based fuels to renewable alternatives that promise to reshape the future of air transportation. This book bridges the gap between academic knowledge and industrial application, offering a detailed overview of the science, technology, and policy driving the adoption of Sustainable Aviation Fuels (SAFs). From understanding feedstocks and conversion pathways to evaluating production technologies, emissions performance, and life-cycle impacts, the text equips readers with a holistic understanding of SAFs within today’s global energy landscape. Across five insightful chapters, readers will find a clear roadmap: the evolution and importance of SAFs; production methods and technological foundations; environmental

SAE International’s Dictionary of ADAS and Connected Vehicles

R-5591/20/2026

The convergence of Advanced Driver Assistance Systems (ADAS) and connected vehicle technologies is ushering in a transformative era of automotive innovation—one that is fundamentally enhancing vehicle safety, efficiency, reliability, and the overall driving experience. SAE International’s Dictionary of ADAS and Connected Vehicles stands as the definitive reference for this rapidly evolving domain, meticulously compiled to clarify and standardize the language shaping modern mobility. Inside, readers will find clear, authoritative definitions encompassing the full spectrum of technologies that enable connected and automated driving—including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) communication systems. This comprehensive resource translates complex engineering terminology into accessible language, enabling engineers, researchers, policymakers, educators, and enthusiasts to share a common technical foundation. Reflecting the latest

Electrification: Vol. 3

EPRCOMPV04202512/29/2025

Safety, ADAS, and Cybersecurity: Vol. 4

EPRCOMPV01202512/29/2025

Infrastructure Enablers for Automation: Vol. 4

EPRCOMPV02202512/29/2025

Automated Vehicles: Development, Testing, and Validation: Vol. 4

EPRCOMPV03202512/29/2025

Commercial Vehicles: Vol. 2

EPRCOMPV05202512/29/2025

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Sideswipe Impact Severity Testing and Analysis with an Instrumented Hybrid III Anthropomorphic Test Device (ATD)

2026-01-0532

4/7/2026

Vehicle-to-vehicle sideswipe collisions are unique in their impact characteristics because the vehicles typically do not reach a common velocity at impact. To better understand the characteristics and dynamics of sideswipe collisions, vehicle-to-vehicle crash testing was performed to find the relationships between variables related to the impact, such as closing speed, relative angle, and overlap depth. This paper discusses data collected for three sideswipe (oblique) impact tests conducted at a testing facility in Buffalo, New York. The tests were conducted using a passenger vehicle as the sideswiping vehicle, which impacted a stationary cargo van. The passenger vehicle was towed into the van at relative angles ranging from 8 to 15 degrees and at velocities of 5 to 20 mph. Two different (but identical) passenger cars and two cargo vans were used during the testing series. Test results were then utilized to investigate a methodology of analyzing sideswipe collisions as a combination of

Danaher, David, McDonough, Sean, Donaldson, Andrew, Neale, William, Cochran, Reece

Multi-Physics Topology Optimization and Neural Network–Based Design of Experiments for Lightweight Automotive Cast Components

2026-01-0510

4/7/2026

This paper presents a hybrid optimization framework that integrates Multi-Physics Topology Optimization (MPTO) with a Neural Network–surrogated Design of Experiments (NN-DOE) to enable lightweight structural design while satisfying crashworthiness, durability, and noise, vibration, and harshness (NVH) requirements under practical casting and packaging constraints. In the proposed MPTO formulation, crash and durability performances are incorporated through equivalent static compliance measures, while NVH performance is assessed using a frequency-domain dynamic stiffness metric, allowing consistent evaluation of trade-offs among competing design requirements. The framework is first demonstrated using a mass-produced passenger-car lower control arm (LCA) as a benchmark component. In this application, MPTO achieves weight reduction under multi-physics objectives by removing non-load-bearing material. Results show that single-discipline optimization produces unbalanced topologies, while

Kim, Hyosig, Senkowski, Andres, Gona, Kiran, Saroha, Lalit, Boraiah, Mahesh

The Effects of Collision-Related Power Loss on Toyota Safety Sense 3.0 Event Data

2026-01-0548

4/7/2026

Toyota vehicles equipped with Toyota Safety Sense (TSS) can record detailed information surrounding various driving events. Often, this data is employed in accident reconstruction to better understand the dynamics of a collision. TSS data is comprised of three main categories: Vehicle Control History (VCH), Freeze Frame Data (FFD), and image records. During an event, it is possible that a vehicle undergoes a catastrophic power loss from the damage sustained during the event. In this paper, the effects of sudden power loss on the VCH, FFD, and images are studied. Events are triggered on a TSS 3.0 equipped vehicle by driving toward a stationary target. After system activation, a total power loss is induced, triggered on the instrument cluster “BRAKE” alert message, at various delays after activation. This testing studies various signals recorded across VCH, FFD and image data including vehicle speed and time and date. Results show that there is a minimum time to record after system

Getz, Charles, Yeakley, Adam, DiSogra, Matthew

Development and Validation of Torque Vectoring and Traction Control Strategies in a FSAE Vehicle Using a Dual-Axle Dynamometer HiL Platform

2026-01-0644

4/7/2026

This study presents the vehicle control optimization of a Formula SAE (FSAE) electric vehicle developed by National Taiwan University Racing Team (NTU Racing), utilizing a dual-axle dynamometer and a real-time Hardware-in-the-Loop platform from Chroma. The novelty of this work lies in the comprehensive system-level validation of independent torque control strategies, namely Torque Vectoring (TV) and Traction Control (TC), implemented directly within the vehicle control unit (VCU), and the high-fidelity simulation of dynamic driving scenarios based on the FSAE circuit. The vehicle features an independently controlled rear-axle, two-wheel drive (2WD) configuration, consisting of two in-wheel motors, self-developed inverters, and planetary gearboxes. During testing, a pre-built CarSim driver model provides throttle, brake, and steering inputs to the VCU via Controller Area Network (CAN) interface. The VCU, in turn, computes the independent torque commands according to the TV and TC

Hsiao, Tsung-Yu, Chen, Zhi-Ren, Jian, Rong-Wei, Chen, Tai-Hsiang, Wang, Tai-Jie, Hu, Wei-Zhe, Ho, Hui-Ting, Wu, Ting-Yu, Lin, Ting-He, Chiu, Joseph

Forensic Acquisition and Analysis of Unsupported Lexus Infotainment Modules via CMD42 Bypass and Data Carving

2026-01-0540

4/7/2026

Crashes involving passenger vehicles increasingly include vehicles equipped with infotainment systems that are unsupported by commercial vehicle system forensics hardware and software. Examiners facing these systems must overcome challenges in acquiring and analyzing user data, requiring an understanding of both digital forensics principles and the proprietary characteristics of the modules. This paper presents a methodology for acquiring data from previously unsupported Lexus infotainment modules, including techniques to bypass CMD42 security locks on SD cards and extract data. Once acquired, the paper outlines methods for analyzing user data through data carving techniques, enabling recovery of information from binary images even when the full file system cannot be reconstructed. Emphasis is placed on maintaining the integrity of the evidence and validating findings through controlled testing. These validation procedures ensure that the recovered information is both accurate and

Burgess, Shanon

Simulation Study on a Neural Tire Model Based on Grouped Feature Learning

2026-01-0645

4/7/2026

The tire model is a crucial component in the design of the K-characteristic of FSAE racing car suspensions, and directly influences the achievement of maximum cornering lateral force. Not only do the slip angle, vertical load, tire pressure, and camber angle affect the mechanical characteristics of the tire, but temperature is also an important influencing factor when FSAE vehicle tires operate at high speeds. However, the modeling process of traditional tire models based on temperature characteristics is often very complex. The FSAE tire test code (FSAE TTC) already has a large amount of official sample data, which provides a basis for data-driven neural network models. This study implemented a hybrid modeling methodology, constructing two cascaded feedforward neural networks that combine the physical interpretability of the Magic Formula tire model with the nonlinear approximation capabilities of neural networks. The first network model uses slip angle, vertical load, tire pressure

Liu, Xiyuan, Wang, Shenyao, Li, Mingyuan, Huang, Jiayu

Study of the Vortex Flow over a Generic Open-Wheel Race-Car

2026-01-0649

4/7/2026

Open wheel race cars present a challenge to the aerodynamic designer because of the numerous wakes and vortices created by the various body components. The present study follows the development of a high-downforce race car and investigates possible vortex manipulations to increase its aerodynamic efficiency. The tools used for this study involved computational fluid dynamics and small-scale wind tunnel testing. Once the basic geometry of the racecar was finalized, cost effective measures were tested to improve its downforce to drag ratio. As an example, by fine tuning the position of different body components, such as the rear wing location relative to the underfloor diffuser exit, vehicle’s aerodynamic performance can be modified. The results of both the wind tunnel and the computational investigations indicated that such simple modifications can positively improve the race-car downforce to drag ratio. Also, once the baseline vehicle’s geometry was frozen and observing that the

Okpysh, Christian, Katz, Joseph, Shute, Robin

Parametric Aerodynamic Design and Development of a Formula 1 2026 Front Wing with Wind Tunnel Validation

2026-01-0646

4/7/2026

The front wing of a Formula 1 car is one of the most important aerodynamic components in design development. Particularly, as it is the first to interact with the upcoming airflow, the aerodynamic flow structures generated will have a strong interaction with the remainder of the car’s components. In 2026, the Fédération Internationale de l’Automobile will introduce new regulations that incorporate new aerodynamic philosophies for the front wing, including active aerodynamics. This paper presents a design methodology study for the development of a Formula 1 2026 front wing, compliant with Issue 9 of the technical regulations. A computational-based, structured optimisation series was conducted to enhance the aerodynamic performance of a front wing concept with a focus on improving downforce, maximising efficiency, and enhancing trailing flow for the remainder of the car. The final front wing concept at 40%, running at 30 m/s, generated 189 N of downforce and 19 N of drag. Active

Jacoulot, Santiago, Soares, Renan F., Marshall, David W.

The Development of an Experimental Methodology for the Measurement of Aeroelastic Components

2026-01-0648

4/7/2026

In high-end motorsport engineering, aerodynamic devices such as front and rear wings are prone to aeroelastic deformations under certain conditions, which can be exploited for vehicle performance gains. Considering the complex interactions between the aerodynamics and structures, experimental evaluation can prove to be a time-effective approach for design, optimisation, research and development regarding aeroelastic bodies. This study presents the development and experimental validation of a deformation tracking system using depth-sensing LiDAR (Light Detection and Ranging) camera technology. The system is based on the use of reflective markers mounted on a given model of interest; this project, a front wing model with a flexible, 3D printed flap element was used as a benchmark. Surface deformation is captured by post-processing point cloud data to extract three-dimensional displacement vectors. A series of controlled measurement tests were first conducted to assess accuracy and

Altinbas, Koray, Soares, Renan F.

Reconstruction of Pedestrian-Vehicle Collisions Using 3D Skeletal Estimation from Vehicle-Mounted Camera Video

2026-01-0575

4/7/2026

Pedestrian fatalities in traffic accidents continue to rise, with severe injuries often resulting from both vehicle impact and subsequent ground contact, frequently occurring outside the field of view of vehicle-mounted cameras. This study presents a proof-of-concept (PoC) approach for reconstructing three-dimensional pedestrian motion—including occluded regions—using dashcam video. The method integrates 2D human pose estimation (MMPose) and monocular depth estimation (Depth Anything V2),the latter was fine-tuned on a custom dataset, to generate 3D skeletal coordinates.To evaluate motion matching, the reconstructed pedestrian poses were quantitatively compared with a database of vehicle collision simulations using the THUMS human body model and skeletal data representing real-world crash scenarios generated in PC-Crash. Composite similarity indices based on thoracic center of gravity trajectory and torso orientation vectors were employed for this comparison. Preliminary results

Onishi, Koji, Wang, Kewang, Uno, Eriko, Ichikawa, Koji, Tanase, Noboru, Ando, Takahiro

Bias and Uncertainty in the Time, Position, and Speed Data of Consumer-Grade GPS Devices

2026-01-0544

4/7/2026

The goal of this study is to quantify the accuracy (bias) and precision (uncertainty) of the time, position, and speed data acquired by a range of consumer-grade devices (4 bike computers, 5 watches, 1 application on 3 smart phones, and a camera) that access Global Positioning System (GPS) satellite signals. We acquired data at each device’s maximum sampling rate (typically 1 Hz) during 207 minutes (twelve sessions of ~17 min) over 61.6 km of road cycling. The time and position data from these devices were compared to real-time kinematic (RTK) data acquired using a differential GPS system, and speed data from these devices were compared to a high-resolution wheel speed sensor synchronized to the RTK data in order to statistically estimate the bias and 95th percentile confidence intervals of the uncertainty of the devices’ data. Overall, we found the position and speed data from the devices generally lagged the reference by 4 s or less, although the lags between the speed and position

Booth, Gabrielle R., Mitchell, Alan L., Siegmund, Gunter P.

Introduction to Catesby Aero Research Facility, CARF, the Coast-Down Tunnel Proving Ground

2026-01-0611

4/7/2026

This paper reports on the Catesby Aero Research Facility (CARF), which began commercial operation in 2019, and summarizes facility characteristics and associated measurement technologies, with an emphasis on vehicle-mounted component-force measurement devices. CARF is a proving ground converted from a former railway tunnel approximately 2.74 km in length and surfaced with high-quality tarmac. The road-surface quality was specified to be comparable to that of SUBARU's proving ground and was achieved using established construction methods. The course is approximately straight with a small longitudinal grade. Key course specifications include an approximately 40 m2 blockage area, a 6 m road width (maximum 8.4 m), flatness σ < 0.5 mm, and a gradient of 0.57%. Relative to outdoor coast-down testing, the tunnel length enables continuous measurement to very low speeds, thereby improving repeatability. A six-component force sensor integrated into the hub unit enables on-road measurement of

Shimoyama, Hiroshi

Machine Learning-Based Prediction of Wind-Induced Interior Noise in Ground Vehicles

2026-01-0599

4/7/2026

In response to increasing customer demand for enhanced passenger comfort and perceived vehicle quality, OEMs in automotive and commercial vehicles are placing significant emphasis on reducing the interior cabin noise. At highway speeds, wind noise is a primary contributor to the overall noise within the vehicle cabin. Conventional approaches to predict vehicle wind noise rely on physical testing, which can only be conducted in the later stages of the design process once a physical prototype is available. Increased adoption of established computational fluid dynamics (CFD) methods has enabled earlier assessment. However, such simulations require several hours to complete, posing a challenge in the context of rapid design iteration cycles. With the growing adoption of artificial intelligence in engineering, machine learning (ML) approaches have been proposed to predict a vehicle’s aerodynamics performance. Nevertheless, development of ML techniques in the context of aeroacoustics

Higgins, John, Fougere, Nicolas, Sondak, David, Senthooran, Sivapalan, Moron, Philippe, Jantzen, Andreas, Bi, Jing, Oancea, Victor

Wind-Tunnel Study of the Traffic-Wake Impacts for Road-Vehicle Aerodynamics

2026-01-0613

4/7/2026

When driving in traffic, the wakes of leading vehicles reduce the wind speed experienced by a following vehicle, lowering its drag relative to isolated driving. These wake effects can persist to large inter-vehicle distances, on the order of hundreds of meters, while lateral convection due to cross winds can influence vehicles in adjacent lanes. Wind tunnel testing was conducted at 30% scale for light- and heavy-duty-vehicle models in a large wind tunnel with a traffic-wake simulation system, expanding upon a previous study that examined only heavy vehicles. Three variants of the DrivAer model, four variants of the AeroSUV model, and three variants of a zero-emission heavy-duty-truck model were tested with a range of simulated wake conditions that varied the type, forward distance, and lane position of the wake-source vehicle(s), for a range of yaw angles up to 11°. Results show drag reductions of up to about 10% for the heavy-duty-truck model, and up to about 20% for the passenger

McAuliffe, Brian, Ghorbanishohrat, Faegheh, Barber, Hali

Simulation Study on Rear-Wheel Steering Control of FSAE Vehicles Based on Temporal Convolutional Network

2026-01-0650

4/7/2026

The Formula SAE (FSAE) race track is characterized by a large number of corners, making cornering performance a key factor affecting lap time. Based on the proportional control strategy for rear-wheel steering angles, this paper proposes a steering angle optimization method using a Temporal Convolutional Network (TCN). The TCN model features a faster training speed than traditional sequential neural networks. In addition, dilated convolutions enable an exponential expansion of the receptive field without increasing computational costs, making it particularly suitable for capturing the temporal dependencies of vehicle states. By processing vehicle dynamic parameters including front-wheel steering angle, vehicle speed, yaw rate and sideslip angle, the model calculates the correction value of the rear-wheel steering angle. This correction value is then superimposed with the reference value of the rear-wheel steering angle derived from the proportional control strategy, which serves as the

Liu, Xiyuan

The Stellantis North America Aero-Acoustic Wind Tunnel Upgrade

2026-01-0609

4/7/2026

The Stellantis North America Aero-Acoustic Wind Tunnel (AAWT) has been upgraded with a cutting-edge 5-belt Moving Ground Plane (MGP) system, featuring an 8.5-meter center belt and four Wheel Spinning Unit (WSU) belts with advanced coatings for durability and visibility. The expanded 9.4-meter turntable enables ±90° yaw and supports vehicles with wheelbases from 1800 mm to 4500 mm and weights up to 5000 kg, accommodating the full Stellantis North America product range. The original 2-stage boundary layer control system was retained, with new tertiary slots added for improved flow quality. A high-stiffness, six-component Horiba balance with integrated calibration weights and tractive force measurement ensures accurate and precise measurements. Facility enhancements include a 550 m2 building addition for equipment and vehicle prep, a dedicated compressor container for clean air supply, and a vehicle underbody wash booth for efficient cleaning. Commissioning confirmed that flow quality

Lounsberry, Todd, Ladouceur, Brent, Fadler, Gregory

Development of Automotive Hood Using Multi-Material Double Injection Molding – An Overview

2026-01-0476

4/7/2026

This paper presents the multidisciplinary development of a hybrid automotive hood manufactured using double-shot injection molding with overmolded brackets. Conventional steel and aluminum hoods, while structurally reliable, pose challenges in terms of weight reduction, pedestrian head protection, and manufacturing cost. Composite and thermoplastic alternatives supported by computational analysis and advanced molding processes provide opportunities to address these challenges. Finite element analysis (FEA) was employed to evaluate torsional and bending stiffness, locking load, and crashworthiness, while pedestrian headform simulations following ECE R127 and EEVC WG17 guidelines were conducted to assess compliance with safety regulations. Adhesion and bonding strength of overmolded polymer–polymer interfaces were studied to validate manufacturing feasibility. Results confirm that hybrid hoods fabricated using multi-material double-shot molding can achieve weight reductions of up to 30

Ganesan, Karthikeyan, Seok, Sang Ho, Jo, Hyoung Han

Safety-Constrained Control of Nonlinear Mobility Systems Using Reference Governor

2026-01-0033

4/7/2026

Ensuring safe operation and reliable control of mobility systems remains a significant challenge, particularly for nonlinear and high-dimensional applications subject to external disturbances with hard constraints and limited computational resources in real-time implementations. A reference governor (RG) can enforce constraints using an add-on scheme that preserves the pre-stabilizing controller while balancing the need to satisfy other requirements, including reference tracking and disturbance rejection. Thus, in this paper, we exploit RG-based strategies focusing on nonlinear mobility systems. While the method is generalizable to other applications, such as waypoint following for autonomous driving, the flight dynamics of a quadrotor system with twelve states are used as an example. We implement a disturbance rejection RG to satisfy safety constraints and track set points. To handle nonlinearity, we propose an optimal strategy to quantify the maximum deviation between the nonlinear

Dong, Yilong, Li, Huayi

A Novel Spot Joint Finite Element for Rapid Stress Analysis of Fasteners in Automotive Structures

2026-01-0182

4/7/2026

Due to the spot weld and mechanical fastener share the similar characteristics to join sheets together with differences in deformation behavior around joint region, a novel spot joint element (user-defined element) consists of regular Mindlin shell elements and equations for different kinematic constraints is proposed to simplify the spot joint representation in lightweight automotive structures. The novel spot joint element can not only provide accurate deformation behavior around joint region but also output mesh-insensitive structural stresses at virtual nodes with the use of traction-based structural stress method for fatigue failure analysis. In this investigation, the structural stress distributions around joint circumference in the lap-shear specimens with spot weld or fastener are first calculated to validate the accuracy of the novel spot joint element. Then, the structural stresses along different cross-sections emanating from joint are also calculated for the specimens with

Wu, Shengjia, Zhang, Lunyu, Dong, Pingsha

A Latent Space Framework for Modeling Transient Engine Emissions Using Joint Embedding Predictive Architectures

2026-01-0423

4/7/2026

Accurately modeling and controlling vehicle exhaust emissions, particularly during highly transient events such as rapid acceleration, is crucial for meeting stringent environmental regulations and optimizing modern powertrain systems. While conventional data-driven modeling methods, such as Multilayer Perceptrons (MLPs) and Long Short-Term Memory (LSTM) networks, have improved upon earlier phenomenological or physics-based models, they often struggle to capture the complex nonlinear dynamics of emission formation. These monolithic architectures attempt to learn from all available data, which increases their sensitivity to dataset variability. They often require increasingly deep and complex architectures to improve performance, thereby limiting their practical utility. This paper introduces a novel approach that overcomes these limitations by modeling emission dynamics in a structured latent space. Using a rich dataset combining real-world driving data from a Portable Emission

Sundaram, Ganesh, Gehra, Tobias, Ulmen, Jonas, Heubaum, Mirjan, Görges, Daniel, Günthner, Michael

Residual “Non-Zero” Speedometer Readings in Crash Tests Using a HYGE ^TM Crash Simulation System

2026-01-0537

4/7/2026

The design trend among analog speedometer and tachometer instruments in recent decades has been toward stepper motor drives. If power is interrupted during a traffic crash, such gauges often do not return to a zero reading. Speedometers and tachometers displaying residual readings after a crash have been observed with increasing frequency in recent years. In conducting a crash reconstruction, a question often arises as to whether such a residual reading corresponds to the indicated vehicle speed at the time of impact in the crash. Prior publications in this area have included a variety of crash tests under a wide range of relatively uncontrolled conditions. The present investigation evaluated a total of nine instrument clusters with a range of static torque required to move the needles when unpowered. The clusters were mounted on a HYGETM crash simulation sled and subjected to consistent impulses at orientations representing frontal, rear, left and right lateral, and left and right

Walker, James, Duran, Amanda, Kent, Steven, Barnes, Daniel, Osterhout, Aaron, Clayton, Aidan

Effects of Obstacle Height and Vehicle Roll Behavior on Electric Vehicle Side Impact Safety

2026-01-0405

4/7/2026

Electric vehicles (EVs) face unique safety challenges under pole side impact conditions, largely due to the presence of floor-mounted battery packs. Existing regulatory test procedures, such as FMVSS 214, primarily address occupant injury using full-height cylindrical obstacles. These procedures were originally developed for internal combustion vehicles (ICVs). However, real-world roadside crashes frequently involve obstacles of varying heights, such as guardrails, curbs, and median bases. While these obstacles pose limited risk to the passenger compartment, they can intrude into the battery pack and trigger thermal runaway. This study investigates the influence of obstacle height on EV pole side impacts. Finite element simulations of a commercially available sedan were conducted against rigid obstacles of different heights. Results reveal a non-monotonic trend of battery intrusion governed by the interplay between rollover dynamics and structural stiffness. Theoretical analyses were

Ma, Chenghao, Xing, Bobin, Zhou, Qing, Xia, Yong

Use Inertial Sliding of Reclined Seat to Enhance Occupant Retention under Rear-End Crash

2026-01-0563

4/7/2026

Autonomous vehicles may attract more passengers to recline their seat for comfort. However, under severe rear-end crashes and large reclining angle, the backward inertia could completely throw occupant out of seat. Even if the occupant body can be restrained by seatbelt, the occupant’s head could slide out of the head restraint area. Any of these situations may cause severe injuries. To address this safety concern, we developed a sliding seat system designed to enhance occupant retention. Activated by impact inertia of rear-end collision, the system allows the seat sliding backward along its track in a controlled manner, and the sliding stroke is accompanied by a restraint force and absorbs some amount of kinetic energy during the sliding. Thus, occupant retention can be enhanced, and injury risks of head and neck can be reduced. To demonstrate this concept, we built a MADYMO model and conducted a parametric analysis. The model includes a 50th percentile human model, a vehicle seat

Dai, Rui, Zhou, Qing, Puyuan, Tan, Shen, Wenxuan

SAE TOMORROW TODAY - How Kansas is Reshaping Multimodal Transportation

135624/7/2026

What does it take to build a multimodal transportation network that actually works for rural communities, growing regions, and everyone in between? In Kansas, that question is critical as the Kansas City Chiefs plan their stadium move and the 2026 FIFA World Cup comes to town. Listen in as we sit down with Matt Messina, Chief of Multimodal Transportation at the Kansas Department of Transportation (KDOT), to explore how Kansas is prioritizing transit solutions for upcoming projects and how community input shapes decisions. It's an insightful journey into the challenges and opportunities of public transit, pedestrian infrastructure, and the future of mobility. 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

Patterson, Lori

Multi-Objective Reinforcement Learning Framework for Transmission Shift Schedule Optimization

2026-01-0162

4/7/2026

Building upon previous work that successfully employed a Reinforcement Learning (RL) agent for the autonomous optimization of transmission shift programs to enhance fuel efficiency, this paper addresses a critical limitation of that approach: the neglect of human-centric factors. While the prior methodology achieved substantial fuel consumption reductions by training an RL agent in a Software-in-the-Loop (SiL) environment, it did not explicitly account for aspects such as driver comfort and preferences, which are paramount for real-world user acceptance and drivability. This work presents a multi-objective optimization framework extending the artificial calibrator to simultaneously maximize fuel efficiency and enhance driver comfort. The method introduces a modified RL reward function that penalizes undesirable shift behavior to ensure a smooth driving experience (drivability). This new methodology also incorporates a mechanism to capture and integrate driver preferences, moving beyond

Kengne Dzegou, Thierry Junior, Schober, Florian, Rebesberger, Ron, Henze, Roman, Sturm, Axel

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