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Announcements for SAE Mobilus

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[ARCHIVED] Important Notice Regarding Scheduled Maintenance
Update: The maintenance was completed successfully and the website is back up again.   --   Original message: We would like to inform you of a scheduled maintenance period impacting Mobilus.  During this time personal login will be disabled preventing personal user Mobilus features like save searches, alerts management, etc. Mobilus content searching and downloading will not be affected. Maintenance Schedule Date: February 6, 2025 Time: 07:00 - 08:00 ET (12:00 - 13:00 GMT/UTC) We will do our best to minimize any inconvenience and complete the process as swiftly as possible.
Service Notice
[ARCHIVED] Counter Reports Issue - Fixed
Last updated: November 11, 2024 [LATEST UPDATE] The issue has been fixed and Counter reports are now accessible through the portal. Usage data until the previous month (i.e. October 2024) can be pulled in reports.  Useful links: FAQs: https://saemobilus.sae.org/help/counter-usage-reports Mobilus Minute video on Counter: https://www.youtube.com/watch?v=45s8S3ixURk Thank you for your patience and understanding. - The Mobilus team -- [PREVIOUS MESSAGE HISTORY] Original announcement: Please note that Counter Database Reports and Title Reports are currently experiencing technical issues, and our team is working on restoring all functionality as soon as possible. We have disabled usage data extraction, while we're looking into the issue.  If you need immediate access to usage reporting, please reach out to your SAE Mobilus sales representative. Thank you for your patience, and we apologize for the inconvenience.
Service Notice
[ARCHIVED] Important Notice: Sunset of Mobilus 3.0
[LATEST UPDATE]   The legacy version of Mobilus (Mobilus 3.0) has now been officially sunset. Thank you for your valuable feedback and continued support in helping to improve Mobilus. - The Mobilus team -- [PREVIOUS MESSAGE HISTORY] IMPORTANT UPDATE: Sunset of Mobilus 3.0 (legacy version) We would like to inform you of an important update regarding SAE Mobilus - As part of our ongoing commitment to enhancing your experience, the legacy version of the platform (Mobilus 3.0) will be phased out on October 31, 2024. The current website with the newly redesigned interface introduced back in April, featuring an intuitive interface, improved search capabilities, advanced search, and in-depth browsing capabilities, will continue to remain operational going forward to support your needs.   What You Need to Know: Last Day of Service (Mobilus 3.0): October 31, 2024 Content Access: You will retain access to all subscribed content through your institutional subscription. Personal Preferences Migration: While most settings tied to your Dasboard in Mobilus 3.0 (alerts, annotations, folders) will transfer over to the new platform, saved searches will not be retained and will need to be recreated in the new platform through your complimentary My Library account. Access the New Platform: Your institutional preferences around login (IP authentication, SSO, username/password) will be retained.   We appreciate your understanding during this transition. Thank you for your continued support. If you have any questions, please don’t hesitate to contact us. Answers to common questions / FAQs are accessible here. Best regards, The Mobilus Team SAE International
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SAE International Digitization and AI Policy (ACTIVE)
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.
Service Notice
Welcome to the new SAE Mobilus® (ACTIVE)
  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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Barriers to Adoption of Artificial Intelligence in Metal Additive ManufacturingEPR202500101/14/2025
Artificial intelligence (AI) is poised to significantly impact metal additive manufacturing (AM). Understanding how one might use AI in AM is challenging because AM experts are not AI experts, nor the other way around. This document introduces AI in AM and guides researchers in accessing relevant literature. It also discusses the hype surrounding AI in AM, the rush to publish peer-reviewed papers that use AI in AM, and the resulting uneven quality of the literature. Conclusions regarding the application of AI in both large and small enterprises are discussed. This document is intended to help illuminate AI in AM for Hands-on engineers who need to quickly understand what levels of problems they might encounter when dealing with AI in AM Engineering managers who need to stay current on emerging trends in their technical realm of responsibilities Policymakers who may not have the relevant technical expertise Faculty and students who want an introduction to AI in AM NOTE: SAE Edge Research
Research Report
AI in Wonderland: Engineering in the Age of Overpromised TechnologyEPR202500201/14/2025
This report explores the move from traditional industry practices to emerging technologies, specifically the integration of artificial intelligence (AI) solutions in engineering service sectors. It highlights the increasing problem of “technology washing,” when organizations overstate (sometimes deceivingly) their technology abilities and ethics, posing challenges to accountability, transparency, and trust in various fields. The rise of AI-based solutions in sectors like autonomous mobility, manufacturing, and aerospace has exposed a contrast between ambitious future aspirations and current technological barriers. With this, the role of human knowledge in guaranteeing ethical, efficient, and clear technology incorporation becomes essential. Starting with an examination of today’s technological scene, this report tackles topics such as the buzz around autonomous systems and the difficulties of standardizing fresh innovations. It also points out the problem of organizations exaggerating
Research Report
Unsettled Issues Regarding Electric Vehicle Battery RecyclingEPR202403012/12/2024
In today's world, the electric vehicle (EV) industry is experiencing a remarkable boom with increasing global demand. With it, comes the surging and unprecedented need for EV batteries. Recycling these batteries has become of crucial importance, as it not only plays a vital role in ensuring the security of the battery supply chain but also serves as a key measure for reducing greenhouse gas emissions. However, there are still several issues that remain unresolved in this domain. Unsettled Issues Regarding Electric Vehicle Battery Recycling delves deep into these issues, thoroughly exploring the current state of the industry and potential solutions to drive sustainable EV battery recycling. By addressing these challenges, we can strive towards a more sustainable future in the EV sector. Click here to access the full SAE EDGETM Research Report portfolio.
Research Report
SAE J3016 as a Learning Device for the Driving Automation Community: Technical, Socio-technical, and Systemic LearningEPR202402912/12/2024
It is the tenth anniversary of SAE International’s Surface Vehicle Recommended Practice effort SAE J3016 to establish a nomenclature standard for driving automation systems and levels of automation. While not exhaustive, this report covers motivation, initiation, and continued development of J3016 regarding driving automation systems, noting that J3016 evolved as a learning device that facilitated the evolution of driving automation systems. It initially worked by establishing common terminology for the technical learning in the field, but over time, J3016 expanded to recognize the human roles in driving automation systems, with later iterations considering broader transportation ecosystems, including fleet operations and remote assistance centers. SAE J3016 as a Learning Device for the Driving Automation Community: Technical, Socio-technical, and Systemic Learning emphasizes ongoing learning to integrate diverse insights about technical, social, and socio-technical challenges of
Research Report
Beyond Aviation: Embedded Gaming, Artificial Intelligence, Training, and Recruitment for the Advanced Air Mobility IndustryEPR202402812/12/2024
Recent advancements in electric vertical take-off and landing (eVTOL) aircraft and the broader advanced air mobility (AAM) movement have generated significant interest within and beyond the traditional aviation industry. Many new applications have been identified and are under development, with considerable potential for market growth and exciting potential. However, talent resources are the most critical parameters to make or break the AAM vision, and significantly more talent is needed than the traditional aviation industry is able to currently generate. One possible solution—leverage rapid advancements of artificial intelligence (AI) technology and the gaming industry to help attract, identify, educate, and encourage current and future generations to engage in various aspects of the AAM industry. Beyond Aviation: Embedded Gaming, Artificial Intelligence, Training, and Recruitment for the Advanced Air Mobility Industry discusses how the modern gaming population of 3.3 million
Research Report
The Software-defined Vehicle: Its Current Trajectory and Execution ChallengesEPR202402711/25/2024
Original equipment manufacturers, Tier 1 suppliers, and the rest of the value chain, including the semiconductor industry, are reshaping their product portfolios, development processes, and business models to support this transformation to software-defined vehicles (SDVs). The focus on software is rippling out through the automotive sector, forcing the industry to rethink organization, leadership, processes, and future roadmaps. The Software-defined Vehicle: Its Current Trajectory and Execution Challenges assesses the state of SDVs and explores the potential hurdles to execution and examines the work being done in the industry. The goal is to evaluate whether the implementation of SDVs will encounter the same fate as electrification or autonomous technologies, which after some level of disillusionment, are expected to pick up momentum in a more mature way. Click here to access the full SAE EDGETM Research Report portfolio.
Research Report
Low-carbon Hydrogen Use Across Economic Sectors and Global RegionsEPR202402611/25/2024
There is great recognition regarding the importance of hydrogen as an energy route for the decarbonization of road vehicles. Several countries are making large investments to create products, services, and infrastructures that allow hydrogen to be used as a clean source for propulsion, but there are still many open questions. This complete hydrogen chain involves production, transformation, transport, storage, and use. Although many initiatives are seeking global production, the use of low-carbon hydrogen is not yet economically competitive. Therefore, for this industry to establish itself, and acknowledging the characteristics of each region, there needs to be more intense coordination of efforts between the different industrial and political segments. Low-carbon Hydrogen Use Across Economic Sectors and Global Regions establishes premises for the hydrogen economy and its main environmental aspects. It also includes proposals and scenarios to establish a strategy that relates to
Research Report
Product Governance and Management for Software-defined Battery Electric VehiclesEPR202402510/21/2024
In recent years, battery electric vehicles (BEVs) have experienced significant sales growth, marked by advancements in features and market delivery. This evolution intersects with innovative software-defined vehicles, which have transformed automotive supply chains, introducing new BEV brands from both emerging and mature markets. The critical role of software in software-defined battery electric vehicles (SD-BEVs) is pivotal for enhancing user experience and ensuring adherence to rigorous safety, performance, and quality standards. Effective governance and management are crucial, as failures can mar corporate reputations and jeopardize safety-critical systems like advanced driver assistance systems. Product Governance and Management for Software-defined Battery Electric Vehicles addresses the complexities of SD-BEV product governance and management to facilitate safer vehicle deployments. By exploring these challenges, it aims to enhance internal processes and foster cross
Research Report
Systems Design Engineering: A Holistic Requirements-led Approach to Concept DesignEPR202402410/21/2024
Many organizations are falling far short of achieving the lifecycle potential of their new product designs. One major source of this suboptimal business performance stems from underleveraging key Systems Engineering and Design Engineering principles in the early phases of the design process. If these are being poorly applied, the following will likely occur: Inefficient use of engineering (and other cross-functional) resources Unnecessarily high product development costs Delayed time-to-market Subpar launch quality Poor system-level safety Suboptimal lifecycle sustainability-related performance Compromised design innovation This report addresses these challenges and articulates how Systems Design Engineering provides nonburdensome and quickly applied methods for overcoming these shortcomings, placing a dedicated focus on the three high-level principles that govern lifecycle product design success. Excellent and efficient performance against each of them is needed to achieve a new
Research Report
Multi-sensor Fusion-based Vehicle LocalizationEPR202402310/21/2024
Multi-sensor fusion (MSF) is believed to be a promising tool for vehicular localization in urban environments. Due to the differences in principles and performance of various onboard vehicle sensors, MSF inevitably suffers from heterogeneous sources and vulnerability to cyber-attacks. Therefore, an essential requirement of MSF is the capability of providing a consumer-grade solution that operates in real-time, is accurate, and immune to abnormal conditions with guaranteed performance and quality of service for location-based applications. In other words, an MSF algorithm depends heavily on data synchronization, cost, an accurate process model, a prior knowledge of covariance matrices, integrity assessments, and security against cyber-attacks. Multi-sensor Fusion-based Vehicle Localization addresses trending technologies in MSF-based vehicle localization and outlines some insights into the unsettled issues and their potential solutions. The discussions and outlook are presented as a
Research Report

Latest Journal Issues

Browse All 16
Passenger Vehicle Systems
SAE International Journal of Passenger Vehicle Systems
Electrified Vehicles
SAE International Journal of Electrified Vehicles
Advances and Current Practices in Mobility
SAE International Journal of Advances and Current Practices in Mobility
Sustainable Transportation, Energy, Environment, & Policy
SAE International Journal of Sustainable Transportation, Energy, Environment, & Policy
Transportation Cybersecurity and Privacy
SAE International Journal of Transportation Cybersecurity and Privacy
Connected and Automated Vehicles
SAE International Journal of Connected and Automated Vehicles
Vehicle Dynamics, Stability, and NVH
SAE International Journal of Vehicle Dynamics, Stability, and NVH
Transportation Safety
SAE International Journal of Transportation Safety
Alternative Powertrains
SAE International Journal of Alternative Powertrains
Commercial Vehicles
SAE International Journal of Commercial Vehicles
Engines
SAE International Journal of Engines
Fuels and Lubricants
SAE International Journal of Fuels and Lubricants
Passenger Cars - Electronic and Electrical Systems
SAE International Journal of Passenger Cars - Electronic and Electrical Systems
Materials and Manufacturing
SAE International Journal of Materials and Manufacturing
Passenger Cars - Mechanical Systems
SAE International Journal of Passenger Cars - Mechanical Systems

Recent Books

Browse All 943
Aerospace Standards Index - 2025ASIN202502/05/2025
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!
Reference
January 2025 AMS IndexAMSJAN-202501/24/2025
Reference
Energy Efficiency of Vehicles, Second EditionR-579-M01/02/2025
This eBook has been provided as the textbook for course number MECH ENG 5543 at Missouri State University. "Energy Efficiency of Vehicles" offers a comprehensive guide to understanding the critical relationship between energy usage and environmental impact. As we face pivotal decisions about energy in the coming decades, this book equips readers with the scientific knowledge needed to make informed, rational choices. In this updated edition, readers will explore: • Detailed calculations related to energy, power, and efficiency. • The environmental consequences of energy consumption. • Models illustrating the impact of city driving on the energy efficiency and fuel economy of cars and trucks. Ideal for students, professionals, and anyone passionate about sustainable energy solutions, this book provides the tools to analyze and improve vehicle energy efficiency, fostering a cleaner and more sustainable future.
Reference
Spacecraft System Health Management: Current Practices and Emerging TechnologiesR-56612/12/2024
Ensuring the safe and reliable operation of spacecraft is a complex task that requires advanced technologies and innovative approaches. This comprehensive guide provides a deep dive into the world of spacecraft system health management. From understanding the intricacies of spacecraft systems to exploring cutting-edge technologies like AI and digital twins, this book offers a valuable resource for anyone interested in the field. Key Features: - Clear and accessible language: The book is written for both technical experts and general readers, making it easy to understand complex concepts. - Real-world examples: Case studies and examples from actual missions bring the theoretical concepts to life. - Future-focused: The book explores emerging technologies that are shaping the future of spacecraft health management, giving readers a glimpse into the future of space exploration. Whether you're a seasoned space engineer or simply curious about the wonders of space exploration, this book
Reference
SAE International’s Dictionary of Electric and Hybrid VehiclesR-56112/04/2024
Welcome to SAE International’s Dictionary of Electric and Hybrid Vehicles, the ultimate reference for mastering the complex and fast-evolving world of electric and hybrid vehicle technologies. Designed for engineers, researchers, students, and enthusiasts, this comprehensive guide is your key to navigating the intricate vocabulary and concepts driving the future of sustainable transportation. As the automotive industry pivots toward greener, more efficient solutions, understanding the terminology and technology behind electric and hybrid vehicles is crucial. This dictionary offers precise, accessible definitions for a wide range of terms—from foundational principles to the latest innovations in battery technology, motor design, and smart charging systems. Each entry has been meticulously curated by experts to ensure accuracy and relevance, providing clear explanations that enhance both comprehension and application. Reflecting the most recent advancements in automotive engineering
Reference
Basic Science and Art of Aircraft Wreckage Reconstruction, Second EditionR-58712/02/2024
Discover the Art and Science of Aircraft Wreckage Reconstruction "Provides excellent guidance on the documentation and reconstruction process and is a good field guide for anyone investigating an aircraft accident." ― Anthony T. Brickhouse, Embry-Riddle Aeronautical University Dive into the essential aspects of aircraft crash investigation with the new edition of Basic Science and Art of Aircraft Wreckage Reconstruction. Written by industry veteran Don Knutson, this comprehensive guide covers the intricacies of investigating crashes, the mindset needed for thorough investigations, and the step-by-step processes for on-scene reconstruction. Designed for practitioners, students, and enthusiasts alike, this book offers detailed references for further reading and research. Through generic yet real-life scenarios, Knutson provides valuable insights into aircraft crash investigations. Whether you're with a government aviation agency, a manufacturer, the military, an insurance company, law
Reference
Fires in Conventional and Electrified Vehicles: Theory, Prevention, and AnalysisR-56711/19/2024
In the ever-evolving landscape of automotive technology, fire safety remains a critical concern. From the era of steam-powered vehicles to today’s cutting-edge electric and hybrid models, understanding the risks and preventive measures for automotive fires is essential for protecting lives and assets. In Fires in Conventional and Electrified Vehicles, Erbis Biscarri, a seasoned expert with extensive experience in both automotive equipment manufacturing and major car manufacturers, presents a definitive guide on the subject. This book provides a thorough exploration of the physical phenomena leading to vehicle fires, offering in-depth analysis methods and prevention strategies tailored to both traditional internal combustion engines and the latest hybrid and electric vehicles. Organized into three key sections—Theory, Prevention, and Analysis—the book delves into the fundamental concepts of fire initiation, sustenance, and propagation, covering a wide range of vehicle systems. It
Reference
Energy Efficiency of Vehicles, Second EditionR-57911/14/2024
"Energy Efficiency of Vehicles" offers a comprehensive guide to understanding the critical relationship between energy usage and environmental impact. As we face pivotal decisions about energy in the coming decades, this book equips readers with the scientific knowledge needed to make informed, rational choices. In this updated edition, readers will explore: • Detailed calculations related to energy, power, and efficiency. • The environmental consequences of energy consumption. • Models illustrating the impact of city driving on the energy efficiency and fuel economy of cars and trucks. Ideal for students, professionals, and anyone passionate about sustainable energy solutions, this book provides the tools to analyze and improve vehicle energy efficiency, fostering a cleaner and more sustainable future.
Reference
Intelligent Electric VehiclesR-58411/01/2024
Embark on a journey into the future of transportation with Intelligent Electric Vehicles. This comprehensive guide demystifies complex concepts, offering a roadmap to harness the monetization opportunities within the thriving IEV ecosystem. From management strategies to cutting-edge technology, this book provides a holistic perspective on the IEV industry. Explore real-world case studies, learn about emerging trends like cockpit intelligence and connected vehicles, and discover how to navigate the challenges and opportunities of this transformative space. Key Features: • Interdisciplinary approach: Bridges the gap between management and technology. • Real-world case studies: Grounds theoretical knowledge in practical applications. • Future-focused insights: Prepares readers for the next wave of innovations. • Monetization roadmap: Offers strategic advice for capitalizing on IEV advancements. Whether you're an automotive industry professional, technology enthusiast, or investor
Reference
By the Numbers: Principles of Automotive Parts Management, Second EditionR-57710/28/2024
In By the Numbers, industry expert Gary J. Naples offers a comprehensive guide to navigating the complexities of modern parts department management. Designed for both new and experienced professionals, this book provides a solid foundation in essential business and personnel management techniques. Understanding that effective parts management requires more than just product knowledge, the author explores the broader aspects of the role, including: • Business acumen: Develop the financial and operational skills needed to succeed. • Inventory management: Optimize stock levels and reduce costs. • Team leadership: Build and motivate a high-performing team. • Customer service: Deliver exceptional service to enhance customer satisfaction. Featuring a new self-assessment tool, Inventory Effectiveness Quotient (IEQ), this book empowers parts managers to measure and benchmark their department's performance. With clear explanations, practical examples, and real-world case studies, By the Numbers
Reference

Recently Published

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Maintenance Scheduling Optimization of Urban Non-intrusive Road Intelligent Transportation System Equipment under the Autonomous Driving Environment2025-01-714802/21/2025
The increasing traveling demands are putting higher pressure on urban networks, where the efficient driving modes highly depend on various non-intrusive ITS equipment for interaction, which asks for higher maintenance scheduling plans minimizing network loss. Current studies have researched methodologies with the aspects of deterministic methods and metaheuristic algorithms under different scenarios, but lack the simulation considering maintenance work type, urban traffic characteristics as well as the ITS equipment. This study aims to optimize the maintenance scheduling plan of urban ITS systems by using the genetic algorithm (GA) and Dijkstra algorithm, as well as other judgmental algorithms to minimize traffic delays caused by maintenance activities, and presents a novel method to assess economic losses. A mixed integer programming model is established simulating the real urban network while considering multiple constraints, including the route selection principle, network updating
Pei, Haoyi, Ji, Yanjie, Chen, Ziang
Technical Paper
Research on NOx Emission Prediction Model for Delivery Trucks Based on Machine Learning and Its Application in Smart Transportation2025-01-718002/21/2025
With the rapid development of smart transport and green emission concepts, accurate monitoring and management of vehicle emissions have become the key to achieving low-carbon transport. This study focuses on NOx emissions from transport trucks, which have a significant impact on the environment, and establishes a predictive model for NOx emissions based on the random forest model using actual operational data collected by the remote monitoring platform.The results show that the NOx prediction using the random forest model has excellent performance, with an average R2 of 0.928 and an average MAE of 43.3, demonstrating high accuracy. According to China's National Pollutant Emission Standard, NOx emissions greater than 500 ppm are defined as high emissions. Based on this standard, this paper introduces logistic regression, k-nearest neighbor, support vector machine and random forest model to predict the accuracy of high-emission classification, and the random forest model has the best
Lin, Yingxin, Li, Tiezhu
Technical Paper
Dynamic Evaluation Method for Public Transport Operation Indicators Based on Rank Transformation2025-01-721902/21/2025
This study presents a method to evaluate the daily operation of traditional public transportation using multi-source data and rank transformation. In contrast with previous studies, we focuses on dynamic indicators generated during vehicle operation, while ignoring static indicators. This provides a better reference value for the daily operation management of public transport vehicles. Initially, we match on-board GPS data with network and stop coordinates to extract arrival and departure timetable. This helps us calculate dynamic operational metrics such as dwell time, arrival interval, and frequency of vehicle bunching and large interval. By integrating IC card data with arrival timetable, we can also estimate the number of people boarding at each stop and derive passenger arrival time, waiting time, and average waiting time. Finally, we developed a comprehensive dynamic evaluation method of public transportation performance, covering the three dimensions: bus stops, vehicles, and
Zhou, Yang, Shao, Yichang, Han, Zhongyi, Ye, Zhirui
Technical Paper
Driver Cognitive Distraction Recognition Based on Multi-Source Data from Simulated Driving Experiments2025-01-714002/21/2025
Nowadays, cognitive distraction in the process of driving has become a frequent phenomenon, which has led to a certain proportion of traffic accidents, causing a lot of property losses and casualties. Since the fact that cognitive distraction is mostly reflected in the driver's reception and thinking of information unrelated to driving, it is difficult to recognize it from the driver's facial features. As a result, the accuracy of prediction is usually lower relying solely on facial performance to detect cognitive distraction. In this research, fifty participants took part in our simulated driving experiment. And each participant conducted the experiment in four different traffic scenarios using a high-fidelity driving simulator, including three cognitive distraction scenarios and one normal driving scenarios. Firstly, we identified the facial performance indicators and vehicle performance indicators that had a significant effect on cognitive distraction through one-way ANOVA. Then we
Qu, Chixiong, Bao, Qiong, Qu, Qikai, Shen, Yongjun
Technical Paper
A GNSS Anti-Interference Integer Float Solution Based on LAMBDA Integer Transform2025-01-715502/21/2025
GNSS is an important means that can provide high-precision navigation and positioning information for intelligent driving. In complex urban environments, after briefly losing the GNSS signal, it takes initialization time for a vehicle to regain high-precision positioning information. Therefore, shortening the initialization time is an important step in providing real-time continuous navigation and positioning services for intelligent driving. The integer float estimator solution has the advantage of free initialization, which can greatly reduce the convergence time of ambiguity fixing. However, its positioning error may show a sudden increase under poor observation conditions. Aiming at the problem that the integer float estimator may be interfered with, this paper proposes an anti-interference integer float estimator method for GNSS based on the LAMBDA integer transform. This paper draws on the idea of integer transform-down correlation in the LAMBDA method to do integer transform
Li, Zhuotong, Yu, Xianwen
Technical Paper
Remaining Useful Life Prediction Method for Highway Electromechanical Equipment Based on Bayesian Algorithm-Optimized CNN-LSTM Model2025-01-714902/21/2025
Efficient maintenance of highway electromechanical equipment is crucial for ensuring reliability within intelligent highway infrastructure and optimizing the allocation of limited maintenance resources. Traditional Remaining Useful Life (RUL) prediction models frequently face limitations due to the complex and dynamic operating conditions of such systems, which often hinder their predictive accuracy and adaptability. To overcome these persistent challenges, this study introduces an advanced RUL prediction model that integrates a Bayesian-optimized Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network. Initially, the study identifies key health indicators that effectively represent the degradation of equipment performance over time. These indicators undergo Spearman correlation analysis to determine their relevance to equipment capacity, ensuring that only the most pertinent features are used for model input. The CNN-LSTM model leverages CNN’s spatial pattern
Wang, Leyan, Zhang, Jian, Yao, Xuejian, Ping, Hao
Technical Paper
Research on Bridge Concrete Crack Damage Prediction Method Based on Deep Learning Temporal Model2025-01-712602/21/2025
Intelligent Structural Health Monitoring (SHM) of bridge is a technology that utilizes advanced sensor technology along with professional bridge engineering knowledge, coupled with machine vision and other intelligent methods for continuously monitoring and evaluating the status of bridge structures. One application of SHM technology for bridges by way of machine learning is in the use of damage detection and quantification. In this way, changes in bridge conditions can be analyzed efficiently and accurately, ensuring stable operational performance throughout the lifecycle of the bridge. However, in the field of damage detection, although machine vision can effectively identify and quantify existing damages, it still lacks accuracy for predicting future damage trends based on real-time data. Such shortfall l may lead to late addressing of potential safety hazards, causing accelerated damage development and threatening structural safety. To tackle this problem, this study designs a deep
Xu, Weidong, Cai, C.S., Xiong, Wen, Zhu, Yanjie
Technical Paper
Identification and Quantitative Analysis Technique of Road Obstacle Safety Risks2025-01-720102/21/2025
The transition from manual to autonomous driving introduces new safety challenges, with road obstacles emerging as a prominent threat to driving safety. However, existing research primarily focuses on vehicle-to-vehicle risk assessment, often overlooking the significant risks posed by static or dynamic road obstacles. In this context, developing a system capable of real-time monitoring of road conditions, accurately identifying obstacle positions and characteristics, and assessing their associated risk levels is crucial. To address these gaps, this study proposes a comprehensive process for rapid obstacle identification and risk quantification, composed of three main components: road obstacle event detection and feature extraction, risk quantification and level assessment, and output of warning information and countermeasures. First, a rapid detection method suited for highway scenarios is proposed based on the YOLOv5 model, enabling fast detection and classification of obstacles in
Chen, Tingting, Chen, Leilei, Yu, Wenlu, Chen, Daoxie
Technical Paper
A Heterogeneous Effects Analysis Method of Highway Crash Factors Based on Causal Framework2025-01-718502/21/2025
The analysis of heterogeneous effects on traffic crashes is crucial for understanding their causal mechanisms and enhancing targeted safety management strategies. However, current methodologies for modeling crash heterogeneous effects lack smooth methods for selecting optimal controls. This study proposes an intuitive variable selection method to improve heterogeneity analysis of crash data, as well as performance evaluation and validation tests. The method utilizes causal discovery algorithms to obtain causal diagrams for selecting confounders, moderators, and neutral control factors in observational collision data. The effectiveness and performance of these methods are assessed through the quality of Heterogeneous Treatment Effects (HTE) estimation. Using a real-world highway crash data, the proposed variable selection process based on causal framework is illustrated. Results indicate that most HTE estimation models perform well in terms of goodness-of-fit and robustness when
Liang, Xiaoxi, Li, Shuang, Xu, Nuo, Guo, Xiucheng, Pu, Ziyuan
Technical Paper
Semantic Segmentation of Lane Lines for Pix2Pix Network Introducing Ghost Module2025-01-720802/21/2025
Technology for lane line semantic segmentation is crucial for ensuring the safe operation of intelligent cars. Intelligent cars can now comprehend the distribution and meaning of scenes in an image more precisely thanks to semantic segmentation, which calls for a certain degree of accuracy and real-time network performance. A lightweight module is selected, and two previous models are improved and fused to create the lane line detection model. Finally, experiments are conducted to confirm the model's efficacy. This paper proposes a lightweight replacement program with the aim of addressing the issue of large parameterization in the generative adversarial network (GAN) model and difficult training convergence. The overall network structure is selected from the Pix2Pix network in the conditional generative adversarial network, and the U-net network of the generator is cut and replaced by the Ghost Module, which consists of a modified downsampling module that enhances the global fusion
Yang, Kun, Wang, Jian
Technical Paper
Tunnel Lining Crack Intelligent Recognition Based on YOLOv11 Algorithm2025-01-713702/21/2025
Tunnel linings are an important safeguard for the integrity and stability of tunnels. However, cracks in the tunnel lining may have extremely unfavourable consequences. With the acceleration of urbanisation and the increasing construction of tunnels, the problem of cracks in the concrete lining is becoming more and more prominent. These cracks not only seriously affect the stability of the structure, but also pose a serious threat to the safety of tunnel operation. If left unchecked, the cracks may expand further and cause various safety hazards, such as water leakage and falling blocks. This in turn will undermine the normal function of the tunnel and endanger the lives of tunnel users. It has been proved that the traditional manual method of detecting cracks in tunnels has problems such as low accuracy and low efficiency. In order to solve this problem, it is very necessary for this study to pioneer an intelligent method for identifying tunnel lining cracks using the YOLOv11
Zhang, Yalin, Niu, Pei, Guo, Feng, Yan, Wei, Liu, Jian, Kou, Lei
Technical Paper
Multi-Stage Registration and Feature Based Robust and Precise LiDAR SLAM2025-01-716102/21/2025
In a complex and ever-changing environment, achieving stable and precise SLAM (Simultaneous Localization and Mapping) presents a significant challenge. The existing SLAM algorithms often exhibit limitations in design that restrict their performance to specific scenarios; they are prone to failure under conditions of perceptual degradation. SLAM systems should maintain high robustness and accurate state estimation across various environments while minimizing the impact of noise, measurement errors, and external disturbances. This paper proposes a three-stage method for registering LiDAR point cloud. First, the multi-sensor factor graph is combined with historical pose and IMU pre-integration to provide a priori pose estimation; then a new method for extracting planar features is used to describe and filter the local features of the point cloud. Second, the normal distribution transform (NDT) algorithm is used as coarse registration. Third, the feature to feature registration is used for
Li, Zhichao, Tong, Panpan, Shi, Weigang, Bi, Xin
Technical Paper
Study on Variable Speed Limit Control Strategy of Highway under Intelligent Connected Environment2025-01-718902/21/2025
This paper presents a novel variable speed limit control strategy based on an Improved METANET model aimed at addressing traffic congestion in the bottleneck areas of expressways while considering the impact of an intelligent connected environment. Traffic flow simulation software was employed to compare the outcomes of the traditional variable speed limit model with those derived from the proposed strategy. The results indicated that under three scenarios—main road, ramp, and lane closure—with a 100% penetration rate of intelligent connected vehicles, the average delay for vehicles utilizing the new model decreased by 9.37%, 11.11%, and 7.22%, respectively. This study offers an innovative approach to highway variable speed limits under an intelligent connected environment.
Qi, Tiancheng, Qu, Xinhui, Gu, Haiyan, Sang, Zheming, Ning, Fangyue
Technical Paper
Construction and Application of Traffic Accident Knowledge Graph Based on LLM2025-01-713902/21/2025
Records of traffic accidents contain a wealth of information regarding accident causes and consequences. It provides a valuable data foundation for accident analysis. The diversity and complexity of textual data pose significant challenges in knowledge extracting. Previous research primarily relies on Natural Language Processing (NLP) to extract knowledge from texts and uses knowledge graphs (KGs) to store information in a structured way. However, the process based on NLP typically necessitates extensive annotated datasets for model training, which is complex and time-consuming. Moreover, the application of traffic accident knowledge graphs by direct information querying within the graph requiring complex commands, which leads to poor interaction capabilities. In this study, we adapt an innovative approach integrates Large Language Models (LLMs) for the construction and application of a traffic accident knowledge graph. Based on the defined schema layer of the traffic accident
Hou, Yingqi, Shao, Yichang, Han, Zhongyi, Ye, Zhirui
Technical Paper
Study on the Effect of Molecular Sieve Types on the Explosion Suppression Performance of Coal Dust during Transportation Process2025-01-718202/21/2025
The Chinese demand for coal necessitates the transportation over long distances, due to the disparity between its availability and the need. With the increase of coal demand, the scale of railroad transportation is also gradually expanding, which leads to the increasingly prominent problem of coal transportation safety. Especially in the transportation process, coal dust explosion has become an important safety hazard due to the accumulation of a large amount of coal dust in some specific Spaces. Therefore, the study of coal dust explosion suppression has become an urgent task at present. The solution to this problem is of great significance to ensure the safety of coal transportation. In this study, the explosion suppression of coal dust by four types of molecular sieves was experimentally analyzed using the Hartmann flame propagation test equipment, and the results showed that mesoporous molecular sieves were far superior to microporous molecular sieves in suppressing explosions. The
DongYe, Shengjing, Zhang, Yansong, Chen, Jinshe, Yang, Yang, Wang, Fei, Han, Jin
Technical Paper
Research on Taxiing Path Planning of Hub Airport Based on Dijkstra Algorithm2025-01-719802/21/2025
Aiming at the problem of insufficient capacity of taxiways in hub airports, which combine the safety interval, conflict resolution and fair principles, a taxiway planning model is established by taking the shortest taxiway as the optimisation goal, considering fuel consumption and exhaust emissions. Dijkstra algorithm is used to transform the taxiing path into an adjacency matrix, and conflict resolution is carried out in a weighted way. Under the premise of ensuring zero conflict of taxiways, the total taxiing distance is reduced. Based on actual operational data from a hub airport in China, the results show that the proposed taxiing path planning method is feasible, shortening the aircraft taxiing distance and improving the surface taxiing efficiency.
Feng, Bocheng, Qi, Xinyue, Zhang, Hongbin
Technical Paper
Port Node Importance and Risk Assessment in Shipping Networks Based on An Improved PageRank Approach and Reliability Analysis2025-01-718302/21/2025
Since the rapid development of the shipping and port industries in the second half of the twentieth century, the introduction of container technology has transformed cargo management systems, while simultaneously increasing the vulnerability of global shipping networks to natural disasters and international conflicts. To address this challenge, the study leverages AIS data sourced from the Vessel Traffic Data website to extract ship stop trajectories and construct a shipping network. The constructed network exhibits small-world characteristics, with most port nodes having low degree values, while a few ports possess extremely high degree values. Furthermore, the study improved the PageRank algorithm to assess the importance of port nodes and introduced reliability theory and risk assessment theory to analyze the failure risks of port nodes, providing new methods and perspectives for analyzing the reliability of the shipping network.
Li, Ding, Cheng, Cheng, Zhao, Xingxi, Li, Zengshuang
Technical Paper
Identifying High-Risk Situations in Right-Turn Phase of Heavy-Duty Vehicles Using Naturalistic Driving Data2025-01-720902/21/2025
The interaction between heavy-duty vehicles turning right and non-motor vehicles going straight has led to severe traffic crashes. It is essential to evaluate the driving risk of heavy-duty vehicles in the right-turn phase. Increasingly, studies have explored some indicators associated with driving risk. Based on naturalistic driving data of 121 heavy-duty vehicles in Nanjing, this research combined factor analysis and K-means cluster algorithm to assess the driving risk of two scenarios, one without a blind spot warning and another with a blind spot warning during the right-turn phase. The results have concluded the driving characteristics of heavy-duty vehicles under different risk levels. It formed a set of driving risk level assessment methods for heavy-duty vehicles in the right-turn phase. This evaluation method is expected to identify high-risk right-turn behaviors of heavy-duty vehicles and provide some insights to practitioners for traffic management.
Zhang, Hedi, Fu, Yuanhang, Ma, Yongfeng, Chen, Shuyan
Technical Paper
RCS Measurement and Modeling of Typical Traffic Participants Based on Millimeter-Wave Radar for Autonomous Vehicles2025-01-719902/21/2025
At present, 77GHz millimeter-wave (MMW) radar has become a critical sensor in intelligent transportation systems due to its all-weather detection capability, which enables it to resist complex weather and light interference. Radar cross section (RCS) is a significant characteristic of radar, greatly impacting the detection quality of traffic targets across various traffic scenarios. RCS is usually measured in an anechoic chamber to establish a model of the RCS of typical traffic participants. However, due to large target fluctuations and multi-angle scattering centers of targets, representing the RCS characteristics of typical traffic participants with a single point is challenging. Taking global vehicle target (GVT), pedestrian target and cyclist target as examples, this paper proposes a method for measuring and modeling the RCS features of typical traffic participants. For the static RCS features of targets, we measured the RCS of the target under different viewing angles in an
Liu, Tengyu, Shi, Weigang, Tong, Panpan, Bi, Xin
Technical Paper
A Car-following Model Considering Acceleration of Multiple Vehicles for Mixed Traffic Flow2025-01-719502/21/2025
The practice of vehicle platooning for managing mixed traffic can greatly enhance safety on the roads, augment overall traffic flow, and boost fuel efficiency, garnering considerable focus in transportation. Existing research on vehicle platoon control of mixed traffic has primarily focused on using the state information of the leading or head vehicle as control input for following vehicles without accounting for the driving variability of Human-driven Vehicles (HDVs), which does not conform to the driving conditions of vehicles in reality. Inspired by this, this paper presents a car-following model for Connected and Automated Vehicles (CAVs) that utilizes communication with multiple preceding vehicles in mixed traffic. The study further investigates the impact of parameters such as the speed and acceleration of preceding vehicles on the car-following behavior of CAVs, as well as the overall effect of different CAV penetration rates on mixed traffic flow. Firstly, a mixed-vehicle
Peng, Fuke, Huang, Xin
Technical Paper
Evaluation Method for the Impacts of Truck Platoons Lateral Control on Pavement Performance2025-01-715202/21/2025
The introduction of autonomous truck platoons is expected to result in drastic changes in operational characteristics of freight shipments, which may in turn have significant impacts on efficiency, energy consumption, and infrastructure durability. Since the lateral positions of autonomous trucks traveling consecutively within a lane are fixed and similar (channelized traffic), such platooning operations are likely to accelerate damage accumulation within pavement structures. To further advance the application of truck platooning technology in various pavement environments, this study develops a flexible evaluation method to evaluate the impact of lateral arrangement within autonomous truck platoons on asphalt pavement performance. This method simplifies the impact of intermittent axle load applications along the driving direction within a platoon, supporting platoon controllers in directly evaluating pavement damage for different platoon configurations. Specifically, a truck platoon
Wenlu, Yu, Ye, Qin, Chen, Daoxie, Min, Yitong, Chen, Leilei
Technical Paper
Intelligent Detection Technologies for Pre-Paving Asphalt Pavement2025-01-717902/21/2025
This paper presents advanced intelligent monitoring methods aimed at enhancing the quality and durability of asphalt pavement construction. The study focuses on two critical tasks: foreign object detection and the uniform application of tack coat oil. For object recognition, the YOLOv5 algorithm is employed, which provides real-time detection capabilities essential for construction environments where timely decisions are crucial. A meticulously annotated dataset comprising 4,108 images, created with the LabelImg tool, ensures the accurate detection of foreign objects such as leaves and cigarette butts. By utilizing pre-trained weights during model training, the research achieved significant improvements in key performance metrics, including precision and recall rates. In addition to object detection, the study explores color space analysis through the HSV (Hue, Saturation, Value) model to effectively differentiate between coated and uncoated pavement areas following the application of
Hu, Yufan, Fan, Jianwei, Tang, Fanlong, Ma, Tao
Technical Paper
Continuous Trust Estimation Method Based on Kalman Filtering in Human-Machine Co-Driving2025-01-718602/21/2025
Subjective trust and active takeover behavior characteristics are two important aspects of trust performance in human-machine co-driving cars. However, trust is a subjective, abstract concept that changes over time and is difficult to measure directly. At present, there is a lack of quantitative research on objective trust and dynamic estimation of continuous trust under the influence of different independent variables, which inhibits its further use and development. This study adopts a continuous objective trust estimation method based on driving behavior, which mathematically describes the continuous measurement problem of objective trust and extracts driving behavior indicators in different traffic event research segments. The objective trust state space equation is established, and the objective trust estimation model is constructed based on the Kalman filter algorithm. Through model parameter definition and model verification, the estimation results and subjective trust are
Lin, Qingyang, Huang, Jun, Wang, Xinpeng, Lyu, Nengchao
Technical Paper
Estimating Extreme Water Levels in Myanmar’s Ayeyarwady Seas by the Monte Carlo Method2025-01-718102/21/2025
Storm surge disasters in the northern Indian Ocean and along the Bay of Bengal pose substantial risks to the safety of lives, property, and industrial trade within Myanmar's Ayeyarwady Region. The absence of long-term tidal data makes traditional frequency analysis methods inadequate for accurately predicting extreme water levels with high return periods. This study utilizes numerical simulations to forecast extreme water levels caused by recurrent cyclonic storm surges along Myanmar's coastline. A combined approach using the Monte Carlo stochastic model and the Delft3D hydrodynamic model was employed for these simulations. The results show that the Delft3D model is effective in predicting tidal levels in engineering contexts, addressing data deficiencies while identifying critical water levels. Model accuracy was validated through extensive simulations, confirming its suitability for forecasting extreme water levels. Although some discrepancies may arise due to limited data
Yin, Kai, He, Liye, Liu, Kaofan, Liu, Shuo, Xu, Sudong
Technical Paper
Matching and Fusing Multi-Source Vehicle Information in Highway Gantry Scenarios Using Convolutional Neural Networks2025-01-713602/21/2025
The performance differences of multiple sensors lead to inconsistencies, incompleteness, and distortion in the perception data of multi-source vehicle information in highway scenarios. Optimizing data fusion methods is important for intelligent toll collection systems on highways. First, this paper constructs a dataset for matching and fusing multi-source vehicle information in highway gantry scenarios. Second, it develops convolutional neural network models, Match-Pyramid-MVIMF-EGS and CDSSM-MVIMF-EGS, for this purpose. Finally, comparative experiments are conducted based on the constructed dataset to assess the performance of the Match-Pyramid-MVIMF-EGS and CDSSM-MVIMF-EGS models. The experimental results indicate that the Match-Pyramid-MVIMF-EGS model performs better than the CDSSM-MVIMF-EGS model, achieving matching and fusion accuracy of 93.07%, precision of 95.71%, recall of 89.17%, F1 scores of 92.32%, and 186 of training throughput respectively.
Wang, Junjun, Zhao, Chihang
Technical Paper