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Mind the Gap: Quantifying Behavioral Fidelity in CARLA using Naturalistic Drone Data2026-01-0771To be published on 07/01/2026
The rapid advancement of autonomous driving systems, particularly SAE Levels 3 and 4, requires rigorous validation strategies to ensure safety during handover and fallback procedures. Although virtual environments provide the necessary scalability, the simulation-reality gap poses a significant risk to the reliability of human-in-the-loop (HITL) assessments. This paper presents a comprehensive, empirical investigation into the transferability of human driving behavior from the Car-Learning-to-Act (CARLA) simulation environment to real-world traffic situations. Using the exiD dataset, which comprises high-precision, drone-recorded, naturalistic trajectories from German highways, we systematically reconstructed distinct traffic scenarios. These scenarios included lane changes, cut-in maneuvers, and curve navigation. Twenty-five participants navigated these scenarios in a driving simulator. The study employed a robust, multidimensional analytical framework to compare the simulated
Rebling, Patrick, Alphan, Metehan, Nenninger, Philipp
Interpretable Tire Force Modeling for Formula Student Vehicle Dynamics and Lap Time Applications2026-01-0762To be published on 07/01/2026
Accurate tire models are a key enabler for vehicle dynamics simulation, control design, and lap time optimization, particularly in the context of Formula Student race cars, where vehicle setups and tire characteristics differ significantly from production vehicles. State-of-the-art tire models, such as Pacejka’s Magic Formula, provide high accuracy but rely on predefined functional structures and a large number of parameters, which limits interpretability and adaptability to new tire compounds. This paper presents a data-driven approach for deriving compact and physically interpretable tire force models using symbolic regression. The proposed method employs an intelligent tree search to systematically explore the space of mathematical expressions and identify models that optimally balance prediction accuracy and structural simplicity. In contrast to black-box machine learning approaches, the resulting models consist of explicit mathematical expressions that enable physical
Anselment, Marcel, Borowski, Julian, Rudolph, Stephan
Synergistic Integration of Advanced Combustion and Air-Path Technologies to Maximize ICE Efficiency in Hybrid Applications2026-01-0733To be published on 07/01/2026
This work investigates the efficiency potential of reciprocating internal combustion engines (ICE) for light-duty vehicle propulsion within the current state of the art and a five-year technological horizon. A validated one-dimensional engine model was extended to integrate multiple advanced technologies, including active prechambers for ultra-lean gasoline combustion, thermal coating, an electric turbocharger capable of supplementary turbine work recovery, variable compression ratio, and variable valve timing implementing a Miller cycle. These enhancements are systematically evaluated to explore their individual and combined effects on engine performance and efficiency. Parametric studies quantify the influence of heat transfer, air–fuel ratio, compression ratio, and other key variables on engine output, enabling the generation of enhanced engine performance maps. These maps reflect synergistic interactions between parameter sets and serve as predictive tools for assessing optimized
Pla, Benjamin, Dolz, Vicente, Serrano, Jose R., Gómez-Vilanova, Alejandro, Oliva, Fermin, Cardenas, Maria, Ariztegui, Javier
Methodological framework for the derivation of objective criteria for ride comfort assessment2026-01-0765To be published on 07/01/2026
In recent years, the automotive industry has faced increasing pressure to accelerate development cycles and reduce associated costs, while simultaneously meeting rising standards of ride comfort driven by the ongoing integration of autonomous driving functionalities. Consequently, it has become essential to ensure that ride comfort attains a high degree of maturity at the very early stages of the automotive development process. This necessitates the establishment of objective criteria that enable the reliable estimation of subjective ride comfort, utilizing simulation-based assessment methods. This study introduces a methodological framework designed to systematically translate the manufacturer specific subjective perception and assessment of ride comfort into objective descriptions using a dynamic driving simulator. The framework is conceived as a generic approach, enabling the comprehensive application to a wide spectrum of subjective ride comfort phenomena, while being specifically
Stroesser, Simon, Zwosta, Tobias, Angrick, Christian, Neubeck, Jens, Wagner, Andreas
Vibration Synthesis and Seat Dynamics Compensation for Single-Axis Seat Vibration in Driving Simulators2026-01-0673To be published on 06/20/2026
Realistic seat vibration reproduction is essential for delivering authentic haptic cues and enhancing driver immersion in driving simulators. Unlike direct playback of road recordings, simulator applications require vibration synthesis that responds interactively to driver inputs and vehicle dynamics. Reproducing these vibrations at the seat is complicated by actuator bandwidth, cross-axis coupling, and the dynamic behavior of the seat structure, which can alter the intended target response. This work presents vibration synthesis and seat dynamics compensation methods implemented on a dual-axis seat vibration reproduction system equipped with horizontal and vertical actuators. Frequency Response Functions (FRFs) were measured to characterize the system dynamics under both single-axis and dual-axis excitations, capturing direct responses as well as cross-axis effects. Run-up, coast-down, and combined excitation tests were then used to evaluate performance under more operational
Muthu Chaiphas, Joshua Daniel, Cuenca, Jacques, Bianciardi, Fabio, Colangeli, Claudio, Deckers, Elke, Denayer, Hervé, Janssens, Karl
Development and Standardization of a Compensation Method for Measurement Equipment Effects to Improve Blocked Force Accuracy in Physical Tires2026-01-0698To be published on 06/20/2026
Although the tire system is not developed in-house but supplied by tire manufacturers, it is a critical component that significantly affects overall vehicle performance, including NVH. To ensure the supplied tires meet vehicle performance targets, our company provides detailed development guidelines, defining block force—closely related to road noise—as a key NVH performance index. As virtual tire development is introduced, manufacturers will submit block force data from virtual models with initial tire designs, enabling early prediction of tire and vehicle-level performance. However, this approach is still under evaluation, with unresolved challenges such as the absence of clear block force target-setting methods and limited accuracy in current virtual evaluations. To address this, we propose using block force measurements from mass-produced or prototype tires as a basis for data-driven target setting and as a reference to enhance virtual model accuracy. Since block force data is
ahn, youngeun, Kang, Min Kyoo, Kim, Yong Dae, Reichart, Ron, Kim, Yonghun, Han, Inhyuk, JI, Unjin, Lee, JeongHwan
Active Learning Supported Metadata Extraction from NVH Simulations2026-01-0720To be published on 06/20/2026
Simulations can only be searched, reused and leveraged as trainings data for machine learning methods if they possess suitable metadata. Manually obtaining this metadata is time-consuming and requires expert knowledge. Consequently, there is often a lack of metadata, and this prohibits the reutilisation of simulation data. Therefore, automated frame works for metadata extraction are essential to quickly, effortlessly and cost-efficiently obtain this information. At present, there are no existing toolboxes for Finite-Element-Simulation data and current Large Language Models are not usable. Nevertheless, machine learning methods are a promising solution for this task. However, classical supervised machine learning methods cannot be used due to the high effort for labelling. Therefore, classical rule-based extraction algorithms are an alternative for fundamental metadata extraction. For more enhanced tasks, where more context is necessary, they are not usable. Active Learning is the
Luegmair, Marinus, Gröttrup PhD, Sören
Augmented Reality and Multimodal Interfaces for Astronaut Training and In-Orbit Operations2026-26-0796To be published on 06/01/2026
Augmented Reality (AR) and multimodal human–machine interfaces (HMI)—combining visual overlays, voice, gesture, eye-tracking, and biometric sensing—are now maturing into flight-relevant technologies capable of transforming astronaut training and in-orbit operations. These interfaces reduce task time, lower error rates, and mitigate cognitive load, thereby strengthening both crew autonomy and mission safety. For India, the timing is critical: Gaganyaan human spaceflight program is approaching decisive crewed missions in the late 2020s, and the Bharatiya Antariksh Station (BAS) is targeted for the 2030s. AR/MMI can be positioned not as an accessory but as a core enabling system for training throughput, real-time crew support, and resilient station operations. This paper provides (i) an overview of India’s current scenario, including Gaganyaan astronaut training ecosystems, IIT Madras collaborations on AR/VR, and Vyommitra humanoid readiness; (ii) synthesis of global case studies—NASA
Yadav, Anoop Singh
System and Method of Strain Emulation using a Beam Load Cell Test Setup2026-26-0743To be published on 06/01/2026
This novel method deals with emulation of Strain of a Structural Measurement System which includes software validation, acceptance tests and training. Current methods for simulating strain and force data for developing and verifying data acquisition (DAQ) software typically rely on costly electronic simulators or specialized hardware, making it challenging and expensive for developers, researchers, and small organizations to test their solutions under realistic conditions. To verify DAQ software, multiple specialized hardware solutions are deployed, that include Electronic Simulators, Commercial DAQ Modules and Hydraulic/Pneumatic test rigs. These technologies pose a challenge with limited flexibility and scalability options for small-scale prototyping, especially in budget-constrained scenarios. The sensors on these equipment may or may not be company approved inducing acceptance challenges. Our invention is an inexpensive, scalable, and mechanically simple alternative. Using a 3D
Murthy, Harsha, Bhat Venkatesh, Aditi, K Padmanabhan, Rahul, Madhu, Sheetal, Garag, Naveen
Automated Flight Maneuver Recognition for Intelligent Flight Simulator2026-26-0713To be published on 06/01/2026
The rapid growth in the number of aircrafts and pilots emphasizes the need for an AI-enabled training framework that can offer precise, automated examination of flight maneuvers, thereby optimizing the pilot’s training efficiency and minimizing iterations of the conduct of flight maneuvers thereby reducing the training time of the pilot for a flight. This paper introduces a novel hybrid methodology to intelligently detect manoeuvres performed during a flight using signal processing and machine learning techniques. A general framework is developed that can be used for all kinds of flight phases and aircraft types. The framework is implemented in a two-step approach. Firstly, Continuous Wavelet Transform (CWT) of the signals of interest for different manoeuvres are generated, to find the time at which a manoeuvre happens during a sortie. CWT looks at the energy levels in the transformed signal and uses smart thresholding technique to pick out only the parts with high energy, to extract
Sahu, Akash, C, Poornima, C, Aravindh, Kaliyari, Dushyant, TK, Khadeeja Nusrath
Predictive Obsolescence Management2026-26-0780To be published on 06/01/2026
This abstract is towards Aerospace Sustainability - End-of-Life Management for Aerospace Assets. In the field of Aerospace, which has a long Life-Cycle process [20-30Years], Component Obsolescence has become a major problem as it prevents Maintenance & sustenance of a product with committed life-cycle period. Obsolescence Management plays a vital role by deriving strategic plans on proactive obsolescence where the system needs to be supported for several decades. This abstract analyzes the obsolescence challenges in the Aviation industry especially in Avionics System impacted by component obsolescence and present the possible proactive obsolescence management in terms of Engineering, Technology, and business/cost elements. The Obsolescence problem cannot be avoided but the impact of obsolescence and mitigate the risk can be minimized by planning and managing response. The obsolescence risk assessment for the Bill Of Materials (BOM) is a paramount activity to manage obsolescence
Dharmananyala, Rohith, Munirathnam, Krishna, Marokeyfrancis, Joisyjose, Sadashivaiah, Nagesh, Kondamari, Harshitha
Integrating Human Factors and Predictive Modeling for Enhanced Aviation Safety: A Data-Driven Approach to Risk Mitigation and System Design2026-26-0795To be published on 06/01/2026
The evolution of the aviation industry from manual control to highly automated and autonomous systems has brought human factors to the forefront of safety, design, and operational efficiency. This paper presents an integrated analysis combining machine learning techniques with statistical modeling to assess the impact of human-system interaction on aviation incident outcomes. The first phase of this research involved a longitudinal dataset covering aircraft design evolution, training programs, and incident records. Using classification algorithms, including XGBoost, the study achieved an accuracy of 81.82% in predicting incident likelihood. Performance metrics such as F1-scores (0.85 for “Incident” and 0.78 for “No Incident”) highlighted the model’s robustness in identifying risk patterns associated with human factors and system complexity. In the second phase, 50 plus detailed aviation incident reports were analyzed using logistic regression, t-tests, and Cook’s Distance to identify
Valiyaparambil, Praveen
TRANSFORMING AIRCRAFT MRO WITH AGENTIC AI AND CONTEXT ENGINEERING2026-26-0788To be published on 06/01/2026
Aircraft Maintenance, Repair, and Overhaul (MRO) operations are highly complex, involving coordination among multiple stakeholders including airlines, MRO providers, OEMs, and regulatory authorities. A significant challenge in this space is managing unplanned events such as Aircraft on Ground (AOG) conditions, where delays can lead to major financial losses to airlines and safety risks. Engineers must quickly diagnose the damage, evaluate compliance against regulatory limits, coordinate with OEMs, and make critical decisions—all while navigating a fragmented ecosystem of disconnected systems, diverse document types, and time-sensitive processes. This paper presents a real-world, intelligent MRO solution that addresses these challenges through the use of Agentic AI and context engineering. The system is designed to automate and augment key MRO workflows such as damage detection, repair pathway selection, compliance verification, and supplier coordination. At its core, the solution is
Abburu, Sunitha, G.V.V., Ravi Kumar, Poovalingam, Sundaresan, Vaderahobli, Devaraja Holla
Energy Saving and Personalized Thermal Comfort Control Based on Reinforcement Learning and Decision Tree2026-99-1714To be published on 05/22/2026
Indoor thermal comfort is closely related to people’s health and work efficiency. Control systems typically consume a large amount of energy to maintain a comfortable thermal environment. Currently, reinforcement learning is widely applied to optimize thermal comfort control systems. However, existing research mainly adopts universal thermal comfort evaluation models that aim to satisfy the majority of people, which makes it difficult to quickly and accurately reflect the specific thermal comfort needs of individuals. As a result, the hot environment is neither comfortable nor energy-efficient in practical use. Therefore, this paper proposes an energy-saving personalized thermal comfort control method based on decision trees and reinforcement learning. First, decision tree learning is used to obtain an individual thermal comfort evaluation model from a small amount of historical data. Then, this individual comfort model is combined with energy consumption to form a reward function
Li, Xianying
Multi-Objective Reactive Power Optimization Method for the Distribution Grid with Distributed Generator2026-99-1702To be published on 05/22/2026
To enhance the economic efficiency and operational security of distribution grids, this paper develops a reactive power optimization model that incorporates distributed power sources. The model aims to minimize the costs of reactive-load compensation equipment, reduce voltage deviations, and lower network losses while satisfying operational constraints. To overcome the common drawbacks of the standard genetic algorithm—such as limited optimization precision and a tendency to converge to local optima—four improvement strategies are introduced. These include an enhanced encoding scheme, an initial population generated via opposition-based learning, an elite retention strategy, and the adaptive adjustment of crossover and mutation rates. Together, these modifications strengthen the algorithm’s global search capability. The proposed approach is validated using the IEEE30 node system. Compared with both the conventional genetic algorithm (GA) and an adaptive genetic algorithm, the improved
Wang, Maoze, Xiao, Wenyu, Liu, Yujia, Xu, Zhengwei, Xia, Yinyong
Research on Carbon Emission Reduction of Structural Assembly Construction Technology in Metro Station Engineering2026-99-1731To be published on 05/22/2026
To reduce the carbon emissions during the construction period of metro stations, two structural prefabrication schemes with varying prefabrication rates, based on the top-down construction method, were proposed and analyzed for their ability to study the carbon reduction potential of structural prefabrication construction technology in metro station construction, in comparison to traditional open-cut cast-in-place methods. A BIM model of the envelope and main structure of a metro station under construction in Qingdao was established to analyses the carbon emission impact factors of the metro station in terms of the consumption of materials, personnel, machinery, and transportation of each subcomponent project. The results show that the structural assembly construction technology can greatly reduce the work of support installation and dismantling, formwork installation and dismantling, and reinforced concrete pouring in the enclosure structure. With the prefabrication rate increasing
Gao, Guangyi, Wang, Zheyong, Dong, Silong, Gou, Jiayuan, Li, Yangqing, Zeng, Tiesen
Low Voltage Ride Through Strategy for Grid-Forming Converters Based on Adaptive Line Impedance and Current Constraint2026-99-0736To be published on 05/15/2026
To address the challenge of balancing voltage support and current limitation in grid-forming converters (GFCs)—a challenge induced by the uncontrollability of active power during transient faults in microgrids and weak grids—a low voltage ride through (LVRT) strategy utilizing adaptive virtual impedance with a variable resistance-to-inductance ratio is proposed. This strategy is designed to maximize the satisfaction of reactive power support and current limiting characteristics. By adaptively generating virtual impedance based on changing line parameters, the method enables adaptation to large disturbance conditions involving variations in line impedance and Short Circuit Ratio (SCR). First, a transient model of the virtual impedance for GFCs is established to clarify the transient instability mechanism. During the transient period, the power loop is controlled to prevent power angle divergence. Second, the influence mechanism of virtual impedance on reactive current and output current
Pang, Bo, Yang, Xiangzhen, Liu, Fang
Short-Term Sag Prediction Approach Based on XGBoost Combining Monotonic Constraints with EMA Features2026-99-0755To be published on 05/15/2026
The sag prediction of overhead ground wire is very important, because excessive sag will reduce the safety margin and endanger the transmission reliability, especially under extreme conditions such as heat wave and icing. To solve this problem, we propose a model that combines Exponential Moving Average (EMA) features and monotonic constraints XGBoost. By fusing multi-source meteorological data and sag monitoring data, sag-related features are extracted after outliers elimination and time alignment. Furthermore, EMA features are introduced to capture short-term fluctuations and time dependence. Monotonic constraints encode the physical prior knowledge of “the higher the temperature, the greater the sag”, which improves the physical interpretability. On the measured data, the model’s coefficient of determination (R2) is increased from 0.709 to 0.879, indicating that the short-term prediction accuracy is significantly improved. The combined application of EMA features and monotonic
Li, Xingyu, Lin, Shizhong, Shao, Zhan, Cui, Shicheng, Chen, Ruiduan, Luo, He
Automotive Engineering: May 202626AUTD05To be published on 05/14/2026
Forvia Hella ready with ADB, but NHTSA test stands in the way A demonstration ride shows the glare-free, game-changing power of adaptive driving beams, already available in Europe. An approval test from NHTSA is proving difficult for OEMs to pass. Sharper validation without brute force How CERTUS reshapes AV testing. Simulation-driven battery development From material selection to system-level performance. How simulation unlocks efficient and innovative motor design Engineers are still at the heart of the development process as simulation tools become great levelers. Engineering in the second quarter of the 21st Century Building a trusted digital twin and decision-centric simulation ecosystem. Engineering in the second quarter of the 21st Century Building a trusted digital twin and decision-centric simulation ecosystem. Independent materials testing for OEM validation How validated data provides the foundation for approved components. Editorial All the ways: Learning via print in a
The Impact of Transition Window Duration on Nominal Transitions of Control in Conditional Automation09-14-01-00165/4/2026
The objective of this research was to understand the impact of transition window duration on success and performance during nominal transitions from conditional driving automation (SAE level 3). Because the driver can be disengaged from driving when conditional driving automation is engaged, the central challenge is how to safely transition from automated control to human control. Past research from the literature on Level 3 Automated Driving Systems (L3 ADS) has focused on safety-critical event responses (e.g., responding to a hazard) and on automation that operates at high speeds, which is not representative of the systems currently deployed that operate in lower-speed traffic jam situations [4, 5]. This article presents an analysis of data from several transition-of-control studies with conditional driving automation in a high-fidelity driving simulator. A range of transition window durations were compared, and different transition-of-control behaviors were coded from video data
Gaspar, John, Ahmad, Omar, Schwarz, Chris, Fincannon, Thomas, Jerome, Christian
Increasing the Competency and Confidence of Older Drivers in the Use of Advanced Vehicle Technologies: The MOVETech Randomized Controlled Trial09-14-01-00135/4/2026
Programs that teach older drivers how to confidently and competently use advanced vehicle technologies (AVTs) are limited. The MOVETech study evaluated a training program specifically designed to teach older drivers how to use these technologies. Participants (n = 119) were randomized to the intervention (training program) or control group (brochure). The intervention involved an in-person classroom education session on the use and benefits of AVTs, and an on-road driving session where participants drove along a pre-defined route in a dual-controlled vehicle with instruction on AVT use by a driving instructor. All participants completed in-person and telephone assessments at baseline and 3 months. Driving performance and on-road AVT competence assessments were the primary outcomes. Self-reported driving confidence, competence, and confidence in use of AVT, crashes, citations, and count of vehicle damage were the secondary outcomes. Program fidelity was also evaluated using a checklist
Nguyen, Helen, Ren, Kerrie, Coxon, Kristy, Neville, Nick, O’Donnell, Joan, Cheal, Beth, Brown, Julie, Keay, Lisa
A Comparative Assessment of Fuel Cell and Battery Powertrains for Formula Student Electric Applications2026-01-50324/27/2026
The organizers of the most prominent Formula Student competitions have recently initiated a preliminary feasibility study on the application of hydrogen-based propulsion technologies in future single-seater race vehicles. These include electric powertrains with electrochemically converted hydrogen in fuel cell–powered vehicles, competing within the electric championship league. Based on the initial set of regulations, this study presents a model-based comparison between battery-powered (BEVs) and fuel cell–powered electric vehicles (FCVs) for Formula Student. The analysis is conducted using energy, power, and efficiency metrics from four candidate models of propulsion systems, implemented in an open and publicly available MATLAB script: two BEVs with varying battery capacities, and two FCVs employing different hybridization strategies. The aim of this study is to pinpoint and quantify the advantages and disadvantages of each technology for the Formula Student use case, and to identify
Martoccia, Lorenzo, Breda, Sebastiano, Fontanesi, Stefano, d’Adamo, Alessandro
WITHDRAWAL NOTICE2026-01-0517.014/7/2026
Fast and Robust CFD Simulation Method for Water Management in Automotive Interior Design2026-01-04874/7/2026
Understanding the fluid flow behavior over and into narrow gaps is crucial for many industrial applications, particularly in the automotive sector. Evaluating the potential of water ingress into narrow pathways and towards components is of great importance to design the water management of such components. The employment of CFD simulations supports the evaluation of potential water ingress into such gaps. Lagrangian based tools are used in a variety of simulation scenarios of fluid flow, especially due to their ability to easily simulate free surfaces with strong curvatures. In our previous work, a validated simulation setup was developed using the meshless simulation tool MESHFREE from Fraunhofer ITWM [8] for simulating water entering small gaps. Especially for industrial use cases, the computation time of several days is too expensive. Thus, we enhanced this approach to a fast and robust CFD simulation that realizes industrial use cases within appropriate time. The development was
Zrnic, Dino, Konstantinovics, Athena, Kospach, Alexander, Rugerri, Evelyn, Loy, Michael, Bäder, Dirk, Michel, Isabel
Data-Driven Calibration of Vehicle Simulations for Accurate Control Response in Development and Testing2026-01-00584/7/2026
With the steady increase in autonomous driving (AD) and advanced driver-assistance systems (ADAS) aimed at improving road safety and navigation efficiency, simulation tools have become a critical part of the development process, allowing systems to be tested while mitigating the risk of physical injury or property damage upon failure. Physics-based simulators are central to virtual vehicle development, yet their control responses often differ from real vehicles, potentially limiting the transfer of controllers and algorithms developed in simulation. As these simulations play an important role in the vehicle design and validation process, a critical question is how well their predicted behavior translates to real-world physical systems. This paper presents a calibration framework for an autonomous vehicle platform that learns the motion characteristics of an experimental vehicle and uses that knowledge to correct the actuator response of a simulation model. The model is trained by
Soloiu, Valentin, Sutton, Timothy, Mehrzed, Shaen, Lange, Robin, Zimmerman, Charles, Peralta Lopez, Guillermo
An Integrated Methodology for Sustainable Mobility Infrastructure Planning: The Atlanta Aerotropolis Case Study2026-01-00974/7/2026
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
New Era of Automotive Cybersecurity2026-01-00864/7/2026
The automotive industry is evolving from a reactive, independently self-determined approach to cybersecurity, complicated by a complex supply chain. Over time, this has resulted in a fragmented industry comprised of any number of proprietary solutions verses a standardized, regulated paradigm to facilitate a platform-oriented approach. This document, an update on collaborative work from the SAE Vehicle Electrical Hardware Security Task Force (TEVEES18B) and GlobalPlatform Automotive Task Force, outlines this transition strategy. An extensible number of additional examples of use cases of Global Platform Technologies are explored in this document.
Mazzara, Bill, Rawlings, Craig
Simulink DevOps Evolution: Bridging Interactive Platforms for Accelerated Model-Based Workflows2026-01-00804/7/2026
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
Simulation Study on a Neural Tire Model Based on Grouped Feature Learning2026-01-06454/7/2026
The tire model is a crucial component in the design of the K-characteristic of FSAE racing car suspensions, and directly influences the achievement of maximum cornering lateral force. Not only do the slip angle, vertical load, tire pressure, and camber angle affect the mechanical characteristics of the tire, but temperature is also an important influencing factor when FSAE vehicle tires operate at high speeds. However, the modeling process of traditional tire models based on temperature characteristics is often very complex. The FSAE tire test code (FSAE TTC) already has a large amount of official sample data, which provides a basis for data-driven neural network models. This study implemented a hybrid modeling methodology, constructing two cascaded feedforward neural networks that combine the physical interpretability of the Magic Formula tire model with the nonlinear approximation capabilities of neural networks. The first network model uses slip angle, vertical load, tire pressure
Liu, Xiyuan, Wang, Shenyao, Li, Mingyuan, Huang, Jiayu
Vehicle Test Platform with Experimentally Correlated Tire Model for Torque Vectoring Control2026-01-06224/7/2026
This paper presents a testing platform for the development of lateral stability control systems in independent motor electric vehicles (EVs). A 10 degree of freedom (DOF) vehicle simulation and a radio control test vehicle are constructed to enable controls validation scalable to full size vehicles. These vehicle simulations, or ‘digital twins’, have been widely adopted throughout the automotive industry due to 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 stability control. As a result, a simulation environment working in conjunction with a test vehicle represents an optimal hybrid approach. In this work, a high fidelity vehicle model is constructed in the Matlab/Simulink environment. To capture the effect of suspension, the digital twin is capable of
Petersen, Nicholas Conner, Robinette, Darrell
Integrating Optimization and Model Fidelity Selection Methods for Real-Time Driver-in-the-Loop Vehicle Simulators2026-01-06214/7/2026
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, and 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
A Shared Autonomy Architecture: Utilizing an Advanced Personalized Control System (APeCS) and Shared Autonomy Arbiter (SAB)2026-01-01474/7/2026
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.
From Reactive to Proactive: Applying Data Analysis and Automation to Mitigate PLC Communication Disruptions2026-01-02504/7/2026
PLCs (Programmable Logic Controllers) are critical devices in manufacturing, enabling the functioning of machinery and the transmission of build data to other systems in a production facility. Thus, maintaining uptime of these devices is crucial for ensuring that a facility can keep its line running, as even a few minutes of downtime can cost a company thousands in lost units and revenue. One particular pain point that causes downtime is broken communication between the devices and downstream applications, especially those that track orders and traceability. While advances in computing and digital technology have enabled the quick detection of lost signaling and the quick restoration of communication channels, there is much work left to be done in this realm. Besides causing downtime, an incident disrupts the flow of the line, leading to significant effort to restore normal production flow, even after resolution of the incident. In addition, the outage and the post-incident recovery
Jan, Jonathan, Preston, Joshua
Adaptive Eco-Driving Feedback for Battery Electric Vehicles: A Reinforcement Learning Approach Using Context-Aware Policy Optimization2026-01-01654/7/2026
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
Multi-Body Dynamics Control Integration Tool Application in Hybrid Vehicle System Identification2026-01-04174/7/2026
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
Lateral Stability and Torque Optimization Distribution for Distributed Drive Vehicles2026-01-06354/7/2026
To enhance the lateral stability and torque optimization of four-wheel hub motor distributed-drive vehicles under complex road conditions, a hierarchical control strategy for yaw stability is proposed. The upper-layer controller designs a yaw moment controller based on sliding mode control theory, establishing both a two-degree-of-freedom vehicle model and a seven-degree-of-freedom vehicle model to track the vehicle's desired yaw rate, desired sideslip angle, actual yaw rate, and actual sideslip angle. This enables the derivation of the corresponding additional yaw moment. The vehicle's operational state is analyzed using the phase plane method based on the sideslip angle and yaw rate, and the total additional yaw moment is computed through weighted calculations according to the identified state. Simultaneously, an unscented Kalman filter observer is implemented to improve the tracking accuracy of the actual yaw rate and actual sideslip angle in the seven-degree-of-freedom model. The
Shi, Cheng'ao, Liu, Bingsen, Zou, Xiaojun, Wang, Tao, Zhang, Ming
Techno-Economic Evaluation of Electrified Vehicle Options in Drayage Fleets2026-01-04544/7/2026
The electrification of drayage fleets offers potential economic and operational benefits, but the financial viability of electrified vehicles remains sensitive to battery cost, energy price, and fleet usage patterns. 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 (PB) and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study develops a unified techno-economic framework that jointly evaluates TCO, PB, and IRR to determine when electrified trucks become cost-effective alternatives to diesel trucks. Building on a previously developed cost modeling tool and using real-world telematics data from a Class 8 drayage fleet at the Port of Savannah, the analysis incorporates projected battery cost trajectories, electricity and diesel price trends, vehicle efficiency improvements, and multiple battery capacities
Sun, Ruixiao, Sujan, Vivek, Goulet, Nathan, Wang, Qixing
An Updated Correlation Study between the Full-Size Wind Tunnels of General Motors, Ford Motor Company, and FCA US LLC2026-01-06124/7/2026
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) - General Motors, Ford Motor Company, and FCA US LLC - 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
Machine Learning Modeling for Compressive Property Prediction of Foams2026-01-05124/7/2026
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, two sets of polyurethane (PU) foams of different densities were tested at four strain rates ranging from 0.01/s to 100/s. ML models capable of predicting compressive stress-strain response for a range of densities were developed. The models 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 density outside of the
M, Gokula Krishnan, Kavimani, Harish, Muppana, Sai Siddhartha, Savic, Vesna, Chavare, Sudeep, V S, Rajamanickam
Design, Fabrication, and Assessment of a User-Friendly Suspension System for a 2025 BAJA Vehicle2026-01-05884/7/2026
A suspension system was designed, fabricated, and tested following a systems design approach by an SAE Off-road Team from a North Midwest university. 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 Baja SAE Competition
Liu, Yucheng, Anderson, Matthew, Larson, Cody, Rodgers, Joshua, Seberger, Aaron, Letcher, Todd
A Scalable and Accessible Remote Testing Framework for Automotive Development2026-01-01914/7/2026
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
AI-Driven Data Consistency and Relationship Inference System for Agile Component Library Management2026-01-01094/7/2026
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
Mathematical Modeling of Trust Calibration for Human–Automation Safety2026-01-05304/7/2026
Trust calibration is vital for safe human–automation interaction but remains largely qualitative. This study develops multiple quantitative frameworks modeling trust as a function of automation reliability. Four progressive models of binary, linear, triangular, and logistic formalize the calibrated trust zone, defining where human reliance aligns with system performance. The framework corrects major misconceptions: that trust is purely qualitative, that low trust–low reliability states are acceptable, and that overtrust and distrust pose equal risk. It establishes a minimum reliability threshold for meaningful trust and identifies distrust as the safer default in high-risk contexts. A case study on an empirical observation of 32 AI applications plotted in the trust–reliability space confirms the analysis, revealing a consistent distrust tendency where reliability exceeds user confidence and other observations. By quantifying trust through reliability, the study reframes it as a
Wen, He, Mounir, Adil
Assessing Energy Use, Cost, and Emissions of Small Regional Rail Vehicles: Methodology and Case Study on a German Track2026-01-04194/7/2026
This work evaluates a standardized 30-ton, 16 m railbus platform optimized 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 and component performance 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 detailed 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 cycle- and calendar-aging model using the rainflow algorithm to extract charge cycles, while cost models include capital, fuel, maintenance, track fees, and staffing. Results show that battery-electric configurations achieve 1 kWh/km energy
Ahrling, Christoffer, Tuner, Martin, Gainey, Brian, Torkiharchegani, Amir, Scharmach, Marcel, Hertel, Benedikt, Alaküla, Mats
Dual Battery Pack System for Multi-Voltage Electric Vehicle Operation2026-01-03924/7/2026
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. A selectable 400V/800V battery system has been developed to support the WTB concept and enhance the power density of permanent-magnet motors in electric vehicles. The RESS comprises two 400V battery packs that can be charged at 400V in parallel or at 800V in series via a DC fast-charging (DCFC) connection. In this study, an 800V driving mode was additionally implemented. A prototype battery management system (BMS) along with existing production voltage, current and temperature measurement block 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
Zhu, Yongjie, Lee, Chunhao, Gopalakrishnan, Suresh, Namuduri, Chandra
Collaborative Development Framework for Electric-Based Software-Defined Vehicles – The European Research Project CODE4EV2026-01-01134/7/2026
The automotive industry is subject to major transformation initiated by societal and economical pull (reducing emissions, zero fatalities, European competitiveness) and accelerated by technology push (electrification, Cooperative, Connected and Automated Mobility (CCAM), and Cooperative Intelligent Transport Systems (C-ITS)). Following this trend, the Software-Defined Vehicle (SDV) targets the integration of software (SW) development methodologies for vehicle development as well as the value delivery shift toward customers along the entire lifecycle. It promises to create benefits for the car manufacturers in terms of faster time to market, easier update – as well as for the car users (private persons, fleet operators) in terms of personalized user experience, upgradability. At the same time, SDV requires a much more integrated and continuous development framework to enable different experts to efficiently develop and validate concurrently the different parts of the vehicles, to gather
Armengaud, Eric, Permann, Robert, Joergler, Sabrina, Barcelona, Miguel Angel, García, Laura, Rodriguez, José Manuel, Ivanov, Valentin, Li, Zhenqian, Nguyen Quoc, Trieu, Rodrigues, Sandy, Kowalczyk, Bogdan, Avdić Čaušević, Amra
Curvature-Based End-to-End Autonomous Vehicle Path Planning at Intersections2026-01-00454/7/2026
Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents a novel approach to curvature prediction using deep neural networks trained on GPS-derived ground truth data, rather than model predictions, providing a more accurate training signal that reflects actual vehicle motion. We develop a multi-modal neural network architecture with temporal GRU encoders that processes vision features, driver intent signals, historical curvature, and vehicle state parameters to predict curvature. A key innovation is the use of GPS-based actual curvature measurements computed from vehicle motion data (κ = ωz/v) as training supervision, enabling the model to learn from real-world driving patterns. The model is trained on 5,322 samples from real-world driving data collected on The University of Oklahoma’s Norman Campus using a Comma 3X device and a 2025 Nissan Leaf electric vehicle. Experimental results
Hajnorouzali, Yasaman, Wang, Hanchen, Li, Taozhe, Burch, Collin, Lee, Victoria, Tan, Lin, Arjmandzadeh, Ziba, Xu, Bin
Development of a System for Enhancing Regression Using Shape Generation AI for Hood Stiffness2026-01-04994/7/2026
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 Simulations2026-01-03894/7/2026
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, Parmar, Dhruv, Muralidharan, Umachandran
Collaborative Cybersecurity in a Shifting Automotive Supply Chain2026-01-00844/7/2026
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
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