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Simulink DevOps Evolution: Bridging Interactive Platforms for Accelerated Model-Based Workflows2026-01-0080To be published on 04/07/2026
This paper builds on last year’s paper presenting DevOps automation in the context of model-based development. Following that paper, we interviewed Simulink users in passenger automotive, motorsports, commercial vehicles, aviation, rocketry, and industrial automation. We discovered that much of the benefit of DevOps platforms to reduce product development cycle time relies on their interactive features. We prototyped new tools to bridge interactive DevOps Git-based platforms with model-based development workflows, and then gathered reactions from another round of interviews. Here we present these interactive DevOps workflows with the feedback from these interviews to contextualize how engineering teams could adopt them to accelerate their own model-based workflows.
Mathews, Jon, Ferrero, Sergio, Tamrawi, Ahmed, Sauceda, Jeremias
Benchmarking Driver Responses in Head-On Conflicts: Toward an Objective Protocol for Human and AV Evaluation2026-01-0534To be published on 04/07/2026
Head-on emergency conflicts present a unique challenge for evaluating driver and automated vehicle (AV) performance. Unlike most emergency scenarios that follow a direct stimulus–response sequence, head-on encounters are characterized by a stimulus–wait–response pattern in which time to contact (TTC) strongly shapes driver behavior. Drawing on naturalistic and simulator data, this study quantified human responses to head-on conflicts. When the opposing vehicle crossed the centerline with more than 2 seconds of TTC, drivers delayed braking until an average of 1.3 seconds (SD = 0.8) before impact, regardless of the timing of the intrusion. By contrast, when TTC was less than 2 seconds, braking was initiated only 0 to 1.0 second before impact. As a result, many drivers applied the brakes less than half a second before collision. These findings provide a meta-analytically grounded benchmark for comparing human behavior to AV decision-making processes and demonstrate the need for conflict
Muttart, Jeffrey, Dinakar, Swaroop, Maloney, Tim, Adikhari, Bikram, Gernhard-Macha, Suntasty
Techno-Economic Evaluation of Electified Vehicle Options in Drayage Fleets2026-01-0454To be published on 04/07/2026
The transition of drayage fleets from diesel to battery electric vehicles (BEVs) poses both opportunities and uncertainties, especially regarding long-term economic viability. While total cost of ownership (TCO) is a useful benchmark, fleet operators and investors are equally concerned with investment performance metrics such as payback period and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study proposes a comprehensive framework to evaluate the economic feasibility of transitioning from diesel to BEVs, jointly analyzing TCO parity conditions, payback periods, and IRRs under different technology, cost, and operational scenarios. Building on a previously developed cost modeling tool, the analysis incorporates evolving battery prices, energy costs, vehicle performance characteristics and other assumptions from 2025 to 2050. Real-world duty cycle data from a Class 8 drayage fleet at the Port of Savannah is used to capture
Sun, Ruixiao, Sujan, Vivek, Goulet, Nathan, Wang, Qixing
AI-Driven Data Consistency and Relationship Inference System for Agile Component Library Management2026-01-0109To be published on 04/07/2026
Reliable component libraries are the foundation of the engineering process and the starting point for all intelligence within CAD tools. In practice, however, libraries created and maintained by librarians often contain incomplete, inconsistent, or outdated data. This paper introduces the component data consistency and relationship inference AI system, developed within Amoeba software, which addresses these challenges by improving component library quality. The system uses AI to infer component attributes such as component type, gender, color, material, etc. Moreover, it can identify relationships such as the family a connector is associated with based on its attributes and geometry. The system improves data consistency in areas such as resolving mismatched wire size constraints imposed by the connector and cavity components. It also utilizes computer vision to identify common connector footprints, cavity sizes, and 2D symbol geometries. Deployed within Amoeba software, the system has
Phan, Dung, Horvat, Bryan
An Integrated Methodology for Sustainable Mobility Infrastructure Planning: The Atlanta Aerotropolis Case Study2026-01-0097To be published on 04/07/2026
By the early 2020s, more than 4.5 billion people have been living in urban areas worldwide, compared to just 1 billion in 1960. Rising growth in urban populations present challenges to infrastructure and transportation systems. Higher traffic levels and reliance on conventional vehicles have contributed to heightened greenhouse gas (GHG) emissions, rising global temperatures, and irreversible environmental degradation. In response, emerging transportation solutions—including intelligent ridesharing, autonomous vehicles, zero-tailpipe-emission transport, and urban air mobility—offer opportunities for safer and more sustainable transportation ecosystems. However, their widespread adoption depends not only on technological performance and efficiency, but also on integration with current infrastructure, safety, resilience to unexpected disruptions, and economic viability. A dynamic, agent-based System-of-Systems (SoS) transportation model is developed to simulate vehicle traffic and human
Rana, Vishva, Balchanos, Michael, Mavris, Dimitri, Valenzuela Del Rio, Jose
An Updated Correlation Study between the Full Size Wind Tunnels of General Motors, Ford Motor Company, and FCA US LLC2026-01-0612To be published on 04/07/2026
As automotive aerodynamic testing facilities evolve to capture more real-world behavior, updating the correlation between old and new technologies is essential. Recently, the three-member consortium of the United States Council for Automotive Research (USCAR) - FCA US LLC, Ford Motor Company, and General Motors - transitioned from full-size static ground plane facilities to 5-belt moving ground plane wind tunnel facilities. The primary objective of this study was to update the correlation data sets to maintain consistent and robust data sharing among companies, which is the cornerstone of USCAR efforts. To achieve this, a set of updated correlation data sets were calculated to replace the original correlation study results from 2008. Additionally, the methodology for applying correlation equations was revised from using averaged wind tunnel data to employing direct wind tunnel-to-wind tunnel correlation equations. In a two-phase correlation effort conducted in 2022 and 2025, the three
Nastov, Alexander, Lounsberry, Todd, Madin, Trevor, Langmeyer, Gregory, Fadler, Gregory, Skinner, Shaun, Horton, Damien
Lateral stability and energy-saving integrated control of distributed drive vehicles with four wheel hub motors2026-01-0635To be published on 04/07/2026
Abstract: In order to improve the lateral stability and energy optimization of four-wheel hub motor distributed drive vehicles under complex road conditions, a layered control strategy for yaw stability is proposed. The upper controller is designed based on the sliding mode control principle to create a yaw moment controller, establishing both a two-degree-of-freedom model and a seven-degree-of-freedom model for the vehicle, tracking the desired yaw rate, desired center of mass side slip angle, actual yaw rate, and actual center of mass side slip angle to obtain the corresponding additional yaw moment. Moreover, the state of the vehicle is analyzed using the phase plane method of the center of mass side slip angle and yaw rate, and the total additional yaw moment for the vehicle is computed based on weighted calculations according to the state. Additionally, an unscented Kalman filter observer is established to enhance the tracking accuracy of the actual yaw rate and actual center of
shi, cheng'ao, liu, bingsen, Zou, Xiaojun, Wang, Tao, Zhang, Ming
Machine Learning Modeling for Compressive Property Prediction of Foams2026-01-0512To be published on 04/07/2026
Foam material models for automotive structural analysis typically require tensile and compressive data at multiple strain rates. The testing is costly and may require a long time to complete. For many applications, foams of similar chemistry are used and the foam structural responses, such as stiffness and compression force deflection, are controlled by the foam density. In such cases, Machine Learning (ML) lends itself as an ideal tool to detect the trends in material response based on density and strain rate. In this paper, five microcellular polyurethane (PU) foams of different densities were tested at four strain rates ranging from 0.01/s to 100/s. A ML model capable of predicting compressive stress-strain response for a range of densities was developed. The model demonstrated good prediction capability for intermediate strain rates at all foam densities and in extrapolating stress-strain curves at higher densities at all strain rates. The strain rate trends for a density outside
M, Gokula Krishnan, Kavimani, Harish, Muppana, Sai Siddhartha, Savic, Vesna, Chavare, Sudeep, V S, Rajamanickam
Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis2026-01-0170To be published on 04/07/2026
Vision-language models (VLMs) offer a promising end-to-end approach for autonomous driving, but their robustness to physical adversarial attacks remains an unaddressed gap for safety-critical deployment. This paper introduces a systematic framework for evaluating these attacks using the Dolphins VLM and CARLA simulator. We employ Natural Evolution Strategies (NES), a black-box optimization method, to generate adversarial patches without internal model access. Our approach uses semantic similarity loss and Expectation over Transformation (EoT) to ensure physical realizability under varying viewing conditions. Patches are strategically placed on realistic advertising infrastructure (bus shelter panels and highway billboards), reflecting a plausible threat model. We establish two complementary scenarios: crosswalk pedestrian detection suppression and highway steering manipulation. Our evaluation reveals critical vulnerabilities. Adversarial patches achieved a 76.0% overall attack success
Fernandez, David, MohajerAnsari, Pedram, Salarpour, Amir, Pese, Mert D.
Multi-Body Dynamics Control Integration Tool Application in Hybrid Vehicle System Identification2026-01-0417To be published on 04/07/2026
The multi-body dynamics (MBD) model and the MATLAB Simulink model can be integrated to create a control-integration model. Using a high-fidelity MBD model to represent the vehicle as the plant, this integrated model can be used to analyze vehicle system physics and develop control strategies. For hybrid vehicles, this process is more complex because the powertrain and other vehicle systems are often built as separate MBD models. This paper describes a method for integrating a powertrain model developed in AMESIM, a vehicle model developed in SIMPACK, and a control model developed in MATLAB Simulink. The resulting integrated model was then used to perform frequency sweep analysis to identify driveline system properties. In particular, the driveline frequency and the amplitude of the transfer function between motor speed and motor torque are critical parameters. By applying active damping control to the driveline system, the peak amplitude and driveline vibrations can be reduced. The
Xing, Xing, Mathew, Vino
Energy Efficiency Analysis of U-Haul Truck and Trailer Systems: Impact of Tongue Weight and Load Variations2026-01-0466To be published on 04/07/2026
The demand for improved energy efficiency in real-world vehicle operations continues to grow with technology enhancement. When transporting large cargos with passenger pickup trucks and rental trailers, the interaction between vehicle payload, towing configuration, and fuel consumption becomes a key factor in overall system efficiency. Understanding how towing configurations and trailer loading influence fuel consumption and vehicle performance is critical for both consumer guidance and vehicle system design. This study investigates the energy efficiency of U-Haul truck and trailer systems, with a particular focus on the influence of trailer tongue weight. U-Haul truck and trailer simulation models were developed using AVL Vehicle Simulation Model (VSM) software, with an F-350 engine brake-specific fuel consumption (BSFC) map integrated to represent realistic engine performance. Two configurations with equal payload were evaluated: (1) a U-Haul truck alone, and (2) a U-Haul truck
Wang, Gang, Yang, Allan, Chen, Yan
Design, Fabrication, and Assessment of a User-Friendly Suspension System for a 2025 BAJA Vehicle2026-01-0588To be published on 04/07/2026
A suspension system was designed, fabricated, and tested following a systems design approach by the Desert Hare Off-road Team from South Dakota State University (SDSU). Compared to previous suspensions, the new suspension system is more reparable and contains a minimal number of custom parts, while still maintaining sufficient strength to withstand dynamic loads experienced when operating the vehicle. Modifications were also made to fit the newly designed vehicle body frame. As an integral part of the team’s 2025 Baja vehicle, the redesigned suspension system contributed to the vehicle’s improved performance during the 2025 SAE (Society of Automotive Engineers) Baja Competition. This paper presents a detailed account of the design, development, and fabrication process of the suspension system. The final design was tested and evaluated via both computer simulations and physical tests, whose efficiency and reliability were finally demonstrated by the team’s improved ranking in the 2025
Liu, Yucheng, Anderson, Matthew, Larson, Cody, Rodgers, Joshua, Seberger, Aaron, Letcher, Todd
Integrating Optimization and Model Fidelity Selection Methods for Real-Time Driver-in-the-Loop Vehicle Simulators2026-01-0621To be published on 04/07/2026
Automotive OEMs perform extensive prototype testing to configure vehicles for objective criteria (performance), and subjective criteria (handling and comfort). To reduce testing time and costs, OEMs rely on real-time Driver-In-the-Loop Simulators (DIL) running complex Multi-Body Dynamics (MBD) models. Recent advances in simulation technology have increased model accuracy but also operating costs, possibly limiting the viability of real-time DIL applications. Running high fidelity MBD models in real-time is computationally intensive and often requires re-configuration, CAE model de-contenting, solver setting optimization, which can introduce significant analysis errors. This presents a core challenge: selecting model fidelity levels that result in computationally efficient simulations, while maintaining sufficient predictive accuracy. This study introduces a methodology that integrates optimization algorithms with decision-making techniques to select the right fidelity within a
Balchanos, Michael, Emara, Mariam, Zarate Villazon, Angel, Mavris, Dimitri
Collaborative Cybersecurity in a Shifting Automotive Supply Chain2026-01-0084To be published on 04/07/2026
The evolution toward software-defined vehicles (SDVs) is causing disruption to the traditional automotive supply chain and breaking down the common hierarchical OEM, tier 1 supplier, and tier 2 supplier relationships. With demands for faster software release cycles, more advanced software projects involving multi-party development, and considerations for end-to-end embedded and cloud integrations, new cybersecurity challenges are introduced that no single organization can address alone. Thus, this disruption creates new trust dependencies and requires new models for collaboration, transparency, and joint responsibility in cybersecurity. This paper presents a collaborative cybersecurity model, emphasizing shared responsibility during multi-party development between OEMs, tier 1 and 2 suppliers, engineering services organizations, and technology and services providers. As such, we explore collaborative approaches for each stage in the development lifecycle including design, development
Oka, Dennis Kengo, Vinzenz, Nico
HaloBus: Edge Computing‑Enabled Real‑Time Boarding and Exit Detection for Enhanced Transportation Safety Using Lightweight AI2026-01-0555To be published on 04/07/2026
This paper proposes HaloBus, an innovative, edge‑computing solution designed to mitigate this risk by detecting student boarding and exiting in real time using lightweight AI based methods. A persistent challenge in elementary school transportation is the issue of missing students after they exit their buses, which disproportionately impacts low-income households. Current safety systems are forced upon the individual households with their own methods. Common methods include applications on a personal device or a small tracker. However, not everyone can afford these options, and every parent deserves to feel like their child is safe. That is why HaloBus was invented. The system employs YOLOv5u—an Ultralytics‑enhanced, anchor‑free, split‑head architecture that offers a superior accuracy–speed tradeoff. By providing real-time, on-device alerts, HaloBus enables immediate intervention to prevent a student from being left behind, thereby shifting the focus from reactive post-incident
Getz, Grayson, Zadeh, Mehrdad, TAN, Teik-Khoon
A Shared Autonomy Architecture: Utilizing an Advanced Personalized Control System (APeCS) and Shared Autonomy Arbiter (SAB)2026-01-0147To be published on 04/07/2026
The shared autonomy framework has become an option with great potential in the field of autonomous vehicles. Human and machine control decisions typically demonstrate strengths in different scenarios. As a result, the robustness of systems can be enhanced by the collaboration between humans and autonomy. A shared autonomy architecture that takes into account both human and environmental factors was proposed in this work. The authority distribution between the human operator and the autonomy algorithm was determined by the Shared Autonomy Arbiter (SAB). Designed with a two-tier structure, the SAB incorporated a policy-level decision module, as well as a numerical-level arbitration tuning module. A fuzzy inference system (FIS) was incorporated to enhance the noise tolerance of the policy selection module. Furthermore, the human factor was taken into account by applying a projection to the users’ control input. The human operator’s control decision was projected by the Adaptive
Sang, I-Chen, Norris, William, Patterson, Albert, Sreenivas, Ramavarapu S., Soylemezoglu PhD, Ahmet, Nottage, Dustin S.
Vehicle Test Platform with Experimentally Correlated Tire Model for Torque Vectoring Control2026-01-0622To be published on 04/07/2026
This paper presents a testing platform for torque vectoring controls utilizing a 10 degree of freedom (DOF) vehicle simulation and a ⅕ scale radio control test vehicle. These vehicle simulations or “Digital Twins” have been widely adopted throughout the automotive industry for their lower operating costs and ease of implementation. Virtual models are not perfect representations of reality, however, and physical testing is still necessary to validate systems for use in the real world. This is especially true when testing safety-critical features such as Advanced Driver Assist Systems (ADAS). As a result, a simulation environment working in conjunction with a test vehicle represents an optimal hybrid approach. In this work, a 1/5 scale radio controlled vehicle with torque vectoring capability is designed and assembled. A high fidelity vehicle model is then constructed in the Matlab/Simulink environment to model test vehicle behavior. The test vehicle features independent suspension for
Petersen, Nicholas Conner, Robinette, Darrell
A Scalable and Accessible Remote Testing Framework for Automotive Development2026-01-0191To be published on 04/07/2026
With the rise of software-defined vehicles and the emergence of cyber threats to vehicular systems, developing teams are compelled to conduct extensive testing on both virtual and physical prototypes at an accelerated pace. This new development landscape necessitates diagnostic tools that are both precise and adaptable. However, proprietary systems dominate this field, often hindering accessibility for students and researchers due to high costs and restrictive licensing. This paper presents the design and implementation of an open-source, low-cost remote testing system tailored for automotive development and diagnostics. The proposed system utilizes Arduino and Raspberry Pi processing units, along with relay-based switching modules, to provide secure remote control of vehicle components through a web-based dashboard equipped with authentication, scheduling, and real-time synchronization capabilities. The tested prototype showcased robust scalability, secure session handling, and
Pries, Andrew, Mohammad, Utayba
The Impact of Industry 4.0 Implemenation on Operational Performance in Automtive Industry2026-01-0251To be published on 04/07/2026
This research investigates the impact of Industry 4.0 technologies on operational performance in the automotive industry, emphasizing the role of organizational culture and structure. As the sector evolves through the integration of smart technologies, understanding how these factors influence the relationship between Industry 4.0 implementation and operational improvement becomes crucial. Utilizing a mixed-methods approach, the study combines qualitative and quantitative methodologies to offer a comprehensive analysis. Qualitative data were collected through interviews with industry experts, uncovering insights into how organizational culture fosters innovation and responsiveness to change. The quantitative analysis focused on performance metrics from automotive companies that have adopted Industry 4.0 practices. The findings reveal a positive correlation between a supportive organizational culture and improved operational performance, highlighting that organizations with adaptive
El Safty, Shady Baher
Dual Battery Pack System for Multi-Voltage Electric Vehicle Operation2026-01-0392To be published on 04/07/2026
Electric Vehicles (EV) have become a major focus in the automotive industry. This paper introduces a propulsion system design, which supports the Wide Torque Band (WTB) concept to boost the power density of PM (permanent magnet) motors in EV Trucks resulting in performance, efficiency, and cost benefits. The WTB drive requires an 800V battery for vehicle operation. The chosen solution combines two 400V battery packs to create a selectable 400V/800V DC bus. The electrical hardware and controls to manage the dual battery packs in either series (800V) or parallel (400V) mode are presented in this paper. A prototype battery management system (BMS) along with existing production Voltage, Current, Temperature Monitoring (VITM) hardware are applied to perform mode switching, safety, and cell balancing. The success of this dual pack hardware enables high voltage dynamometer testing of a new 800V DU (Drive Unit) and inverters for EVs. The flexibility of switching between two voltage levels (400
Zhu, Yongjie, Lee, Chunhao, Gopalakrishnan, Suresh, Namuduri, Chandra
Assessing Energy Use, Cost, and Emissions of Small Regional Rail Vehicles: Methodology and Case Study on a German Track2026-01-0419To be published on 04/07/2026
The decarbonization of regional rail remains a pressing challenge in the European Union, where nearly half of the 200,000 km network remains unelectrified. Lightweight, single-car railbuses historically provided efficient service on such lines, but modern replacements are scarce. This work evaluates a standardized 30-ton, 16 m railbus platform for unelectrified regional service, focusing on propulsion system design and trade-offs between range, cost, and emissions. A MATLAB/Simulink drive-cycle model was developed to simulate energy consumption under realistic operating conditions. The Erfurt–Rennsteig route in Germany (130 km round trip, gradients up to 6 %) was selected as a representative case study. The model incorporates sub-models for traction motors, lithium-ion batteries (LFP and LTO), fuel storage, fuel cells, and ICE gensets across multiple fuel options (diesel, gasoline, methane, ethanol, methanol, HVO, FAME, and hydrogen). Battery lifetime is estimated using a combined
Ahrling, Christoffer, Tuner, Martin, Gainey, Brian, Torkiharchegani, Amir, Scharmach, Marcel, Hertel, Benedikt, ALAKÜLA, Mats
Using Unreal Engine Software for Driving Studies: Developing Documentation to Support Collaboration2026-01-0517To be published on 04/07/2026
Context: A wide range of applications already support driving simulator studies, including CARLA, miniSim, VI-GRADE, SCANeR, and others, each with distinct strengths and limitations. However, many academic institutions are turning to free and accessible gaming platforms such as Unreal Engine and Unity. In the Human Factors Group at the University of Michigan Transportation Research Institute (UMTRI), a multidisciplinary team of 19 students is developing an Unreal Engine-based driving simulator as a research tool to investigate the difficulty of driving roads and for other purposes. Problem: Until recently, the absence of consistent documentation has created major obstacles, including errors in code management and steep learning curves for new contributors. To address the issue, the team initiated a development freeze and established documentation standards tailored to Unreal Engine’s Blueprint framework, a graphical node-based programming language that poses organizational and learning
Erturk, Selay, Stimec, Luka, Xin, Jared Hongyi, Tasmaan, Ashley, Mitani, Kate, Zhang, Luya, Green, Paul
Development of a System for Regression Model Enhancement Utilizing Shape Generation AI2026-01-0499To be published on 04/07/2026
This study proposes a method to enhance regression models by shape generation AI. The approach focuses on automatically identifying regions within the design space where the model’s prediction accuracy is low. Once these regions are identified, new and diverse sample shapes are automatically generated by the shape generation AI and incorporated into the training dataset. The regression model is then retrained to improve its performance. By iteratively repeating this cycle of exploration, shape (FE mesh) generation, and model updating, the model’s reliability and accuracy across the entire design space are progressively enhanced. This method addresses data sparsity issues common in complex design tasks and enables better generalization to underrepresented regions. The effectiveness of the proposed system was demonstrated through a case study involving hood outer panels in automotive design. The results showed that adding AI-generated shapes improved prediction accuracy, particularly in
Taniguchi, Mashio
Optimizing EV Battery Models for Full Vehicle Simulations2026-01-0389To be published on 04/07/2026
The increasing adoption of electric vehicles (EVs) demands accurate yet computationally efficient battery models that can be integrated into full vehicle simulations. At the cell level, mechanical battery models often employ fine-scale elements to capture localized deformation and failure phenomena. While such detailed discretization enables high-fidelity predictions, it also imposes significant computational costs that become prohibitive when scaling up to pack-level or full-vehicle crash and durability simulations. This research addresses the challenge by systematically simplifying cell-level mechanical models to reduce computational burden while preserving predictive accuracy. We propose an approach in which larger elements and reduced complexity representations are introduced without compromising the model’s ability to replicate experimentally observed behaviors. The methodology emphasizes model validation against targeted loading conditions, ensuring that the essential mechanics
Sahraei, Elham, Brar, Bavneet, Parmar, Dhruv, Muralidharan, Umachandran
Adaptive Eco-Driving Feedback for Battery Electric Vehicles: A Reinforcement Learning Approach Using Context-Aware Policy Optimization2026-01-0165To be published on 04/07/2026
Energy efficiency and range optimization remain critical challenges to the widespread adoption of battery electric vehicles (BEVs). As a result, there is a growing demand for intelligent driver assistance systems that can extend the operating range and reduce range anxiety. This paper presents an adaptive eco-feedback and driver rating system based on proximal policy optimization (PPO) reinforcement learning, designed to support drivers with the target to reduce energy consumption and maximize driving range. The system processes real-time driving data, such as velocity, acceleration and powertrain status. Map data of high quality is used to anticipate traffic events, including but not limited to speed limits, curves, gradients, preceding vehicles and traffic lights. This contextual awareness allows the system to continuously assess driving behavior and provide personalized, context-aware visual feedback alongside a dynamic driving behavior rating. A PPO agent learns optimal feedback
Stocker, Christoph, Hirz, Mario, Martin, Michael, Kreis, Alexander, Stadler, Severin
Curvature-Based End-to-End Autonomous Vehicle Path Planning at Intersections2026-01-0045To be published on 04/07/2026
Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents an end-to-end approach to curvature prediction using deep neural networks trained on real-world driving data. We develop a comprehensive neural network architecture that directly maps high-dimensional sensor inputs to curvature predictions, eliminating the need for intermediate feature engineering steps. The model processes multi-modal inputs including vision features, vehicle state parameters, and environmental context through a deep learning pipeline. Our end-to-end approach demonstrates the feasibility of learning complex driving behaviors directly from raw sensor data, providing a more robust and generalization solution compared to traditional rule-based methods. The neural network architecture incorporates dropout regularization and adaptive learning rate scheduling to ensure stable training and optimal performance. Results show
Hajnorouzali, Yasaman, Wang, Hanchen, Li, Taozhe, Burch, Collin, Lee, Victoria, Tan, Lin, Arjmandzadeh, Ziba, Xu, Bin
Maintaining a Healthy Safety Culture in Autonomous Vehicle Development and Operations2026-01-0528To be published on 04/07/2026
The deployment of autonomous vehicles (AVs) requires not only technical excellence but also the cultivation of a robust organizational safety culture. While safety engineering standards and assurance processes provide essential structure, a culture that prioritizes learning, accountability, and continuous improvement is critical to ensuring public trust and operational resilience. This paper explores how UL 4600, the comprehensive safety standard for the evaluation of autonomous systems, and Safety Performance Indicators (SPIs) can be leveraged to build and sustain a Just Culture within an AV company. UL 4600 emphasizes systematic hazard identification, safety case construction, and ongoing evidence generation. When applied beyond compliance, it provides a framework for embedding safety considerations into day-to-day engineering and operational decision making. By mapping UL 4600 clauses into organizational processes and linking them with measurable SPIs—such as rates of system
Wagner, Michael, Gittleman, Michele
Control Strategy and Dynamic Characteristics of a Hydrogen Opposed-Cylinder Free-Piston Engine Generator2026-01-50172/23/2026
Free-piston engine generator (FPEG), as a novel energy conversion device, has the advantages of good fuel adaptability and high energy utilization. Combustion variation between cycles poses a significant challenge to the running control of an FPEG. A hierarchical control strategy, including motion, combustion, and generation power controllers, is designed in this paper to achieve the stable and efficient running of a hydrogen-fueled opposed-cylinder FPEG prototype. Piston motion is controlled by adjusting the generation current, which is adjusted through iterative learning using piston displacement feedback and adaptive control using piston velocity feedback. Generating power is regulated by controlling the throttle opening angle, which is adjusted through iterative learning. A multidisciplinary joint mathematical model is developed to simulate the dynamic characteristics and verify the control strategy. The simulation results reveals that the dead center position accuracy can be
Wang, Jiesheng, Liu, Liang, Xu, Zhaoping
SAE TOMORROW TODAY - Why SDV Strategy Needs Application Layer Innovation135562/20/2026
What's really holding back software-defined vehicles (SDVs) ... and where should automakers shift their focus? Listen in as we sit down with John Wall, President of QNX, to explore how a trusted, safety-certified foundation frees OEMs to innovate faster, collaborate more effectively, and deliver differentiated vehicles. Drawing on insights from the Under the Hood: SDV Developer Report, they unpack why automakers must shift away from maintaining complex, non-differentiating software and focus instead on application-layer innovation to better define brand identity and customer experience. You'll also learn how QNX is expanding into robotics, healthcare, and industrial automation with its secure, high-performance operating system. Whether you're fascinated by autonomous cars, collaborative robots, or the future of AI in physical systems, this episode is packed with insights on innovation, safety, and the power of partnerships. We'd love to hear from you. Share your comments, questions and
Patterson, Lori
Teaching Points for a Class on “Critical Issues in Composite Maintenance, Repair, and Overhaul”AIR5719B (Current)2/13/2026
This document is to be used as a checklist by curriculum developers to create courses or training for critical composite repair, maintenance, and overhaul issues. This document will not take the place of courses or training requirements for specific job roles of a composite repair technician, inspector, or engineer.
AMS CACRC Commercial Aircraft Composite Repair Committee
GSE Training VideosAIR4730A (Current)2/12/2026
This document provides information on the preparation and use of video for operational and maintenance training of qualified personnel associated with GSE.
AGE-3 Aircraft Ground Support Equipment Committee
Research on Online Learning Architecture for Engine Combustion Prediction on Multi-Platform Simulation03-19-01-00052/11/2026
To address the limitations of conventional offline data-driven models for engine parameter prediction in HIL testing, including poor generalization and inefficient use of supplementary data, this study develops an innovative cross-platform online learning architecture that integrates a pre-trained Python-based Wiebe parameter prediction model with high-fidelity MATLAB/Simulink engine simulation. The proposed framework incorporates five key functional modules (real-time data processing, online regression prediction, performance evaluation, incremental learning optimization, and engine simulation) to enable dynamic adaptation to varying engine conditions through seamless integration of Python’s incremental learning algorithms with Simulink’s simulation environment. By implementing a kth order polynomial decay learning rate strategy, the architecture significantly improves model convergence under limited training conditions while enhancing real-time performance and reliability in HIL
Wei, Mingxin, Shuai, Xiuyun, Wang, Zhaoyu, Zhao, Feiyang, Yu, Wenbin
SAE TOMORROW TODAY - Episode 318 - A New Era For Aerospace and Defense135542/5/2026
What if booking a trip into orbit were as easy as booking a flight on your phone? As the defense and aerospace industries evolve at unprecedented speed, what once felt like science fiction is fast becoming reality. For a deep dive into the latest innovations reshaping the skies, we welcomed back Todd Tuthill, Managing Partner, Rincon Aerospace, to discuss everything from the rise of reusable rockets to the democratization of access to low Earth orbit. Listen in as he shares an insider perspective on trends like blended wing aircraft, AI adoption in aerospace, and the coming wave of drone delivery and eVTOLs. It's an episode you don't want to miss! We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your
Patterson, Lori
Q&A26TOFHP02_112/1/2026
The advanced construction equipment packing the convention center halls and surrounding lots will understandably be the stars of the triennial CONEXPO trade show, taking place March 3-7 in Las Vegas. But the latest technologies in fluid power and motion control that help those machines operate efficiently will also command attention from showgoers. The Bosch Rexroth mobile hydraulics team will be on-site in a joint booth with partner HydraForce (Booth S80245), showcasing their current product portfolio. Rafael Cardoso, Bosch Rexroth engineering manager, mobile systems and software, expects to have conversations about advanced control and automation, “focused on the demand for smarter, software-driven control strategies that enhance precision, productivity, downtime reduction and operator assistance features.”
Gehm, Ryan
Rolls-Royce pursues pure methanol for marine engines26TOFHP02_052/1/2026
Rolls-Royce has successfully tested the world's first high-speed marine engine powered exclusively by methanol on its test bench in Friedrichshafen, Germany. The company began this engine-development journey six years ago when it gathered experts to determine what the future fuel of the maritime industry should be, according to Denise Kurtulus, senior vice president of global marine at Rolls-Royce. “For us, it's clear. It's methanol,” she said. Rolls-Royce worked with industry partners as part of the joint project meOHmare, which is funded by the German Federal Ministry for Economic Affairs and Energy. Injection system specialist Woodward L'Orange and the WTZ Roßlau technology and research center contributed their expertise. Their goal was to not only develop a comprehensive concept for a CO2-neutral marine engine based on green methanol, but also to run it on the test bench by the end of 2025.
Gehm, Ryan
Engineering a Coating for Bacteria-Free ProduceTBMG-545572/1/2026
Dr. Mustafa Akbulut, Professor of Chemical engineering, has teamed up with Horticultural Science Professor Luis Cisneros-Zevallos to engineer longer-lasting, bacteria-free produce.
Digital tech a Las Vegas headliner26TOFHP02_032/1/2026
With the rise of AI and other new digital technologies on the horizon, ACT Expo 2026 will be a crucial intersection for industry leaders to map out the route ahead. Since 2011, ACT Expo has served as a meeting point of technology and business discussions for the commercial vehicle industry. The 2026 show in Las Vegas (www.actexpo.com) is shaping up to be another important waypoint for the industry as it continues to grapple with new technologies, regulations and other significant challenges. This year's agenda program builds on ACT Expo's long-established emphasis on clean transportation and places an increased focus on the digital frontier, including AI, autonomy, connectivity and software-defined vehicles. Truck & Off-Highway Enginering interviewed Erik Neandross, president of the Clean Transportation Solutions group at TRC, about what topics are emerging as the main trends heading into 2026 and what he thinks will be some of the most important themes of the upcoming convention.
Wolfe, Matt
SAE TOMORROW TODAY - SAE: Where Meaningful Relationships Take Shape135531/30/2026
Curious how SAE can open doors in the automotive industry and beyond? Listen in as we sit down with Dean Case, Membership Director, SAE SoCal, whose 45-year journey in the automotive industry is packed with stories of racing, engineering, and community building. From joining SAE as a Cal Poly student to shaping the future of motorsports and electric vehicles at Mazda, Ford, and Nissan, learn how SAE International became a springboard to building lifelong connections and uncovering new opportunities. Whether you're a student, engineering professional, or automotive enthusiast, this conversation is packed with inspiration, practical advice, and proof that SAE provides a firm foundation for meaningful relationships that last. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders
Patterson, Lori
SAE TOMORROW TODAY - Advocating for Growth in the U.S. Auto Industry135521/27/2026
From tariffs to trade policies, today's automakers must respond to a variety of pressures to remain competitive in a changing manufacturing landscape. As the voice of international automakers in the United States, Autos Drive America is strengthening the automotive manufacturing by advocating for trade and investment policies that expand jobs and support economic growth in America. Listen in as we sit down with Jennifer Safavian, President & CEO, to discuss why a strong U.S. auto industry is vital for the nation's economy and how Autos Drive America is supporting integrated supply chains that keep American manufacturing competitive on the global stage. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on
Patterson, Lori
Fundamentals of Sustainable Aviation FuelsR-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
Yilmaz, Nadir
Conceptual Crash Analysis in Automotive Applications with Reduced Computational Effort2026-26-04511/16/2026
In the realm of automotive safety engineering, the demand for efficient and accurate crash simulations is ever-increasing. As finite element (FE) modeling of components becomes increasingly detailed and the availability of advanced material models improves, crash simulations for full vehicles can become time-consuming. Evaluating the crash performance of any vehicle subsystem requires structural simulations at different levels. While the design and configuration phase deals with a local simulation in representative load cases, full vehicle simulations are required later for a final digital proof of achieved requirements and development targets. This paper introduces a novel methodology for replacing full vehicle crash simulations, as required for a local view on the structural load path development, through segment-models. By adapting segment-model simulations, a significant reduction in computational time and resource usage is achieved, thereby optimizing CPU cluster performance and
Moncayo, David, Malipatil, Anand, Prasad, Rakesh, Kunnath, Allwin
Simulation Based Optimization of the NVH Characteristics in Modern High Speed Engine During Development Phase2026-26-03261/16/2026
Engine noise mitigation is paramount in powertrain development for enhanced performance and occupant comfort. Identifying NVH problems at the prototype stage leads to costly and time-consuming redesigns and modifications, potentially delaying the product launch. NVH simulations facilitate identification of noise and vibration sources, informing design modifications prior to physical prototyping. Early detection and resolution of NVH problems through simulation can significantly shorten the overall development cycle and multiple physical prototypes and costly redesigns. During NVH simulations, predicting and optimizing valvetrain and timing drive noise necessitates transfer of bearing, valve spring, and contact forces to NVH simulation models. Traditional simulations, involved continuous force data export and NVH model evaluation for each design variant, pose efficiency challenges. In this paper, an approach for preliminary assessment of dB level reductions across design iterations is
Rai, Ankur, Deshpande, Ajay Mahadeo, Yadav, Rakesh
Waste Heat Recovery Evaluation in FCEV Truck on Different Drive-Cycles2026-26-02591/16/2026
Growing global warming and the associated climate change have expedited the need for adoption of carbon-neutral technologies. The transportation sector accounts for ~ 25 % of total carbon emissions. Hydrogen (H2) is widely explored as an alternative for decarbonizing the transport sector. The application of H2 through PEM Fuel Cells is one of the available technologies for the trucking industry, due to their relatively higher efficiency (~50%) and power density. However, at present the cost of an FCEV truck is considerably higher than its diesel equivalent. Hence, new technologies either enabling cost reduction or efficiency improvement for FCEVs are imperative for their widespread adoption. FCEVs have a system efficiency around 40-60% implying that around half of the input energy is lost to the environment as waste heat. However, recapturing this significant amount of waste heat into useful work is a challenge. This paper discusses the feasibility of waste heat recovery (WHR
P V, Navaneeth
Bio-Inspired Decentralized Mapping for Autonomous Vehicles2026-26-06351/16/2026
The BioMap system represents a groundbreaking approach to collaborative mapping for autonomous vehicles, drawing inspiration from ant colony behavior and swarm intelligence. It implements a fully decentralized protocol where vehicles use virtual pheromone trails to mark areas of uncertainty, change, or importance, enabling efficient map consensus without centralized coordination. Key innovations include novel pheromone-based compression algorithms and bio-inspired consensus mechanisms that allow real-time adaptation to dynamic environments. In a simulated urban scenario (Town10HD), three vehicles achieved balanced load distribution (±1.8% variance) and comprehensive coverage of a 253.2m × 217.9m × 22.4m area. The final fused map contained 311 chunks with 72,785 particles and required only 10.4 MB of storage. Approximately 49.2% of map particles exceeded the pheromone significance threshold, indicating active importance marking, while no high-uncertainty regions remained. These results
Bhargav, Anirudh S, Subbarao, Chitrashree
Automotive Cybersecurity Incident Response (CSIR) Framework and Its Integration into OEM Processes2026-26-06131/16/2026
The exponential growth of connected and autonomous vehicles has significantly escalated cybersecurity threats, compelling automotive Original Equipment Manufacturers (OEMs) to adopt robust and structured Cybersecurity Incident Response (CSIR) capabilities. Current automotive cybersecurity regulations, such as AIS 189 in India and UNECE WP.29 globally, mandate precise frameworks for proactive threat detection, timely response, and comprehensive incident documentation. This research presents an innovative, comprehensive CSIR framework specifically tailored to integrate seamlessly into OEM cybersecurity management processes. Leveraging a combination of real-time monitoring systems, structured threat categorization methodologies, and integrated escalation and communication protocols, the proposed CSIR framework ensures efficient incident handling aligned with stringent regulatory compliance. The framework encompasses advanced methodologies including Vehicle Security Operations Center (VSOC
Chaudhary lng, Vikash, Desai, Manoj, Chatterjee, Avik, Chatterjee lng, Avik
Multi-Band Frequency Approach to Correlate Proving Ground and Road Simulator for an Electric Two-Wheeler2026-26-05561/16/2026
Real-world usage subjects two-wheelers to complex and varying dynamic loads, necessitating early-stage durability validation to ensure robust product development. Conducting a full life-cycle durability testing on proving grounds is time-consuming, extremely difficult for the riders involved, and costly, which is why accelerated testing using rigs such as the road simulator system have become a preferred approach. The use of road simulators necessitates, accurately measured inputs and precise simulation to ensure proper actuation of the rig, thereby enabling realistic representation of road undulations. This paper covers two important aspects essential for achieving an accurate and clear representation of road simulation in a 4-DOF road simulator, encompassing both longitudinal and vertical simulations at the front and rear of the vehicle. The first aspect involves the development of an instrumentation strategy for the two-wheeler, with careful identification of directionally sensitive
Ganju, Shubham, V, Vijayamirtharaj, Prasad, Sathish, R S, Mahenthran
Virtual Validation of ADAS Algorithms Using Driver-in-the-Loop Simulation2026-26-00501/16/2026
Driver-in-the-Loop (DIL) simulators have become crucial tools across automotive, aerospace, and maritime industries in enabling the evaluation of design concepts, testing of critical scenarios and provision of effective training in virtual environments. With the diverse applications of DIL simulators highlighting their significance in vehicle dynamics assessment, Advanced Driver Assistance Systems (ADAS) and autonomous vehicle development, testing of complex control systems is crucial for vehicle safety. By examining the current landscape of DIL simulator use cases, this paper critically focuses on Virtual Validation of ADAS algorithms by testing of repeatable scenarios and effect on driver response time through virtual stimuli of acoustic and optical warnings generated during simulation. To receive appropriate feedback from the driver, industrial grade actuators were integrated with a real-time controller, a high-performance workstation and simulation software called Virtual Test
Sharma, Chinmaya, Bhagat, Ajinkya, Kale, Jyoti Ganesh, Karle, Ujjwala
Prediction of REESS Factor for SOC Correction in Hybrid Vehicles2026-26-04711/16/2026
The regulatory mechanisms to measure emissions from automobiles have evolved drastically over the years. Certification of CO2 emissions is one of them. It is not only critical for environmental protection but can also invite heavy fines to OEMs, if not complied with. In homologation test of a Hybrid Vehicle, it is necessary to correct the measured CO2 to account for deviations in measurement from failed Start-Stop phase and difference between start and end State of Charge (SOC) of battery. The correction methodology is also applicable for vehicle simulation in Software-in-Loop environment and for analyzing vehicle test data for CO2 emissions with programmed digital tools. The focus of this paper is on the correction of CO2 derived from SOC delta in the WLTP homologation drive cycle. The battery energy delta due to difference in SOC between start and end of drive cycle should be converted to corresponding CO2 expended from Internal Combustion Engine. The resulting correction factor is
Gopinath, Shravanthi Poorigali, Khatod, Krishna
Adoptive Safety Solutions in Road Simulators for Commercial Vehicle Structural Validation2026-26-05331/16/2026
Road Simulators used to carry out accelerated structural durability validation of a vehicle. As a commercial vehicle manufacturer, for our commercial vehicles structural validation, we are using 8 poster road simulators. We use road load data, torture track data, synthetic profiles or road events as the input test data. From a mini 4 wheeler trucks to high capacity 8 wheeler truck, and any bus variant is being tested at road simulator. All the vehicle variants are tested with prescribed road and load conditions for the pre-determined life. Each wheel of the vehicle is positioned on the wheel pan of the hydraulic actuators so that each actuator excites the corresponding vibration data. The vehicle is being restrained as per the manufacturers recommendation. Manufacturer recommendations widely addresses the risks associated with the test rigs. In addition to that there are risks associated with the vehicle running, vehicle handling, vehicle positioning. For example, when durability test
Arumugam, Paramasivam, N, Gopi Kannan, N, Mahendra, Muthu kumar, Pandurangan, Singh, Laxman, Tiwari, Manish, V, Subash
Establishing a Scalable Virtual Framework for ADAS Validation Traffic Jam Pilot with Real-World Scene Integration2026-26-05061/16/2026
Advanced Driver Assistance Systems (ADAS) are instrumental in improving road safety and minimizing traffic-related incidents. However, their development and validation processes are resource-intensive, requiring substantial time, cost, and domain-specific expertise. Moreover, real-world testing introduces significant safety challenges. To address these issues, virtual simulation platforms offer high-fidelity environments for the secure and efficient testing of ADAS functions. This research presents a virtual validation framework for a Traffic Jam Pilot (TJP) algorithm utilizing such simulators. The framework features detailed models of camera and radar sensors, capturing essential parameters like detection range and field of view, alongside a vehicle plant model and road infrastructure modeling that includes elements such as curvature, slope, banking angles, and varying lane widths. A perception stack is developed using synthetic sensor data and is integrated with the TJP control
Agrawal, Mridul, Ithape, Avinash, Sharma, Prashant, Trivedi, Abhishek
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