Motivated by the inclusion of active flow control provisions in the 2026 Formula One regulations, and building upon previous studies of Trapped Vortex Cavity (TVC) implementation in inverted front wings, this paper investigates the effectiveness of TVC as a flow control mechanism applied to vehicle diffusers. Both active and passive configurations were considered for three diffuser geometries: a base straight-line diffuser, an inverted airfoil-shaped diffuser, and a diffuser inspired by a Formula One car. The study employed numerical simulations to evaluate the aerodynamic performance and the potential benefits of integrating TVC systems. Across all types of diffusers, the implementation of a circular TVC cavity resulted in a significant improvement in the lift-to-drag ratio (CL/CD). In the active flow control configuration, a 10% improvement was observed in the straight diffuser under a limited mass-flow rate. With optimized cavity positioning and radius, the airfoil-shaped and

Ming Kin, NG, Teschner, Tom-Robin

Thermal Management of Electric Microtruck Motors

2026-01-5026

4/9/2026

This study investigates the gradeability performance of an L7e-class electric micro truck from both vehicle dynamics and thermal perspectives. A 1D simulation model (Amesim) was developed and validated with multiple test results. Using inputs such as motor characteristics, drivetrain configuration, and vehicle mass, the model analyzed vehicle performance on a 20% gradient, calculating the required torque, achievable motor speed, and corresponding vehicle speed. Furthermore, gradeability limits were evaluated, and the effects of gear ratio and airflow rate around the air-cooled motor on both gradeability and thermal behavior were examined. The findings provide practical insights for improving the powertrain and cooling system design of lightweight electric vehicles. The results showed that selecting an appropriate gear ratio can enable the motor to operate more efficiently under demanding driving conditions. A 20% increase in the gear ratio was found to delay motor heating by up to 10

Turan, Azim, Kantaroğlu, Hasan Hüseyin, Akbaba, Mahir, Kasım, Recep Faruk, Yarar, Göktuğ

Personalized Driver Model for On-Ramp Merging Using LSTM

2026-01-0525

4/7/2026

This study develops a personalized driver model for expressway merging, embedding individual driving characteristics into automated longitudinal and lateral control via Long Short-Term Memory (LSTM) networks. Uniform assistance (Advanced Driver Assist System, ADAS) can feel uncomfortable when it does not match a driver’s style; we therefore target the merge maneuver—a safety-critical task requiring anticipation and timing—and test whether merging-related context improves model fidelity. Driving data were collected in a high-fidelity motion-base simulator across two merging scenarios (13 licensed drivers in total). Inputs comprised ego speed, Headway distance and relative speed to the lead vehicle, and geometric context variables (distance to the end of the acceleration lane and to the hard/soft nose); outputs were longitudinal and, in the cross-scenario study, lateral accelerations. Models were trained per driver and evaluated by root mean square error (RMSE). Including merging context

Shen, Shuncong, Hirose, Toshiya

New Era of Automotive Cybersecurity

2026-01-0086

4/7/2026

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

High Strain Rate Behaviors of Fiber Reinforced Composites in Principal and Off-Axis Directions

2026-01-0175

4/7/2026

Materials can exhibit significantly different mechanical behaviors compared to quasi-static conditions at high strain rates (> 100 s-1). High strain rate tests using setups such as SHPB (Split-Hopkinson Pressure Bar) can provide, in a practicable manner, the stress-strain relations for a material at high strain rates. Such properties are vitally needed for activities such as simulation-driven impact safety design of composite structures deployed in the form of automotive body parts and assembly, and other sub-systems. Although the behaviors of isotropic and ductile materials such as various metallic alloys appear to have been extensively studied and reported in literature, dependence of mechanical properties of fiber-reinforced composites especially in different off-axis directions are extremely difficult to come across. To fill up this void, a detailed experimental study has been carried out on high strain rate mechanical characterization of a laminated orthotropic glass/epoxy

Bawa, Prashant, Deb, Anindya, Barui, Ananya, Zhu, Feng

Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis

2026-01-0170

4/7/2026

Vision-language models (VLMs) are increasingly used in autonomous driving because they combine visual perception with language-based reasoning, supporting more interpretable decision-making, yet their robustness to physical adversarial attacks, especially whether such attacks transfer across different VLM architectures, is not well understood and poses a practical risk when attackers do not know which model a vehicle uses. We address this gap with a systematic cross-architecture study of adversarial transferability in VLM-based driving, evaluating three representative architectures (Dolphins, OmniDrive, and LeapVAD) using physically realizable patches placed on roadside infrastructure in both crosswalk and highway scenarios. Our transfer-matrix evaluation shows high cross-architecture effectiveness, with transfer rates of 73–91% (mean TR = 0.815 for crosswalk and 0.833 for highway) and sustained frame-level manipulation over 64.7–79.4% of the critical decision window even when patches

Fernandez, David, MohajerAnsari, Pedram, Salarpour, Amir, Pese, Mert D.

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

2026-01-0645

4/7/2026

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

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

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

2026-01-0510

4/7/2026

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

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

Speed Determination Using Audio Analysis of Dash Camera Video from Commercial Vehicle Tires Frequencies for Vehicle Accident Reconstruction

2026-01-0536

4/7/2026

Prior research has validated a reliable method for determining vehicle speed using audio recorded by cameras mounted in vehicles, specifically for rolling passenger vehicle tires. Passenger vehicle tires produce a frequency component directly correlated to vehicle speed when traveling on concrete roadways. However, prior research has not been conducted on audio for rolling commercial vehicle tires, which differ in construction from passenger vehicle tires. The stiffer Commercial tires produce audio signals on roadway surfaces that passenger vehicles tires did not when tested in the prior study. The current research concluded that commercial vehicle tires rolling on various roadway surfaces also generated a frequency that varied with vehicle speed. The purpose of this study was to outline, test, and confirm the source of the speed-dependent frequency and to develop a validated method for use in forensic applications. A modified version of the passenger vehicle tire equation from prior

Vega, Henry V., Cornetto, Anthony, Ngo, Long Justin, Hatab, Ziad, Hunter, Eric

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

2026-01-0650

4/7/2026

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

Liu, Xiyuan

Data-Driven Feedforward Compensation-Based Trajectory Tracking Control

2026-01-0028

4/7/2026

Advanced autonomous driving is a critical component in the intelligent development of new-generation electric vehicles. Research on reliable chassis control algorithms ensures the safety and stability of autonomous vehicles during operation. To enhance the control performance of autonomous vehicles and improve the accuracy of trajectory tracking, this paper proposes a data-driven feedforward compensation trajectory tracking control approach. By optimizing the design of the feedforward compensation loop, systematic errors and latency in the vehicle’s steering system are mitigated, thereby enhancing the precision and robustness of the control algorithm. Initially, the paper analyzes the control errors present when the vehicle responds to controller commands. Subsequently, the paper focuses on the steering angle errors in trajectory tracking, identifying and analyzing the most relevant factors. A time-delay neural network (TDNN) based on data-driven principles is designed to model and

Yang, Yijin, Yuan, Yin, Wang, Zhenfeng, Su, Ailin, Zhang, Zhijie, Lu, Yukun

Optimal Vehicle Sampling for Fleet-Wide Emissions Monitoring using Submodular Maximization

2026-01-0283

4/7/2026

This article addresses the problem of optimal vehicle sampling for fleet-wide in-use emissions monitoring, a necessity driven by the absence of direct emissions sensors in modern production vehicles and the variable impact of in-use changes and operational factors (mileage, time-in-service, workload) on emissions performance across a fleet. Recognizing that comprehensive fleet testing is impractical due to significant downtime and cost, we propose a novel approach to identify a small, yet optimally informative subset of vehicles for sampling. The proposed approach leverages submodular function maximization, a technique rooted in optimal experimental design, specifically D-optimal design, to maximize the determinant of the information matrix (e.g., of XTX, where X is the regressor/design matrix in the case of a linear in parameters model). This approach ensures that the collected data yields maximum information for refining and building accurate models for emissions changes. We compare

Zhang, Jiadi, Li, Xiao, Kolmanovsky, Ilya, Tsutsumi, Munecika, Nakada, Hayato

High-Speed Flashing in the Age of the Software-Defined Vehicle

2026-01-0112

4/7/2026

In recent years, the use of software-defined platforms has become increasingly prevalent. As a result, flashing ECUs has become an important factor in ensuring efficiency, quality, and compliance in vehicle production. Conventional approaches, such as final end-of-line flashing, are increasingly unsuitable for the growing amounts of data, complex dependencies, mixed physics and protocols, and traceability requirements. This SAE paper presents the current trends and challenges in ECU flashing. It highlights the impact of the exponential growth in software payloads and the necessary migration to offline and parallel workflows. This can only be achieved through closer integration with automated and robot-assisted production, considering the requirements of cybersecurity and verifiability. It also addresses the shift toward end-to-end flashing ecosystems, where updates are performed consistently from a single source covering the assembly line, warehouses, yards, workshops, and over-the-air

Böhlen, Boris, Budak, Oguz, Wells, Michael

Impact of User-Adjustable Vehicle-Dynamics Tuning on Vehicle Durability

2026-01-0194

4/7/2026

Software-defined vehicles offer customers a greater degree of customization of vehicle controls and driving experience. One such feature is user-adjustable tuning of vehicle ride and handling, where customers can vary ride height, damper stiffness, front-rear torque balance, and other aspects of vehicle dynamics. While promising a great customer experience, such a feature can expose the vehicle to a wider range of structural loads than those in the nominal design condition, particularly when such tuning is extended to cover spirited “sport” mode driving, off-road driving, etc. In this paper we present a novel methodology combining Road Load Data Acquisition (RLDA) data and real-world telemetry data to estimate the impact of user-adjustable vehicle-dynamics tuning on structural durability. In doing so, the method combines the physics of damage accumulation (from RLDA data) with user behavior (from telemetry data) to present an accurate assessment of the impact on durability, moving

Demiri, Albion, Ramakrishnan, Sankaran, White, Dylan, Khapane, Prashant, Borton, Zackery

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

2026-01-0423

4/7/2026

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

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

Machine Learning Modeling for Compressive Property Prediction of Foams

2026-01-0512

4/7/2026

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

Data-Driven Study on Chassis Suspension Performance Degradation

2026-01-0582

4/7/2026

The performance of chassis suspension mechanisms critically affects vehicle handling, ride comfort, and safety. Implementing real-time health monitoring for chassis systems contributes to preventing severe consequences such as increased body roll or loss of handling stability caused by shock absorber softening or spring stiffness degradation under deteriorating operating conditions, while circumventing the substantial costs associated with professional facility-based chassis inspections. With the rapid development of sensing and data analytics technologies, data-driven approaches are increasingly used in health monitoring. This study aims to achieve online monitoring of chassis suspension performance degradation using a deep neural network (DNN). First, a half-car model incorporating both vertical and pitch motions was established to simulate bumpy road conditions, with the aim of constructing a dataset that includes key vehicle suspension parameters and vehicle states related to their

Liao, Yinsheng, Lei, Yisong, Su, Ailin, Wang, Zhenfeng, Shi, Shuai, Zhang, Lei, Zhang, Junzhi, Ma, Changye

Developing Safety Case for Automated Driving following UL 4600 & ISO 5083

2026-01-0527

4/7/2026

Automated Driving Systems (ADS) rely on AI algorithms, machine learning, and sensor fusion to perform autonomous driving tasks. Safety challenges arise due to the probabilistic behavior of AI/ML algorithms and the need to ensure safety within defined Operational Design Domains (ODDs). Traditional standards such as ISO 26262[3] (Functional Safety) and ISO 21448[4] (SOTIF) address hardware and software failures or functional deficiencies but are insufficient for higher-level autonomous systems (SAE Levels 3–5). To close this gap, additional standards such as UL 4600[1] and ISO 5083[2] provide complementary frameworks for ADS safety assurance. UL 4600[1] establishes a claim-based safety case encompassing the vehicle, infrastructure, and processes, emphasizing structured arguments supported by evidence and reasoning. It offers guidance on autonomy functions, V & V, tool qualification, dependability, and safety culture. ISO 5083[2] focuses on design, verification, and validation of ADS

Mudunuri, Venkateswara Raju, Almasri, Hossam, Fan, Hsing-Hua

Development of Technology to Distinguish Contaminants using LiDAR Near-Field Point Cloud based on Real-Field Data

2026-01-0020

4/7/2026

Reliable environmental perception under adverse and contaminated conditions is a critical requirement for autonomous driving systems. Although LiDAR sensors play a central role in such perception, their performance is significantly degraded by surface contamination caused by environmental factors such as rain, snow, dust, anti-icing materials, and bug splatter impacts. However, most existing public datasets and prior studies rely on simulated or laboratory-generated contamination scenarios, which limit their applicability to real-world autonomous driving. To address this gap, we construct a large-scale real-world dataset collected from approximately 22,000 km of on-road driving across diverse regions of the United States, covering a wide range of naturally occurring environmental contamination conditions. The dataset was acquired using a multimodal sensing platform integrating LiDAR, perception RGB cameras, infrared camera sensors, and external monitoring systems, enabling

Kim, Hunjae

An Updated Correlation Study between the Full-Size Wind Tunnels of General Motors, Ford Motor Company, and FCA US LLC

2026-01-0612

4/7/2026

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

ANN and RSM-Driven Optimization of Tribological Characteristics of CuO-Doped Castor Oil Bio-Lubricants

2026-01-0353

4/7/2026

The development of renewable and eco-friendly bio-lubricants can address the environmental challenges posed by petroleum-based lubricants. At the same time, it is possible to improve the tribological properties of lubricants through alternative sources. To overcome these problems, castor oil is a potential basis for bio-lubricants due to its high viscosity, natural lubricity, and biodegradability. In the current work, castor oil was chemically modified by the epoxidation process. This process has improved the tribological properties of castor oil through the epoxidation method. In this method, the presence of hydrogen peroxide acts as an oxidizing agent while sulfuric acid serves as a catalyst, converting the unsaturated double bonds present in the oil into oxirane rings. At the same time, this modification enhanced the thermal stability and tribological applications in harsh operating conditions. The tribological performance of the epoxidized castor oil, further reinforced with copper

Prabhakaran, J, Pali, Harveer Singh, Singh, Nishant Kumar

From CFD Simulations to Machine Learning: A Comparative Study of ML Tools for Aerodynamic Drag Prediction

2026-01-0598

4/7/2026

This study presents a comparative assessment of two machine learning approaches for predicting aerodynamic drag coefficients (Cd) in automotive vehicle designs using data derived from computational fluid dynamics (CFD) simulations. The first approach employs traditional regression models trained on structured parametric data generated through controlled geometric variations, while the second approach integrates unstructured point-cloud geometry with structured metadata using a multi-modal deep learning framework. Both methods are evaluated within their respective contexts to understand their strengths, limitations and potential roles in automotive aerodynamic workflows. Rather than identifying a single best approach, the study highlights how these methods address different design needs and resource constraints, providing insights for future hybrid strategies that combine interpretability with geometric sensitivity. The work aims to establish a foundation for data-driven aerodynamic

Kumar, Gaurav, Khanna, Susheel

Lap Time Performance Sensitivity to Tire Model Parameters: An Optimal Control Framework for Model Selection and Calibration

2026-01-0214

4/7/2026

The push for vehicle development through virtual prototyping and testing in motorsports highlights the critical challenge of tire model selection and calibration, especially when vehicle dynamics must be accurately captured. The calibration process for tire models such as the Pacejka Magic Formula (MF) relies on parameter identification and experimental data fitting. While optimization algorithms have been implemented to calibrate tire models, few studies explore the effects of parameter selection on overall vehicle performance, complicating prioritization for the vehicle’s modeling and simulation strategy. To bridge this gap, this paper leverages optimal control methods to quantify how the variability of MF tire model parameters propagates to the overall vehicle model and impacts lap time prediction accuracy. To achieve this, a subset of parameters critical to combined slip of the MF tire model are varied through a Design of Experiments (DOE). These variations are executed on a flat

Zarate Villazon, Angel M., Brown, Ian, Balchanos, Michael, Mavris, Dimitri

The Golden Tester

2026-01-0092

4/7/2026

The objective of this paper is to understand the effort required to integrate the hardware and software of in-vehicle cybersecurity systems. The in-vehicle cybersecurity method discussed is the SAE J1939-91C, which involves Network formation, Rekeying, and secure Message Exchange between Electronic Control Units (ECUs). The SAE J1939-91C network security protocol operates over a CAN-FD network to perform necessary cryptographic operations and key generation. To evaluate the method, test vectors were created to validate SAE J1939-91C key generations and cryptographic operations on the simulated ECU in-vehicle network system hardware (such as the Beacon or Pi devices). We introduce a lightweight, transport-agnostic benchmark comprising deterministic AES-CMAC test vectors and a simple verification utility, requiring no specialized hardware or build system. This minimal artifact set enables reproducible and machine-parsable validation of SAE J1939-91C security across diverse lab

Zachos, Mark, Medam, Krishna Teja

ZSI Impedance Source Inverter Integrated XFC (Extreme Fast Charging) for Electric Vehicle

2026-01-0438

4/7/2026

This paper explores the application of an Improved Enhanced-Boost Quasi-Z-Source Inverter in AC-connected extreme fast charging (XFC) stations for electric vehicles (EVs), aiming to reduce conversion stages and enhance system efficiency. AC-connected XFCs offer superior reliability compared to DC-connected systems due to better fault tolerance and reduced sensitivity to power fluctuations but traditionally suffer from increased complexity and reduced efficiency due to multiple conversion stages. The proposed inverter addresses this by combining DC-DC and DC-AC conversion into a single stage, simplifying the system, decreasing losses, and improving efficiency. Furthermore, this research investigates the use of Spiking Neural Networks (SNNs) for generating the precise pulse width modulation (PWM) signals required for the Quasi-Z-Source Inverter. SNNs offer potential advantages in terms of dynamic response and adaptability compared to traditional PWM techniques, allowing for optimized

Saliesh, Dileep, Sanaboyina, Prudhvi, Chhagar, Rohnitsingh, Satyanarayan, Swapna

From Prediction to Design: Using Context-Aware Graph Neural Networks and Explainable AI to Anticipate Transfer-of-Control

2026-01-0049

4/7/2026

Drivers often interact with partial automation (SAE Level 2) systems, initiating transfer of control (TOC) either by handing control over to the automation or by taking it back. Accurately predicting these interactions may inform the design of future automation systems that adapt proactively to the operating context, enhance comfort, and ultimately may improve safety. We present a context-aware framework that generates a unified driver–vehicle–environment representation by fusing data from in-cabin video of the driver and of the forward roadway with vehicle kinematics, driver glance, and hands-on-wheel behaviors. This representation was encoded in a hierarchical Graph Neural Network that classified driver-initiated TOCs to: (i) Manual-to-automation and (ii) Automation-to-manual transitions and predicted time-to-TOC. Shapley-based explainable AI was used to quantify how the importance of behavioral, contextual, and kinematic cues evolved in the seconds preceding a TOC. Analysis of a

Zhao, Zhouqiao, Gershon, Pnina

The Cure for Overgrown Process Garden or Why Engineers Have Given Up with ASPICE

2026-01-0067

4/7/2026

The useability of development processes in the automotive sector has decreased in the past years to a level at which their application and true benefit to is being questioned. Such degradation can be attributed to new additions to the processes and introduction of FuSa and Cybersecurity standards. The processes try to keep up with the shift from the traditional ‘plan–implement–test–roll-out' methodology to more agile methods. In addition, process departments typically in charge of these processes, focus on compliance to the letter of the standard to achieve certification, often with little thought to the actual implementation and the process they will be used by their engineering teams. Process growth to meet the needs of new and more complex technologies often mandates the use of new tools, which if implemented incorrectly can lead to unnecessary bureaucracy and additional overheads. Furthermore, the language of these new processes is in a form from assessor, making it difficult for

Weber, Matthias, Kmiec, Mateusz, Romijn, Marcel, Nedkov, Detelin

LiDAR-Driven A* Path Planning with Dynamic Obstacle Avoidance for Autonomous Navigation in ROS

2026-01-0030

4/7/2026

Autonomous mobile robots are becoming a key part of everyday operations in industries like manufacturing, logistics, healthcare, and even home assistance. A core requirement for these robots is the ability to navigate efficiently and reliably within their operating environments. To do this automation, the robot needs to understand its surroundings, figure out where it is on a map, and find a safe path from where it is to where it needs to go without bumping into anything. This paper presents an effective grid-based path planning solution for autonomous indoor navigation with a mobile robot. Achieving reliable and collision-free navigation in changing environments is a major challenge for mobile robotics. This is especially true when obstacles can appear unexpectedly, requiring quick re-planning. To tackle this issue, an improved A* algorithm was implemented to work closely with LiDAR for environmental awareness. The improved algorithm was added to the robot’s navigation system, and

Devaraj, Sriram Sanjeev, Park, Jungme

Ambient and Initial Temperature Effects on Battery Electric Vehicle (BEV) Range and Energy Consumption Rate Modeled in FASTSim

2026-01-0277

4/7/2026

Ambient and initial temperatures significantly impact the energy consumption rate (ECR) of battery electric vehicles (BEVs) due to auxiliary loads and the temperature dependence of battery efficiency. This study introduces a streamlined, physics-based thermal modeling approach within the FASTSim tool that bridges the gap between oversimplified constant-load models and computationally expensive high-fidelity simulations. By employing a lumped thermal mass framework, the model captures fundamental energy balances and critical non-linear energy penalties while maintaining the computational efficiency required for expansive sensitivity studies. The simulations evaluated a compact BEV hatchback with a resistive heater over city (UDDS) and highway (HWFET) test cycles. Compared to a 22°C initial and ambient temperature baseline, a -7°C initial/ambient temperature resulted in a 221% increase in the ECR for the city cycle and a 100% increase for the highway cycle. Conversely, a 45°C initial

Baker, Chad, Steuteville, Robin, Holden, Jake, Gonder, Jeffrey, Carow, Kyle

Leveraging Machine Learning for Occupant Body Size Estimation via B-Pillar Seatbelt Systems

2026-01-0559

4/7/2026

Occupant body size in vehicles varies significantly, encompassing differences in height, mass, and overall body composition. Adaptive restraint systems, featuring adjustable parameters such as belt load limiters, steering column load limiters and stroke, seat pan stiffness, and airbag pressure, can offer more equitable protection tailored to individual body sizes. In this study, a test rig modeled after the Volvo XC90 (2016) was used to collect data from 46 participants who were dressed in typical summer clothing and seated upright, without slouching or leaning sideways. Stepwise adjustments of the seat pan and seatback were performed. The collected measurements include seat pan movements (front-back and up-down), seatback recline, and key seatbelt-related parameters, such as belt payout length, D-ring angle, lap belt length, and buckle tension. The collected data was then used to train machine learning models to predict individual occupant characteristics: standing height, mass, and

Wang, Da, Ahmed, Jawwad, Rowe, Mike, Brase, Dan

Techno-Economic Evaluation of Electrified Vehicle Options in Drayage Fleets

2026-01-0454

4/7/2026

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

Adaptive Torque Vector Control for Distributed Drive Intelligent Electric Vehicles Using Model and Data Driven Approaches

2026-01-0628

4/7/2026

To effectively improve the performance of chassis control of distributed drive intelligent electric vehicles (EVs) under difference road conditions, especially in combing road information and chassis control for improving road handling and ride comfort, is a challenging task for the distributed drive intelligent EVs. Simultaneously, inaccurate chassis control and uncertainty with system input, are always existing, e.g., varying road input or control parameters. Due to the higher fatality rate caused by variable factors, how to precisely chose and enforce the reasonable chassis control strategy of distributed drive intelligent EVs become a hot topic in both academia and industry. To issue the above mentioned, an adaptive torque vector hierarchical controller based on road level and adhesion is proposed, which optimizes the comprehensive. First, combined with the characteristic of the unbalance dynamic force caused by the air gap between the stator and the rotor of the in-wheel motor, a

Wang, Zhenfeng, Zhao, Gaoming, Zhang, Zhijie, Zhou, Zitao, Huang, Taishuo, Ma, Changye

Experimental Study on Macroscopic Characteristics of Ammonia Spray under Flash Boiling and Non-Flash Boiling Conditions with Large-Diameter Single-Hole Nozzles

2026-01-0339

4/7/2026

Ammonia is regarded as a potential alternative fuel, and its spray characteristics are crucial for efficient combustion in engines. For large-bore engines suitable for heavy-duty vehicles or ships, the adoption of large-diameter nozzles is expected to ensure an appropriate fuel flow rate while improving fuel-air mixing efficiency, thereby enhancing in-cylinder combustion performance. This paper conducted an experimental study on the characteristics of liquid ammonia sprays under wide thermodynamic conditions, a wide range of injection pressures, and a wide range of nozzle diameters. The study found that at room temperature, as the ambient pressure increases from 0.1 MPa to 4 MPa, the development of spray penetration slows down. However, at 0.05 MPa, the radial expansion of the near-field spray is greater, and the penetration is slightly behind that at 0.1 MPa. The liquid penetration increases with the increase in ambient temperature. This was because the increase in temperature reduced

Liu, Yi, Zhong, Jie, Hu, Yuchen, Zhu, Wuzhe, Yunliang, Qi, Qingchu, Chen, Wang, Zhi

Design and Evaluation of a Deep Neural Network Model for Prediction of Diesel Equilibrium Combustion Products and Characteristics

2026-01-0293

4/7/2026

Accurate prediction of equilibrium combustion products and thermodynamic properties is essential for optimizing engine performance, enhancing combustion efficiency, and reducing emissions in diesel-powered systems. Traditional methods for combustion modeling often involve solving complex chemical equilibrium equations or thermodynamic relations, which could be computationally expensive and time-consuming. In this study, we present a data-driven approach using a deep neural network (DNN) model to predict the equilibrium combustion products and key thermodynamic characteristics of diesel under varying thermodynamic conditions. The proposed DNN model is trained on a comprehensive dataset generated from equilibrium calculations. The inputs include pressure, temperature, and equivalence ratio, covering a relatively wide range to encompass diesel equilibrium combustion under various conditions. Outputs are equilibrium combustion products and thermodynamic properties, including enthalpy

Ji, Huangchang, Wang, Kai, Guo, Zhefeng, Han, Yang, Lee, Timothy

Accelerated Computed Tomography for Spray Characterization

2026-01-0341

4/7/2026

Computed tomography (CT) is a valuable diagnostic technique for visualizing spray plume direction and assessing mixture quality within combustion chambers under engine-relevant conditions. High-speed extinction imaging followed by tomographic reconstruction enables temporally and spatially resolved measurements of liquid volume fraction and plume evolution in multi-plume sprays. Traditionally, tomographic reconstruction requires capturing multiple angular views by rotating the injector and averaging over numerous injections to ensure statistical convergence. This process is time-intensive, particularly due to the large volume of data acquisition and the corresponding delays in data saving, particularly when acquiring many injections per view angle. In this study, we investigate the minimum number of injections required to achieve sufficient CT image quality, thereby significantly reducing experimental time. Two injectors are evaluated: a symmetric 8-hole Spray M injector from the

Yi, Junghwa, Wan, Kevin, Pickett, Lyle

Development of a Tire Blowout Experimental Platform for Dynamics and Control Validation

2026-01-0202

4/7/2026

Tires are critical to vehicle dynamics, transmitting traction, braking, and cornering forces to the road. A tire blowout, the sudden and rapid loss of inflation pressure due to puncture or structural failure, can cause severe instability, rollover, or collisions. Understanding vehicle response during blowout events is essential for developing robust safety systems and control strategies. Earlier developed simulation models are used to study and understand vehicle behavior during blowouts, but there is a lack of on-road testing platforms to validate these models experimentally. In this paper, an experimental platform integrating a tire blowout device and an instrumentation system has been developed to address this gap. The blowout device consists of multiple solenoid valves mounted on the wheel surface and powered by a 12V power supply. All valves can be triggered at the same time using an RF remote, producing rapid and synchronized deflation. As an extension of this implementation, an

Kanthala, Maha Vishnu Vardhan Reddy, Krishnakumar, Ashwin, Lin, Wen-Chiao, Chen, Yan

Prediction of Battery Self-Discharge Under Sparse Data Using Physics-Guided Machine Learning

2026-01-0155

4/7/2026

Predicting battery self-discharge across wide temperature ranges and extended durations remains a significant challenge due to the scarcity of physical test data, which is typically limited to a few temperature points and short observation windows. This limitation complicates generalization and increases the risk of inaccurate extrapolation. To address this, the paper introduces a machine learning–based framework designed to predict self-discharge behavior under diverse thermal conditions and longtime horizons. Multiple modeling strategies are examined, including feedforward neural networks, long short-term memory (LSTM) architectures, synthetic data generation, and physics-informed integration of governing equations. Particular emphasis is placed on hybrid and physics-regularized models that embed first-principles relationships to guide extrapolation beyond the observed data domain. This approach mitigates the inherent instability and potential errors associated with purely data

Chavare, Sudeep, Zeng, Yangbing, Muppana, Sai Siddhartha, Miao, Yong, Xu, Simon

A Cooperative AFS/DYC Lateral Stability Control for FWDIEV Based on Dissipative Energy Method

2026-01-0619

4/7/2026

To enhance the lateral stability of four-wheel-drive intelligent electric vehicles (FWDIEV) under extreme operating conditions, this paper proposes a cooperative control strategy integrating active front steering (AFS) and direct yaw moment control (DYC) based on dissipative energy method. A nonlinear three-degree-of-freedom vehicle model is established to analyze the evolution of the vehicle state phase trajectory. A quantitative lateral stability index is constructed using dissipative energy to accurately evaluate the vehicle’s lateral dynamics. Utilizing dissipative energy and its gradient information, a time-varying stability boundary is defined under dynamic constraints, and adaptive weighting coordination between the AFS and DYC systems is designed to achieve coordinated control of front steering angle and additional yaw moment. A feedforward–model predictive control (FF-MPC) framework is developed, in which a feedforward module generates compensation based on driver intent to

Zhao, Kun, Zhao, Zhiguo, Wang, Yutao, Xia, Xue, Chen, Xi, Hu, Yingjia

Ensemble of Experts for Generating Digital Parking Maps from Remote Sensing Images

2026-01-0107

4/7/2026

A digital parking map with precise parking spot geospatial information is crucial for tasks such as automatic valet parking, parking spot recommendations, and parking route optimization. This paper presents a parking map generation scheme that extracts high-definition parking spot geometry from remote sensing images. These images often suffer from occlusion, inconsistent resolution, and varying luminosity conditions. The proposed scheme utilizes a model ensemble paradigm, integrating multiple machine learning models to enhance the accuracy and quality of the generation of parking maps. The experiments demonstrate that the proposed scheme achieves an 80.5% parking spot detection precision and a center-to-center geometric representation error of 0.93 meters.

Shukla, Ajitesh, Cao, Xiaofei, Liu, Yongkang, Takeuchi, Yusuke, Sisbot, Akin

Automotive Crash Dynamics Modeling Accelerated with Machine Learning

2026-01-0568

4/7/2026

Crashworthiness assessment is a critical aspect of automotive design, traditionally relying on high-fidelity finite element (FE) simulations that are computationally expensive and time-consuming. This work presents an exploratory comparative study on developing machine learning-based surrogate models for efficient prediction of structural deformation in crash scenarios using the NVIDIA PhysicsNeMo framework. Given the limited prior work applying machine learning to structural crash dynamics, the primary contribution lies in demonstrating the feasibility and engineering utility of the various modeling approaches explored in this work. We investigate two state-of-the-art neural network architectures for modeling crash dynamics: MeshGraphNet, a graph neural network that is widely employed in physics-based simulations, and Transolver, a transformer-based architecture with a physics-aware attention mechanism designed to maintain linear computational complexity with respect to geometric

Nabian, Mohammad Amin, Chavare, Sudeep, Akhare, Deepak, Ranade, Rishikesh, Cherukuri, Ram, Tadepalli, Srinivas

Development of an ‘Online’ Combustion System Calibration Method using a Non-Dominated Sorting Genetic Algorithm

2026-01-0285

4/7/2026

Calibration is a major resource bottleneck and source of risk in powertrain technology development. A promising alternative to the typical design-of-experiments (DoE) approach is the use of a ‘Non-Dominated Sorting Genetic Algorithm’ (NSGA) calibration method, where an iterative process is used to directly identify the Pareto Fronts between performance metrics, for example, net mean effective pressure (NMEP) and NOx emission. The goal of the present work was to develop and demonstrate a fully ‘online’ combustion system calibration method based on an NSGA, where the algorithm operates directly on experimental data rather than empirical models as is typical in the literature. This was completed by first designing an optimal NSGA for combustion system calibration and then demonstrating its use for an experimental combustion system calibration on a single cylinder gasoline engine at one operating condition. Results from the design process here indicate that ‘online’ NSGAs have a strong

Mansfield, Andrew

Optimization Strategies of Gliding Arc Plasma-Based Ammonia Cracker towards Onboard Applications

2026-01-0292

4/7/2026

Ammonia has emerged as a viable hydrogen energy carrier owing to its superior hydrogen density and mature industrial utilization. However, ammonia faces critical challenges including inadequate ignition characteristics and sluggish combustion kinetics, necessitating supplementary high-reactivity fuels for optimizing combustion. Onboard ammonia decomposition technology resolves this problem through on-demand hydrogen real-time production. Among existing ammonia decomposition methods, gliding arc plasma (GAP) demonstrates exceptional promise for onboard hydrogen production given its high processing flow rate，decent hydrogen conversion rate, and transient response capability. Prevailing research predominantly relies on experimental approaches, with insufficient understanding of the effects of specific electrical field parameters and inlet pressure on system performance. This study established a quasi-one-dimensional numerical model for GAP-assisted ammonia decomposition. A comprehensive

Dong, Guangyu, Li, Xian, Zhou, Yanxiong, Xu, Jie, Li, Liguang

Sideslip Angle Estimation for Mining Trucks Based on Real-Time Cornering Stiffness Identification

2026-01-0618

4/7/2026

High-precision estimation of key vehicle–road state parameters is crucial for ensuring the accurate and safe control of mining trucks (MT), as well as for reliable trajectory tracking. Among these parameters, the vehicle sideslip angle is particularly critical for assessing and predicting lateral stability. However, its direct measurement is challenging, and its estimation typically depends on an accurate characterization of tire cornering stiffness. For MT, large variations in loading conditions (from empty to fully loaded) pose significant challenges to sideslip angle estimation due to the resulting nonlinearity and variability of tire cornering stiffness. To address this issue, a novel joint estimation framework integrating the Moving Horizon Estimation (MHE) and Square-Root Cubature Kalman Filter (SCKF) is proposed to simultaneously achieve high-precision estimation of both tire cornering stiffness for each tire and vehicle sideslip angle. In this framework, the cornering stiffness

Xia, Xue, Shen, Peihong, Jiao, Leqi, Li, Tao, Chen, Huiyong, Zhao, Kun, Jiao, Leqi, Zhao, Zhiguo

Collaborative Cybersecurity in a Shifting Automotive Supply Chain

2026-01-0084

4/7/2026

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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