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Vertical Flight Society Technical Papers - Coming Soon to Mobilus

Vertical Flight Society papers coming soon to SAE Mobilus. In partnership with the Vertical Flight Society (VFS), SAE Mobilus will be hosting more than 17,000 technical papers and proceedings from VFS. Click here to see a sample of the content covering decades of rotorcraft and vertical flight technologies.

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

Browse All 177

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 advanced air mobility and drone industry further enable the development of modern, safe, and practical WIG platforms. Click here to access the full SAE EDGETM Research Report portfolio.

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 mitigation of algorithmic biases. Click here to access the full SAE EDGETM Research Report portfolio.

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 actionable solutions to help stakeholders better navigate the transition toward a circular future for mobility. It further highlights the overlooked complexities in the Global South, emphasizing the importance of ethical market expansion and localized mobility strategies. Click here to access the full SAE EDGETM Research Report portfolio.

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 suggestions for future research will also be discussed. Click here to access the full SAE EDGETM Research Report portfolio.

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 track, as well as the economics of moving capital intensive automation systems toward a service-provision business model. Click here to access the full SAE EDGETM Research Report portfolio.

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 Control for Autonomous Vehicles presents a hybrid framework that combines data-driven insights with the robustness of physical models. It identifies key challenges in fusing these disparate methods and outlines potential solutions in developing robust fusion strategies, establishing generalized mixed dynamics models, and designing multi-objective robust control systems. In addition, the report explores future research directions to enhance learning efficiency, improve adaptability to rare but critical scenarios, and ultimately pave the way for secure, efficient, and human-like autonomous driving systems. Click here to access the full SAE EDGETM Research Report portfolio.

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 redefined what it means to be a "product." Click here to access the full SAE EDGETM Research Report portfolio.

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 intelligence (AI) is instrumental in processing such data, and in that context, “edge AI” is a more recent type of implementation. Edge AI involves AI algorithms in edge computing devices, which requires hardware operating close to where data is generated. This report explores the potential of edge AI in CCAM. Different perspectives on edge AI for CCAM are explored and definitions drafted. Primary applications are explored, and an outlook on further advancements in applications is presented. The report includes a discussion on the benefits, risks, and challenges related to the use of edge AI in this domain. Major issues such as privacy and cybersecurity are considered, as are misconceptions. Furthermore, potential learning benefits, using experiences gained in other sectors, are introduced. NOTE: SAE Edge Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. These reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.

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 educational programs in leveraging AI for transportation applications are emphasized in this report. Click here to access the full SAE EDGETM Research Report portfolio.

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

Transportation Cybersecurity and Privacy

Connected and Automated Vehicles

Vehicle Dynamics, Stability, and NVH

Transportation Safety

Alternative Powertrains

Commercial Vehicles

Passenger Cars - Electronic and Electrical Systems

Passenger Cars - Mechanical Systems

Fuels and Lubricants

Materials and Manufacturing

Engines

Recent Books

Browse All 633

Ceramic Matrix Composites

R-4608/27/2026

The fifth volume of this compendium publishes technical guidelines and properties of ceramic matrix composite material systems. The content includes information on material selection, processing, characterization, testing, data reduction, design, analysis, quality control, application, case histories, and lessons learned of typical ceramic matrix composite materials. Initial Release of Volume 5 superseded Mil-HDBK-17-5 dated June 17, 2002. The Composite Materials Handbook, referred to as CMH-17, is an engineering reference tool that provides comprehensive, practical engineering guidelines that have proven reliable for the design, fabrication, characterization, test, and maintenance of polymer matrix, metal matrix, ceramic matrix, structural sandwich, and non-metallic AM materials. CMH-17 provides information and guidelines necessary to design and fabricate end items from composite materials. The compendium includes reliable data, linked to control of processes and raw materials, to provide material property basis values and design information that promotes the safe use of ceramic matrix composites in aerospace applications. CMH-17 provides information and guidelines necessary to design, fabricate, and maintain components from composite and non-metallic additively manufactured (AM) materials. In support of this purpose, the handbook constitutes an overview of the field of advanced materials technology and engineering. It is used by engineers worldwide in designing and fabricating products made from composite materials.

July 2026 AMS Index

AMSJULY-20267/22/2026

Metallic Materials Properties Development and Standardization (MMPDS) Handbook - 2026 - Volume I

B-994_VOLUME17/1/2026

The Metallic Materials Properties Development and Standardization MMPDS-2026 supersedes MMPDS-25 and prior editions of the MMPDS Handbook as well as all editions of MIL-HDBK-5, Metallic Materials and Elements for Aerospace Vehicle Structures Handbook that were maintained by the U.S. Air Force. The last edition, MIL-HDBK-5J was cancelled by the U.S. Air Force in March 2006. This document contains design information on the mechanical and physical properties of metallic materials and joints commonly used in aircraft and aerospace vehicle structures. All information contained in this Handbook has been reviewed and approved using a standardized process. The development and ongoing maintenance process involves certifying agencies, including the FAA, DoD, and NASA, and major material suppliers and material users worldwide. The information and procedures in this Handbook are continuously reviewed and modified or removed as determined to be appropriate. With advances in materials and fastener systems, and with the review process of existing information, periodic updates of the MMPDS should be expected. As such, it is recommended that the latest version of the MMPDS be used. Volume II: Process Intensive Materials and Joining Technologies.

Metallic Materials Properties Development and Standardization (MMPDS) Handbook - 2026 - Volume II

B-994_VOLUME27/1/2026

Volume II publishes the framework for generating and analyzing the data so that material sponsors can generate the right data, knowing how it will be analyzed by the Battelle Memorial Institute. MMPDS-2026 Volume II Process Intensive Materials and Joining Technologies does not currently have any material properties, focusing on additive manufacturing of metallic materials. MMPDS-2026 Volume II focuses on non-conventional processes and will be published each year. Volume I: Conventional Materials and Joint Allowables.

1D and MultiD Modeling Techniques for IC Engine Simulation Second Edition

R-5805/29/2026

The internal combustion (IC) engine remains the most widely used thermal machine in the world and will continue to play a central role in global mobility for decades to come. Even as alternative powertrains expand, optimized IC engines—often integrated within hybrid and electrified architectures—are essential to achieving low fuel consumption, near-zero pollutant emissions, and reduced carbon dioxide output. Advances in combustion strategies, synthetic fuels, turbocharging, and aftertreatment systems are driving this evolution. This fully revised and expanded second edition reflects the rapid progress made in engine modeling and simulation over the past two decades. One-dimensional (1D), three-dimensional (3D), and coupled 1D–3D modeling techniques have become indispensable tools in modern engine design, enabling engineers to evaluate performance, efficiency, and emissions through advanced virtual engine environments. Bringing together contributions from leading international researchers and industry experts, this book presents the most significant and recent developments in IC engine simulation. Topics include 0D, 1D, quasi-3D, and full 3D modeling approaches; turbocharging and aftertreatment systems; co-simulation and real-time applications; hydrogen and two-stroke engines; and advanced thermal modeling techniques. Designed for engineers, researchers, and graduate students, this second edition serves as a comprehensive reference for understanding, comparing, and applying state-of-the-art IC engine modeling methodologies in an evolving mobility landscape. Chapter topics include: • state of the art of 1D thermo-fluid dynamic simulation models • virtual engine development: 1D- and 3D-CFD up to full engine simulation • advanced 0D and quasiD thermodynamic combustion models for SI and CI engines • compressor and turbine (turbocharger, supercharger, or turbocompounding device) as boundary conditions for 1D simulations • 3D-CFD combustion models for SI and CI engines • model order reduction for real-time simulation and control applications • modeling of EGR systems •1D engine model in XiL application-a simulation environment for the entire powertrain development process • coupling of 1D and 3D fluid dynamic models for hybrid simulations • extending the 1D approach to the simulation of 3D components: the quasi-3D approach • 1D simulation models for aftertreatment components• 3D simulation models for after-treatment systems • modeling of IC engine silencers and tailpipe noise: 1D and 3D approaches • hydrogen-fueled internal combustion engines • optimization of the gas exchange process in advanced 2-stroke engines • IC engine 0D/1D thermal modeling

Variable Compression Systems for Future Engines and Fuels

R-5534/30/2026

Variable Compression Engines: Enabling Net Zero explores variable compression ratio (VCR): one of the most promising—and historically elusive—advancements in internal combustion engine (ICE) technology. Long recognized for its thermodynamic benefits, VCR has challenged engineers for decades due to its mechanical complexity. Today, as the global mobility landscape demands ever higher efficiency, lower emissions, and greater fuel flexibility, VCR has emerged as a viable and transformative solution. This book delivers a comprehensive and authoritative examination of VCR technology, quantifying its efficiency and CO2-reduction potential while surveying the wide range of mechanisms conceived, developed, and tested over more than a century of engine innovation. By combining historical insight with modern analysis, the authors reveal the remarkable ingenuity of past and present engine designers—and demonstrate that VCR is not only feasible, but increasingly essential. Positioned squarely within the context of global decarbonization, the book argues for a realistic, inclusive pathway to net-zero emissions—one in which ICEs continue to play a critical role. VCR technology enables higher efficiency across almost all operating conditions, supports advanced combustion strategies, and allows engines to operate effectively on a broad spectrum of low- and zero-carbon fuels, from biofuels to synthetic e-fuels. With VCR now in high-volume production and poised for broader adoption across automotive, heavy-duty, and marine applications, this timely volume is an essential resource for OEMs, suppliers, policymakers, researchers, and students seeking practical, scalable solutions for a sustainable energy future. Chapter topics include: • compression ratios and fuels • compression ratio limits • cylinder head VCR • cylinder block VCR • connecting rod variable compression • piston VCR • cranktrain and linkages VCR • modulated crankshaft eccentricity VCR • axial or barrel engine VCRs • high power and downsized engines • impact of VCR systems on engines • VCR applications for the future • variable compression for future engines and fuels • VCR cost–benefit analysis

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 include: • Holistic reliability across design, manufacturing, supply chain, and marketing. • Advanced experimental designs, including split-plot and fractional factorial methods. • Fault tree analysis (FTA) and FMEA for failure prediction and prevention. • Supply chain optimization through multivariate process control. • Production and field reliability using design for testability and diagnostics. • Electric vehicle system and battery reliability analysis. • Marketing reliability driven by voice of customer and data-based value analysis. This book is an essential resource for engineers, operations leaders, and technical managers who want to build resilient systems, unlock innovation, and achieve long-term competitive advantage through disciplined, data-driven reliability.

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 performance and emissions reduction potential; safety, certification, and market integration; and future projections supported by international initiatives and regulatory frameworks. By combining technical depth with practical relevance, Fundamentals of Sustainable Aviation Fuels serves as both a foundational reference and a forward-looking guide for researchers, policymakers, industry professionals, and students. It underscores that achieving sustainability in aviation depends not only on innovation but also on global collaboration, paving the way toward cleaner skies and a resilient, low-carbon future for air travel.

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 global standards, research, and innovations, this dictionary bridges the gap between theory and application. It fosters interdisciplinary collaboration, supports safer system design, and provides clarity for those engaged in regulatory development or technology deployment. As the pace of mobility innovation accelerates, precise and accessible communication becomes indispensable. SAE International remains committed to advancing global transportation knowledge—empowering professionals to navigate, contribute to, and shape the future of intelligent, connected, and sustainable mobility with confidence and clarity.

Recently Published

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Design and Implementation of an Automatic Tractor-Container Binding Identification System Based on Multi-Source Perception Fusion

2026-99-2034

11/13/2026

To address the tractor-container binding problem in drop-and-pull transportation scenarios, this paper proposes an automatic tractor-container binding identification method based on multi-source perception fusion and implements a corresponding application system. The system integrates RFID, Bluetooth, and container positioning devices to determine whether a tractor and a container are bound, enabling automatic identification and dynamic management of binding relationships throughout the entire transportation process. In terms of identification strategy, a hierarchical decision-making and fusion verification mechanism is adopted. Through cross-validation of multi-source data, the reliability of identification results is significantly improved. When the reader fails to return data or is offline, the system can perform identification based on the trajectories of tractors and containers, thereby ensuring continuity and stability of the identification process. This approach effectively reduces the impact of complex environmental factors and wireless network fluctuations on identification results, improves binding identification accuracy, and enhances logistics transportation efficiency. A one-month large-scale test was conducted in real-world drop-and-pull transportation scenarios. The system achieved a binding record accuracy rate of 98.04%, according to the test, demonstrating the system’s feasibility and usefulness in actual engineering applications. The system offers dependable technical assistance for sophisticated, data-driven drop-and-pull transportation management.

Deng, Shiwei, Luo, Sen, Zhang, Xiang, Hu, Junjie, Wang, Zhi

Functional Resilience Assessment of Urban Rail Transit Stations from a Network Perspective--A Case Study of Shenzhen

2026-99-2004

11/13/2026

This study explores challenges in maintaining operational resilience for urban rail transit networks during emergencies. It develops a comprehensive evaluation system based on resilience triangle theory, covering pre-event, during-event, and post-event stages to assess station functional resilience across the full event cycle. By integrating complex network theory with dynamic passenger flow simulation, the research analyzes transportation system topological features and passenger adaptive behaviors during incidents. Key methodological innovations include a dual-constraint feasible path criterion that combines time flexibility thresholds and transfer tolerance parameters to evaluate passenger preferences for efficiency and comfort simultaneously, and a spatiotemporally extended recovery efficiency model using time-varying passenger load factors to overcome limitations of traditional static assessments. Empirical analysis of Shenzhen’s rail transit network verifies the framework’s ability to accurately identify and quantify the resilience performance of critical vulnerable nodes. Findings reveal terminal stations like Buji and Mucunwan reduce the largest connected subnetwork ratio to approximately 90%, significantly degrading overall network connectivity; over 85% of passengers at stations within Futian District’s central loop can switch routes, indicating high structural redundancy; and during morning peak hours, stations like Wuhe and Minzhi exhibit recovery efficiencies below 1% with recovery times exceeding 120 minutes, demonstrating substantial spatiotemporal resilience differences. The research provides a quantifiable, stage-specific, and behavior-integrated evaluation tool for rail transit resilience assessment, offering theoretical and practical insights for enhancing resilience evaluation systems through network-passenger interaction analysis.

Kang, Zhiping, Peng, Wenxiang, Xue, Zhaojie, Zhang, Ying, Wang, Guocong

Adaptation Analysis of 643-t-Class Modular Roll-On/Roll-Off Transportation Equipment Based on SPMT for High-Temperature Gas-Cooled Reactors

2026-99-2136

11/13/2026

Modular construction is crucial for the development of fourth-generation nuclear technology, particularly high-temperature gas-cooled reactors (HTGRs). Owing to its heavy weight, complex center of gravity, and sensitivity to dynamic disturbances during transport, the 643-t core module presents a major safety and accuracy challenge for roll-on/roll-off (Ro-Ro) transportation. Traditional static self-propelled modular transporter (SPMT) load-distribution algorithms are not adequate for the transportation of nuclear power modules because of dynamic response lag. Accordingly, a dynamic load adaptive balancing (DALEA) algorithm is proposed, integrating dynamic center-of-gravity prediction and hydraulic-suspension adaptation into a closed-loop control system. A 0.01 s sampling interval is used in the control model, while the co-simulation results show convergence within 0.08-0.09 s and a steady-state load-balance error within +/-2.5%. Using MATLAB/Simulink and ANSYS co-simulation platforms, DALEA, genetic algorithm (GA), static load allocation (SLA), and particle swarm optimization (PSO) were compared. Under uniform Ro-Ro operation, DALEA achieves a load-balancing error of 1.8%, corresponding to a 41.9% reduction relative to GA (3.1%), and reduces convergence time by 61.9% relative to GA (0.21 s to 0.08 s). The maximum platform deflection is 2.3 mm, the initial hydraulic-pressure deviation is 3.2% and then stabilizes within approximately 2.1%, the tire ground-pressure deviation is <=8%, and the total supported-load fluctuation is <=2.1%. Under the co-simulation conditions considered in this study, the 66-axis SPMT adaptive scheme indicates feasibility for 643-t module Ro-Ro transportation and provides a technical reference for modular HTGR transport.

Wang, Zhe, Qin, Yujie

Structural Optimization of the Air Intake Duct in Fuel Cell Vehicles Considering Transmission Loss and Pressure Drop

2026-99-2151

11/13/2026

To improve the acoustic performance of the intake system in fuel cell vehicles while maintaining airflow efficiency, this study investigates the intake duct using numerical simulation methods. A comprehensive evaluation of acoustic transmission loss and pressure drop characteristics is conducted. Based on the original structure, an improved design incorporating a perforated inner tube and a sound-absorbing layer is proposed. The pressure drop is calculated using computational fluid dynamics (CFD), while the acoustic transmission loss is analyzed using the finite element method (FEM). The results show that the improved intake duct significantly enhances transmission loss within the frequency range of 20–2,000 Hz, achieving effective broadband noise attenuation, particularly in the mid-to high-frequency range. Although the pressure drop increases slightly compared with the original structure, it remains within an acceptable engineering range and has no significant adverse effect on airflow performance. Overall, the proposed design achieves a good balance between noise reduction and flow efficiency, providing a useful reference for the structural optimization of fuel cell vehicle intake systems.

Wang, Wanjia, Li, Qizhong

Temporal Stability Analysis of the Factors Affecting Freeway Crash Injury Severity Based on a Generalized Ordered Logit Model

2026-99-2133

11/13/2026

This study investigates the determinants of freeway crash injury severity and examines whether their effects remain stable over time. Using crash records collected on the Guangzhou-Shenzhen Coastal Freeway in China from 2014 to 2019, we estimate generalized ordered logit models that explicitly consider the ordinal structure of injury outcomes. Two groups of likelihood-ratio tests are then applied to evaluate temporal transferability among yearly models. The estimation and marginal-effect results show clear time-varying influences rather than stable relationships. Variables associated with vehicle category, crash pattern, season, time of day, horizontal alignment, grade, and bridge segments display noticeable changes in their effects across years. These findings suggest that temporal instability should be incorporated into freeway injury-severity analysis so that transportation agencies can avoid drawing biased conclusions from pooled data.

Liu, Shaoxin, Yu, Wenjing, Lin, Dingzong, Zeng, Qiang, Wang, Xiaofei

Hybrid-Powered Tracked Vehicle Integrated Energy Management Strategy

2026-99-2123

11/13/2026

To address the energy management strategy of hybrid tracked vehicles, and in order to achieve a more rational power distribution among the engine-generator set and the power battery, thereby improving overall fuel economy and system operating efficiency, this paper proposes an integrated energy management control strategy based on a rule-based strategy and deep Q-networks (DQN). Considering the structural characteristics and operating features of the series-hybrid tracked vehicle power system, a relatively comprehensive vehicle dynamics and energy management simulation model is established on the MATLAB/Simulink platform. We introduce a deep reinforcement learning method. The agent’s observation inputs are the vehicle’s operating state and power demand, whereas fuel consumption, battery limits, and various system operating restrictions are folded into the optimization objective. By continuously interacting with the model environment, the agent can continuously learn energy allocation strategies for the powertrain. After training is completed, the trained agent is tested under specific operating conditions, and the proposed integrated control strategy is compared with traditional constant-voltage control and a multi-point speed control strategy. Simulation results show that, under the same test conditions, the integrated control method can optimally adjust engine speed, thereby reducing the number of operating points in the low-efficiency range. While ensuring vehicle dynamic performance and system stability, this strategy can reduce fuel consumption by at least 3.66%, thereby demonstrating its feasibility and effectiveness in energy management for series-hybrid tracked vehicles.

Yan, Zhipeng, Shi, Feina, Zhao, Zhiying, Niu, Liye, Geng, Jiangbo

Neural Network-Based Rollover Early Warning for Multi-Axle Special Vehicles

2026-99-2153

11/13/2026

To address the issues that multi-axle special vehicles are prone to rollover accidents during high-speed maneuvers due to their high load capacity and elevated center of mass (CoM), and that the traditional Time-To-Rollover (TTR) rollover early warning algorithm based on state estimation suffers from misjudgment and false alarm problems, this study takes a specific five-axle special vehicle as the research object to investigate rollover early warning algorithms. First, a vehicle dynamic rollover index accounting for unsprung mass was established, the index was validated via multi-condition simulations, and a rollover early warning threshold of 0.8 was determined in conjunction with the dynamic characteristics of multi-axle special vehicles. Subsequently, integrating vehicle state estimation results, a TTR rollover early warning algorithm based on state estimation was developed, and its early warning performance as well as inherent limitations were verified via simulations. Finally, a neural network-corrected TTR rollover early warning algorithm (NN-C-TTR) was proposed, the overall architecture of the algorithm and the neural network module were designed, and the algorithm was validated through both simulation tests and real-vehicle tests. The results demonstrate that the proposed algorithm effectively addresses the misjudgment and false alarm issues of the traditional TTR early warning algorithm, achieves accurate advance warning of rollover risks for multi-axle special vehicles, and exhibits outstanding reliability and engineering practicality.

Yin, Yongqi, Liu, Qiang, Yang, Yuxue, Zhu, Chengyong, Jiang, Xinhao, Zhang, Chunchao, Zhang, Zhengyi, Shu, Hongbin

Numerical Simulation of Green Water on Fixed Structures Based on OpenFOAM

2026-99-2135

11/13/2026

To analyze wave loads on fixed offshore structures during green water events, this study employs the open-source CFD software OpenFOAM to conduct numerical simulations of green water on fixed structural decks. A numerical wave tank is first established by developing wave generation models based on both the piston-type wavemaker method and the velocity-inlet method, and by implementing a damping wave-absorption model through modification of the two-phase flow solver interFoam. On this basis, an overset grid technique is adopted to represent the fixed structure, enabling the simulation of green water on the fixed structure. The results show that the second-order Stokes waves generated by both wave-making methods converge with mesh refinement, with the velocity-inlet method exhibiting higher accuracy. Under second-order Stokes wave action, the simulated wave loads on the fixed structure agree well with analytical solutions. The present work provides a reliable numerical approach for safety design and load evaluation of marine structures.

Bai, Chunpu, Sun, Xiaofeng

Influence of Base Course Moisture Content on Asphalt Pavement Performance in Cold Regions Based on Grey Relational Analysis

2026-99-2125

11/13/2026

To explore the influence of base course moisture content (BCMC) on asphalt pavement performance in cold regions, the correlation between measured pavement performance indexes and BCMC was explored by using grey relational analysis (GRA). First of all, the test section BCMC and service performance indicators were detected and collected by embedded data detection equipment and field investigation methods. Secondly, the evolution relationship of BCMC and service performance indexes (transverse crack index, rutting index, resilient deflection index, and base compressive stress index) for asphalt pavement was analyzed. Finally, the GRA was applied to study the correlation between BCMC and service performance indexes in a cold region, and the influence of BCMC on asphalt pavement performance was revealed. It was found that different asphalt pavement types have different BCMC and service performance indexes. The correlation between transverse crack index and BCMC is higher, the relationship of stress diffusion angle, rutting index, and BCMC is second, and the correlation between deflection index and BCMC is poor. The results reveal the influence of BCMC on service performance indexes and provide a certain engineering basis for improving asphalt pavement performance in cold regions.

Zhang, Sufeng, Diao, Wanmin, Sun, Cheng, Zhao, Zhenguo, Yu, Tengjiang

Research on Charging Strategies for Electric Logistics Vehicles Considering Time-of-Use Electricity Prices

2026-99-2120

11/13/2026

Against the backdrop of the “dual-carbon” goals and the rapid development of urban cold chain logistics, electric logistics vehicles have been widely deployed due to their advantages of low emissions and low operating costs. However, their operational performance is still constrained by limited driving range, uncertain charging behavior, and fluctuations in time-of-use electricity prices. Charging strategy selection plays a critical role in determining distribution cost and system efficiency. To address the charging decision-making problem of electric logistics vehicles under time-of-use electricity pricing, this paper develops an optimization model with the objective of minimizing total charging cost. The model incorporates practical operational constraints, including vehicle load capacity, state of charge safety thresholds, and customer time windows. A genetic algorithm is employed to solve the model, and case studies based on the Solomon dataset are conducted to compare distribution routes and charging costs under partial charging and full charging strategies. The results show that the partial charging strategy enables flexible “on-demand charging”, effectively reducing charging volume and charging time, thereby lowering total charging cost. In contrast, the full charging strategy improves driving range reliability and ensures delivery continuity. The findings provide decision support for logistics enterprises to select appropriate charging strategies under time-of-use electricity pricing.

Zheng, Chaonan, Yan, Jun, Zhang, Wanxin, Hou, Zesong, Li, Bin

Research on Multi-Train Cooperative Energy-Saving Optimization Based on Regenerative Braking Energy Recovery

2026-99-2134

11/13/2026

With the acceleration of urbanization, passenger traffic on urban rail transit has proliferated rapidly, emerging as a significant source of urban energy consumption and impacting the efficient operation and sustainable development of urban rail transit systems. In the field of multi-train cooperative energy-saving operation for urban rail transit, previous research has faced two main challenges: First, studies often indirectly represent regenerative braking energy recovery rate through the overlap time of “traction-braking” phases, neglecting to account for intuitively demonstrating energy-saving effects. Second, research has predominantly centered on mathematical formulation and algorithmic development, neglecting practical engineering constraints and failing to adequately assess the viability of implementing operational plans in real-world scenarios. To bridge these identified gaps, this study fully integrates the characteristics of power supply sections with the spatiotemporal coupling of train operations. It analyzes the interaction scenarios of regenerative braking energy among all up- and down-bound trains within the same power supply zone that influence each other. Employing the spatiotemporal network modeling method, a three-dimensional space-time-speed grid network framework is constructed. The global train operation time and state are discretized, and the objective function is formulated to minimize the net energy consumption of train operations. A mixed-integer programming (MIP) model is developed subject to various constraints such as departure intervals, stop durations, safety tracking, and additional operational constraints. The resulting solution is an energy-optimized urban rail train timetable and operational diagram. Numerical experiments are conducted in engineering scenarios to verify the efficacy of this approach, thereby establishing a generalized framework for energy-saving optimization in actual urban rail transit lines. Two solution methods based on the constructed model are proposed for different engineering applications, ensuring safety, operational efficiency, and energy sustainability of urban rail transit operations.

Chen, Yuefeng, Lao, Ziyao, Jiang, Jiahui, Jiang, Yangsheng, Huang, Jingyang

A Three-Dimensional Multi-Vehicle Bin Packing Method Based on VDN

2026-99-2132

11/13/2026

With the rapid development of e-commerce and on-demand logistics, distribution centers are confronted with the dual challenge of surging cargo volumes and insufficient loading efficiency. In scenarios involving the parallel transportation of multiple vehicles, loading decisions across different vehicles exhibit mutual coupling characteristics. Without coordinated global planning, such unregulated decision-making not only diminishes the overall space utilization rate of the fleet but also gives rise to risks of uneven load distribution and compromised transportation stability. Existing research in this field, however, remains primarily focused on single-vehicle loading optimization, a limitation that renders it unable to meet the practical operational demands of modern logistics systems. To address these issues, this paper investigates the three-dimensional cargo loading problem from a multi-vehicle collaborative perspective and proposes a multi-agent reinforcement learning (RL) method based on Value Decomposition Networks (VDN), which is designed to realize the joint optimization of transportation efficiency and vehicle operational safety. Specifically, the paper models the multi-vehicle loading process as a Partially Observable Optimal Multi-Agent Markov Decision Process (OPOMDP). By adopting a centralized training and distributed execution framework, each vehicle is enabled to generate a globally coordinated loading strategy solely on the basis of its local observation data. On this foundational framework, two key mechanisms are devised to enhance the performance of the loading strategy. First, a stability cost metric, which is constructed based on dynamic shifts in the vehicle’s center of gravity, is introduced to quantify the rollover risk of the vehicle; this metric is then integrated into the reward signal of the RL model, thereby guiding the strategy to maintain transportation safety while improving the cargo loading rate. Second, a specialized placement reward mechanism is proposed to incentivize each agent to proactively explore optimal cargo rotation methods and compact spatial packing structures, which in turn reduces intra-layer voids and elevates the overall volume utilization rate of the cargo compartment. Experimental results demonstrate that the proposed method outperforms traditional heuristic algorithms and mainstream RL baseline algorithms by a significant margin in terms of loading success rate, volume utilization rate, and weight utilization rate. Meanwhile, the stability constraint mechanism embedded in the model effectively mitigates the vehicle rollover risk, ultimately achieving the synergistic optimization of loading efficiency and transportation safety.

Meng, Xiancheng, Xue, Guixiang, Yin, Haiyang, Sun, Yunjun

Shear Stability of Hybrid Fiber-Reinforced Composite Wing and Airworthiness Static Test Verification Method

2026-99-2137

11/13/2026

In this paper, the airworthiness compliance of hybrid fiber-reinforced composite wings is analyzed, focusing on the analysis of shear stability, and the theoretical analysis, numerical simulation, and experimental research are carried out systematically. Carbon fiber and glass fiber are used to form a hybrid reinforced epoxy resin system. In the process of testing the properties of the material, the nonlinear constitutive relationship of the material is grasped. In this study, the first-order shear deformation theory and classical laminated theory are used to construct a reasonable model to analyze the shear stability of the wing structure. With the help of refined three-dimensional finite element simulation, the analysis of the parameters is carried out, and the impact of the ply sequence, skin thickness, and the proportion of ±45° plies on the critical load is analyzed. In this study, the static test scheme is designed according to the requirements of China Civil Aviation Regulations Part 25 (CCAR-25) airworthiness, and the ultimate shear load is simulated by four-point bending. During monitoring of the structural response, a variety of technologies are used, including acoustic emission, displacement, and strain. The test results show that when the structure reaches 105% of the design load, it will be unstable. There is a certain deviation between the simulated buckling load and the experimental one, but it is within the acceptable range, which shows that the numerical simulation is reliable. In this study, the whole technical path is constructed, including material characterization, stability analysis, and experimental verification, which can provide a reference for the airworthiness compliance of hybrid fiber composite wings.

Han, Zhen

Adaptability Analysis of Green Technologies for Plateau Railways Based on Game Theory Combination Weighting

2026-99-2126

11/13/2026

This study suggests a unified evaluation framework to evaluate the adaptability of green design technologies for plateau railway initiatives. We build an evaluation system that looks at four areas: External Environmental, Resource & Environmental, Socio-environmental, and Technical Feasibility. This system is needed because of the unique problems that come with high altitude, hypoxia, and ecological fragility. A new method for combining weights based on Game Theory uses both subjective (Analytic Hierarchy Process (AHP) and objective (Entropy Weight, Grey Relational Analysis) inputs to find strong indicator weights. The final ranking is done using a TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model that uses these weights. The results show that the system is very adaptable overall, with Technical Feasibility and Resource & Environmental Adaptability being the best. Subsystem analysis shows that the Labor-saving and Environmental Protection systems are the most flexible. The framework does a good job of finding specific technologies that need to be improved because they are not very adaptable. Boxplots, line charts, and heatmaps are examples of visualizations that can help you understand performance distribution and find areas for improvement. This study provides a pragmatic, scientifically validated decision-support instrument for the selection and enhancement of sustainable technologies in extreme-environment railway engineering.

Zhang, Chi, Yang, Xinfeng

Route Optimization for Vehicle-Drone Cooperative Delivery Mode Considering Multi-Depot Collaboration and Multi-Type Rendezvous Points

2026-99-2131

11/13/2026

The vehicle-drone cooperative delivery mode has demonstrated significant potential in enhancing last-mile delivery efficiency by effectively integrating the transportation advantages of both vehicles and drones. However, increasingly complex last-mile delivery scenarios impose higher requirements on this cooperative approach. To accommodate the demand for routing flexibility arising from distribution network layouts featuring multi-depot collaboration and multi-type rendezvous points used for drone launch and retrieval, this paper explores the collaborative multi-depot vehicle routing problem (MDVRP) with drones, considering multi-type rendezvous points (CMDVRP-D-MTRP). Specifically, vehicles and drones can flexibly select their respective departure and return nodes for each distribution route, and drones can choose rendezvous points from either fixed parking nodes or temporary parking nodes converted from customer nodes that are actually visited by associated vehicles. First, based on the framework of the half-open multi-depot vehicle routing problem (HOMDVRP), the CMDVRP-D-MTRP is formulated as a Mixed-Integer Programming (MIP) model to minimize the total cost, including distribution and vehicle waiting costs. Second, by adapting benchmark instances of the MDVRP, this paper designs test instances tailored to the CMDVRP-D-MTRP covering various scales and distribution features. We apply the Cardinal Optimizer (COPT) solver to validate model feasibility and analyze the impact of problem scale on computational performance. Finally, based on the classification criteria of multi-depot collaboration and multi-type rendezvous points, numerical experiments are conducted across four delivery modes to verify the effectiveness of the proposed model. Compared to the traditional delivery mode that lacks multi-depot collaboration and relies solely on single-type rendezvous points, the proposed model for the CMDVRP-D-MTRP reduces the average total delivery cost by 20.58%.

Xiao, Shichang, Lu, Shengye, Yu, Shaohua

Spatially Differentiated Reliability Modeling for Smart AtoN Networks during Offshore Wind Farm Construction

2026-99-2152

11/13/2026

The construction of large-scale offshore wind farms significantly increases the complexity of surrounding navigable waters, posing new challenges to the reliability assessment of aids to navigation (AtoN) systems. Traditional evaluation methods often assume uniform risk conditions, which may oversimplify the non-uniform navigational environment during the construction phase. To address this issue, this paper proposes a spatially differentiated reliability assessment framework for smart AtoN networks. The construction area is divided into several spatial zones according to navigational characteristics and traffic intensity. Based on this spatial partitioning, a binary decoupling mechanism of node failure risk is established by separating the impacts of external hazards and internal engineering activities. The hardware degradation process of AtoN nodes is modeled using the Weibull distribution, and an environmental acceleration factor (EAF) is introduced to characterize the spatial variation of physical stress levels. Monte Carlo simulation is then employed to analyze the reliability evolution of the smart AtoN networks over the entire construction cycle. The results reveal pronounced spatial non-uniformity in the degradation trajectories of AtoN nodes across different zones. Compared with conventional models based on uniform assumptions, the proposed approach can more accurately capture early-stage risk concentration and the long-tail resilience effect of low-risk nodes. These findings provide quantitative support for spatially differentiated maintenance strategies and inspection planning, thereby improving navigation safety management in offshore wind farm construction areas.

Xu, Ruqing, Zeng, Fanping

Scheduling Optimization for Automated Container Terminals Using Discrete Artificial Protozoa Optimization

2026-99-2129

11/13/2026

As container terminals advance toward intelligent automation, collaborative scheduling of quay cranes (QCs), automated guided vehicles (AGVs), and automated stacking cranes (ASCs) becomes increasingly complex due to task release times, resource exclusivity, limited transfer-buffer capacity, and battery charging constraints, which induce waiting and blocking and increase makespan. This paper develops a SimPy-based discrete-event simulation model for the QC–AGV–ASC system and formulates a makespan minimization problem under fixed charging rules. Candidate solutions comprise a task sequence and offline assignments for yard-side and transportation resources, which are decoded into executable plans via simulation. Multiple metaheuristics are implemented on a unified evaluation interface and compared across different task scales and resource configurations.

Yang, Miaofei

Fault Diagnosis for Rolling Bearings Based on RCMDE and GG

2026-99-2130

11/13/2026

Given the nonlinear and nonstationary nature of fault signals in rotating machinery, this paper proposes a fault feature extraction and identification method based on refined composite multiscale dispersion entropy (RCMDE) and Gath–Geva (GG) clustering. In the proposed approach, RCMDE is first employed to extract feature sets from the vibration signals. Principal component analysis (PCA) is then applied for dimensionality reduction, and the resulting low-dimensional features are fed into the GG clustering algorithm to achieve fault diagnosis. The effectiveness of the proposed method is validated on a typical bearing fault dataset, with classification performance evaluated using the classification coefficient and partition entropy. Experimental results demonstrate that the proposed RCMDE-GG achieves superior performance in fault feature extraction and exhibits excellent classification capability.

Wang, Guohui, Wang, Haiming, Wang, Tiecheng, Liu, Yongqiang

Structural Design and Simulation Analysis of a New Type of Road De-Icing Roller

2026-99-2128

11/13/2026

This article designs a new type of road de-icing roller. Based on the physical properties of ice and snow on urban roads, theoretical design calculations are performed on the de-icing roller, and the resistance torque and tangential working resistance during operation are obtained. The results indicate that the performance of the ZL50 loader is sufficient for the de-icing roller to operate normally. The finite element simulation method is employed for static analysis, fatigue analysis, modal analysis, and transient dynamics analysis. The results indicate that, under both static load conditions for the de-icing roller shaft and dynamic load conditions due to the de-icing roller impacting the ice layer, the strength, stiffness, and service life meet the design requirements. There is no risk of resonance coupling between the de-icing roller and the loader during operation. This study provides a reliable theoretical basis for the structural optimization and engineering application of road de-icing roller drums.

Sun, Hecheng, Bao, Junhua

Research on the Grading Diagnosis Method for Wheel Tred Faults of EMU Based on Envelope Demodulation

2026-99-2155

11/13/2026

To address the issues of low noise resistance and complex engineering implementation in traditional methods for diagnosing wheel tread faults in high-speed trains, a hierarchical diagnostic method based on envelope demodulation is proposed. This method collects axle box vibration signals and synchronized speed signals, calculates the tread fault characteristic frequency based on wheel diameter, and, after signal preprocessing and Hilbert envelope demodulation, locates the maximum amplitude at the 1x frequency corresponding to the characteristic frequency in the envelope spectrum. After conversion to dB, fault level is determined by using fixed three-level thresholds of 13 dB (preliminary warning), 18 dB (alarm), and 23 dB (alarm). The results of the simulation and field experiment show that this method can accurately extract fault features even in a strong noise environment with a signal-to-noise ratio as low as -3 dB, and the diagnosis accuracy rate reaches 93.3%. Additionally, the process is simple, computationally light, and easy to implement in engineering, providing an efficient and reliable solution for online monitoring of wheel tread faults in high-speed trains.

Li, Hailong, Hong, Zhi, Wang, Peng

ST-ParFusion: Spatio-Temporal Parallel Fusion Network for Traffic Flow Prediction Based on Multi-Relation Driving

2026-99-2013

11/13/2026

Accurate and real-time traffic state prediction is fundamental to optimizing urban traffic control and improving the service quality of online mapping platforms. While data-driven deep learning methods have shown promise in handling large-scale data, most existing approaches suffer from limitations due to their reliance on simplistic modeling components that cannot fully capture complex and multi-scale spatiotemporal dependencies. Unlike prior studies that predominantly utilize static graph structures or sequential processing, this research addresses the challenge of modeling dynamic correlations by proposing a Spatiotemporal Parallel Fusion Network. The theoretical framework of this study integrates graph spectral theory with advanced attention mechanisms to model both the physical and semantic topology of traffic networks. The proposed methodology features a multi-source spatial modeling framework that fuses a static connectivity graph, an inverse distance weighted graph, and a flow similarity graph via multi-relational graph convolution. This approach allows for the simultaneous capture of physical connectivity and dynamic traffic patterns. Furthermore, utilizing a parallel architecture, the temporal modeling component integrates multi-head self-attention with two-dimensional causal dilated convolutions. A cross-attention fusion module is further introduced to dynamically regulate spatiotemporal interactions. Comprehensive tests conducted on four practical traffic datasets indicate that the introduced model significantly outperforms thirteen cutting-edge benchmark methods. The study confirms that the proposed multi-graph strategies and parallel architectures effectively reduce mean absolute error and root mean square error, validating the efficacy of the proposed solution for the advancement of intelligent transportation systems.

Chen, Lulu

A Tunnel Deformation Monitoring Method Based on Line-Structured Light and Swin Transformer

2026-99-2007

11/13/2026

Tunnel deformation monitoring is essential for tunnel safety assessment and early warning. As vital components of transportation infrastructure, tunnels require continuous and reliable structural health monitoring to ensure the efficiency of traffic systems. Traditional tunnel deformation monitoring methods, such as total stations and settlement gauges, typically rely on discrete point measurements. These approaches struggle to capture continuous, full-field deformation of the entire tunnel cross-section, which is essential for identifying localized damage and non-uniform settlement. To overcome this limitation, we propose a tunnel deformation monitoring method based on line-structured light. By projecting line-structured light with a laser emitter, the proposed method enables full-field deformation measurement of the tunnel cross-section. This approach provides continuous and dense deformation measurement rather than discrete point measurements. Accurate extraction of the line-structured light from captured images is a key step in the proposed method. To address the difficulties caused by intricate backgrounds in tunnel environments, we propose a background segmentation approach based on the Swin Transformer. The proposed model improves the separation of the line-structured light from the surrounding background interference. It remains effective under conditions such as dust, surface texture variation, and uneven illumination. Across 30 measurement trials, the proposed method achieved a mean error of 0.5834 mm, demonstrating sub-millimeter accuracy, as well as good stability and reliability under non-ideal imaging conditions.

Xiao, Zhiwen, Li, Zheng, Xu, Junhao, Zhu, Jiasong, Hua, Yuansheng

A Roadside Monocular Vision Method for Target Ranging and Speed Estimation Using 3D Object Detection

2026-99-2016

11/13/2026

With the development of intelligent transportation systems, roadside video perception is expanding from traditional detection and tracking to higher-level measurement outputs, such as inter-target distance and target speed. These measurements are important for traffic flow estimation, accident warning, and timely responsibility assessment after incidents. Monocular cameras offer low cost, easy deployment, and wide coverage, but recovering reliable 3D scale from 2D pixels remains difficult; therefore, achieving stable monocular ranging and speed estimation under practical deployment constraints is still challenging. In this paper, we propose a roadside monocular vision–based method for target ranging and speed measurement. By combining a 3D object detection approach with coordinate transformation, the method outputs real-world target coordinates and speeds in the scene. We conducted several experiments, and the results show that our method can obtain target spatial coordinates and speeds in surveillance settings with reliable performance.

Chen, Ziyi, Lu, Mingfeng, Li, Lan, Pang, Yi, Chen, Yue, Yan, Tianyang, Zhang, Feng, Hu, Weidong

A Deformation Monitoring Method Based on UAV Multi-Source Data Fusion

2026-99-2019

11/13/2026

Water conservancy and transportation infrastructure are traditionally monitored using techniques including total stations, sensor networks, or global navigation satellite systems (GNSS). Although these techniques offer high accuracy at discrete points, they are unable to identify deformations outside of monitored locations, and increasing point density significantly increases costs. Growing interest has been shown in the recently developed unmanned aerial vehicle (UAV)–based remote sensing; nonetheless, most studies rely on single data sources, which results in poor accuracy and low robustness in complex situations. To overcome these drawbacks, this study proposes a deformation monitoring method based on UAV multisource data fusion. A high-precision fused model is created by combining high-resolution oblique imagery with airborne LiDAR point clouds. During aerial triangulation, preprocessed LiDAR point clouds serve as geometric constraints. This combines extensive texture information with high geometric accuracy to enable accurate registration within a single coordinate system. A two-stage registration strategy, comprising feature-based singular value decomposition (SVD) and the iterative closest point (ICP) algorithm, is employed for multi-period models. Surface displacements are measured using cloud-to-cloud (C2C) distances, which are then broken down into orthogonal X-, Y-, and Z-components to meet technical requirements. Validation on a reservoir dam shows that the proposed strategy greatly improves monitoring accuracy and reliability when compared to single-source approaches. The automated workflow provides a practical solution for long-term infrastructure monitoring by facilitating efficient data processing and the detection of subtle surface deformations.

Xu, Junhao, Ni, Guizhu, Li, Zheng, Hua, Yuansheng

Optimization of Pedestrian–Vehicle Traffic Flow Organization Design Based on BIM and Simulation Technologies

2026-99-2006

11/13/2026

In order to optimize the traffic organization design under the condition of human-vehicle interaction and solve the problem of a lack of intuitive decision-making basis in the existing scheme, this paper proposes an optimization method for traffic organization design based on BIM and simulation technology. Firstly, BIM modeling and parametric expression of traffic space and organizational elements are completed, ensuring the consistency and operability of design information. Secondly, combined with the simulation technology, the key spatial bottlenecks and index selection of multi-scheme dynamic replication are identified, and the advantages and disadvantages of different design schemes are comprehensively evaluated. Then, through the comparison of multiple rounds of simulation and test of the optimization scheme, the key performance indicators are selected for detailed analysis and evaluation. Furthermore, the TOPSIS method is used to analyze the results of each scheme, and the recommended design scheme is finally determined by considering multiple influencing factors. The experimental results show that the combination of BIM and simulation technology can effectively improve the design selection, identify and solve the spatial bottleneck in the design, optimize the traffic flow organization, and provide an important theoretical basis and practical reference for the traffic organization design under the condition of coexistence of people and vehicles. This study provides a framework for the combination of BIM and simulation for traffic organization design under the condition of coexistence of people and vehicles, which has important reference significance for improving the design selection effect and reducing the operation risk.

Ye, Xiongfeng, Liu, Shaobo

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