Browse Topic: Data acquisition and handling

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Speed Determination Using Audio of Dash Camera Video from Commercial Vehicle Tires Frequencies for Vehicle Accident Reconstruction2026-01-0536To be published on 04/07/2026
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 and therefore 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 research was
Vega, Henry V., Cornetto, Anthony, Ngo, Long Justin, Hatab, Ziad, Hunter, Eric
Comparative Analysis of Rear Collision Prevention Systems2026-01-0535To be published on 04/07/2026
Rear Collision Warning (RCW) and Rear Emergency Braking (REB) are key Advanced Driver Assistance Systems (ADAS) designed to prevent collisions with Vulnerable Road Users (VRUs) when a vehicle is reversing. These systems typically rely on a combination of radar, ultrasonic, and camera sensors. This study provides a comparative evaluation of Rear Collision Prevention systems across different vehicle manufacturers. Testing was performed on the 2022 Cadillac Escalade, 2017 Subaru Outback, and 2021 Ford F-150, using both static and moving pedestrian targets. Data collection was carried out with a VBOX data acquisition system, focusing on time-to-collision (TTC) as the primary performance metric. The analysis highlights variations in system effectiveness and provides a brief overview of OEMs' approach to pedestrian safety.
Harrington, Shawn, Takbhate, Shubham Ramraja, Nagarajan, Sundar Raman
A Cooperative AFS/DYC Control for FWDIEV Based on Dissipative Energy Method2026-01-0619To be published on 04/07/2026
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
Curvature-Based End-to-End Autonomous Vehicle Path Planning at Intersections2026-01-0045To be published on 04/07/2026
Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents an end-to-end approach to curvature prediction using deep neural networks trained on real-world driving data. We develop a comprehensive neural network architecture that directly maps high-dimensional sensor inputs to curvature predictions, eliminating the need for intermediate feature engineering steps. The model processes multi-modal inputs including vision features, vehicle state parameters, and environmental context through a deep learning pipeline. Our end-to-end approach demonstrates the feasibility of learning complex driving behaviors directly from raw sensor data, providing a more robust and generalization solution compared to traditional rule-based methods. The neural network architecture incorporates dropout regularization and adaptive learning rate scheduling to ensure stable training and optimal performance. Results show
Hajnorouzali, Yasaman, Wang, Hanchen, Li, Taozhe, Burch, Collin, Lee, Victoria, Tan, Lin, Arjmandzadeh, Ziba, Xu, Bin
An Image-Derived Multimodal Framework for Automotive Tire Exterior Noise Prediction2026-01-0220To be published on 04/07/2026
This study presents an image-derived multimodal AI framework for early-stage tire noise evaluation. The proposed model requires only multi-angle photographs captured by a standard smartphone and basic tire specifications. From these images, scaled three-dimensional (3D) meshes and fixed-view depth maps are reconstructed and combined with numerical parameters within a neural network architecture. Three input branches—a point-cloud–gradient branch, a depth-map convolutional neural network (CNN) branch, and a specification multi-layer perceptron (MLP) branch—are jointly trained using a composite loss that integrates frequency-weighted mean squared error (MSE), spectral cosine similarity, FFT-domain consistency, and A-weighted sound-level terms. A dataset of 28 tires, spanning passenger, SUV, and pickup applications for both battery electric vehicles (BEVs) and internal combustion engine (ICE) vehicles, was evaluated using leave-one-out (LOO) cross-validation. The model achieved a mean
Shao, Guangxin, Shopoff, Scott, Franklin, Nicholas
An Efficient Approach to Key Negotiation in an Automotive Environment2026-01-0087To be published on 04/07/2026
Negotiating Keys for applications such as message authentication within a vehicle presents many problems as, in designing the algorithm; the algorithm must be able to be utilized by small, fixed-point processors. In addition, if there is a desire to do this algorithm in the manufacturing environment, there are severe time constraints placed on how long this algorithm can take, as there are strict station time requirements, which are expensive to change, and any time utilized in the plant can negatively affect vehicle throughput. Additionally, negotiating these keys between many ECUs can greatly increase the time required to negotiate a common key using standard multi-party Diffie-Hellman. Timing would also be an issue in the case of using pair-wise Diffie-Hellman for encryption and distribution of keys utilizing a key master. To solve these problems in multi-party key negotiation, we have utilized the Elliptic Curve variation of the Burmester-Desmedt (ECBD) algorithm. ECBD is
Van Dam, Theo, Mazzara, Bill
Adaptive Economic Model Predictive Control for Engine Emissions Management with Compensation for Performance Drift2026-01-0287To be published on 04/07/2026
This paper addresses the changes in engine emissions due to in-use component changes through the synergistic application of predictive control, machine learning, and onboard adaptation. In particular, we consider an adaptive economic Model Predictive Control (eMPC) strategy to mitigate the effects of performance drift on Nitrogen Oxides (NOx) and Soot emissions from compression ignition (diesel) engines. A performance drift block, which applies a multiplier and offset to nominal emissions, is integrated with a high-fidelity Neural Network (NN) plant model to simulate these characteristic changes. To counteract variability, two online adaptation methods are integrated within the eMPC framework: One is based on Recursive Least Squares (RLS) and another on a continuously updated online NN. The proposed control architecture is validated through simulations over standard transient cycles. Results demonstrate that while the rate-based eMPC possesses inherent robustness to performance drift
ZHANG, JIADI, Li, Xiao, Kolmanovsky, Ilya, Tsutsumi, Munecika, Nakada, Hayato
A Control Method for Dual Motor Redundant Steer System based on Zeroing Neural Networks2026-01-0581To be published on 04/07/2026
In the process of driving a vehicle, the steering system directly determines the vehicle's travel path and direction, serving as the physical basis for precise control in autonomous driving. Whether it is lane keeping, turning, or obstacle avoidance, high responsiveness of the steering control is required to execute the commands of the decision-making system. This study focuses on the dual-motor steer-by-wire (SBW) system, addressing the tracking issues arising from factors such as dual-motor coupling. A control strategy for coordinated dual motor drive in an autonomous driving steering system based on dual-motor redundancy is proposed. A dynamic model of the steer-by-wire system is constructed, and the software algorithm employing a zeroing neural network for the dual-motor redundant steering is used to solve the synchronization issues of the dual-motor system, based on the concept of cross-coupled control. Finally, simulation analysis was performed on the MATLAB simulation platform
Yang, Lingang, xiong, min, Zeng, Dequan, Petitti, Antonio, Nawaz, Aamir, Linh, Nguyen Vu, Hu, Yiming, Yu, Yinquan, CARBONE, GIUSEPPE, GAO, LETIAN, Jiang, Xiaomeng
Oscillatory Behavior of Understeering Vehicles2026-01-0623To be published on 04/07/2026
Oscillations in understeering vehicles are occasionally described in the literature, primarily in terms of the poles of the yaw rate response, but perhaps not completely appreciated in their complexity. This work shows that as speeds of the understeering vehicle increase, the increasingly underdamped poles of the yaw rate transfer function combine with the effects of a low frequency zero and a reduced steady-state response to result in oscillations greater than would be expected from eigenvalues alone. A speed range for acceptable yaw rate response is suggested, and it is shown that a typical understeering passenger car operates within this range. As the understeering vehicle’s speed increases beyond this range, the high speed limit of the oscillation frequency is found.
Williams, Daniel
Collaborative Cybersecurity in a Shifting Automotive Supply Chain2026-01-0084To be published on 04/07/2026
The evolution toward software-defined vehicles (SDVs) is causing disruption to the traditional automotive supply chain and breaking down the common hierarchical OEM, tier 1 supplier, and tier 2 supplier relationships. With demands for faster software release cycles, more advanced software projects involving multi-party development, and considerations for end-to-end embedded and cloud integrations, new cybersecurity challenges are introduced that no single organization can address alone. Thus, this disruption creates new trust dependencies and requires new models for collaboration, transparency, and joint responsibility in cybersecurity. This paper presents a collaborative cybersecurity model, emphasizing shared responsibility during multi-party development between OEMs, tier 1 and 2 suppliers, engineering services organizations, and technology and services providers. As such, we explore collaborative approaches for each stage in the development lifecycle including design, development
Oka, Dennis Kengo, Vinzenz, Nico
Impact of C-rates on Electrical Behavior of Li-ion Batteries under Coupled Temperature-load Conditions2026-01-0383To be published on 04/07/2026
The utilization of lithium-ion batteries in electric vehicles becomes increasingly common. Battery performance degradation is influenced by both temperature and boundary constraints. Monitoring the usable cell capacity (UCC) under real-world operating conditions is essential for ensuring accurate state-of-health estimation. Capacity tests in laboratory settings are typically conducted at low C-rates to approximate equilibrium conditions, whereas in real vehicle applications, charging currents are often much higher. This discrepancy in charge/discharge rates frequently results in deviations between laboratory characterization and on-board BMS capacity estimation. To investigate the impact of C-rates under coupled temperature-load conditions, we conducted long-term cycling tests on LFP/graphite pouch cells at 25 °C and 45 °C under different constrained conditions. The UCC was evaluated at different test rates (1/20C, 1/3C, and 1C) as an indicator of aging. The results show that
ZHANG, Shan, Niu, Zhice, Xia, Yong
High Strain-Rate Behaviors of Fiber Reinforced Composites in Principal and Off-Axis Directions2026-01-0175To be published on 04/07/2026
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 orthotropic glass, carbon and a natural
Bawa, Prashant, Deb, Anindya, Zhu, Feng
Energy Management Strategy for Hybrid Mining Trucks Based on Global SOC Planning and Neural Network Optimal Control2026-01-0446To be published on 04/07/2026
Hybrid mining trucks, as core equipment for mine transportation, face high energy consumption and significant fluctuations in power demand during cyclic operations due to prolonged exposure to demanding operating conditions characterized by heavy loads and variable working conditions. To address the issues of high energy consumption and significant fluctuations in power demand during the cyclic operation of mining trucks, this paper proposes a hybrid mining truck energy management strategy based on global SOC (State of Charge) planning and neural network optimization control. First, a powertrain model was developed for a typical operating cycle of a hybrid mining truck, and its accuracy was validated by comparing it with experimental data. Using dynamic programming algorithms to plan the SOC for single-cycle operations provides a rational reference for energy allocation across different operational phases of mining trucks during a single cycle. Next, using the powerful nonlinear
Yang, Jianyu, Zhao, Zhiguo, Chen, Huiyong, Li, Tao, ZHUANG, Wenyu, Shen, Peihong, Tang, Peng
Research on the control of magnetic weakening scheme for the voltage utilization rate of hybrid transmission2026-01-0447To be published on 04/07/2026
This paper proposes a weak magnetic control method for PMSM current regulators based on the ratio of pressure to frequency and voltage utilization rate when the motor speed exceeds the weak magnetic speed. That is, the direct-axis offset current id is generated through the action of weak magnetic feedforward and weak magnetic I regulator, and the control of weak magnetic speed can directly adjust the demagnetization effect of id to achieve weak magnetic control of PMSM. The weak magnetic control method of PMSM current regulators with the outer loop of voltage utilization rate is adopted. It is proposed to use the phase-locked loop control method to decode the motor position signal in a closed loop and calculate the motor speed. The fluctuation of the position signal is reduced and the signal is more stable. This paper realizes constant power speed regulation based on high voltage utilization rate and weak magnetic control method at high speed operation above the base speed, which
Jing, Junchao, Yan, Xiaoqian, Liu, Yiqiang, Sun, Fengjiao, Dai, Zhengxing
Neural Surrogates and Reinforcement Learning for Heavy-Duty Diesel Torque Control: A Data-Driven Alternative to PID2026-01-0199To be published on 04/07/2026
Accurately reproducing the torque trace of regulatory drive cycles is central to heavy-duty diesel emissions certification and repeatable development testing. Conventional controllers—PI/PID with gain scheduling and feedforward mapping—can meet requirements but demand extensive hand-tuning and struggle with strong nonlinearities and time-varying delay. Building on prior work that quantified the limits and benefits of gain-scheduled and feedforward strategies under the FTP transient cycle, this work investigates a fully data-driven control framework. First, a recurrent neural network (LSTM) torque surrogate is trained on dynamometer data to capture engine dynamics from readily available signals (e.g., engine speed, commanded pedal/torque, recent torque history) and actuator constraints. Second, this learned “world model” is used to train a reinforcement learning (RL) policy that outputs pedal commands to minimize torque-tracking error while regularizing control effort and respecting
Cook, James, Puzinauskas, Paulius, Bittle, Joshua, Hall, Spencer
Optimization Strategies of Gliding Arc Plasma-Based Ammonia Cracker towards On-Board Applications2026-01-0292To be published on 04/07/2026
Ammonia has emerged as a viable hydrogen energy carrier owing to its superior hydrogen density, 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 cracking technology resolves this problem through on-demand hydrogen in-time production. Among existing decomposition methods, gliding arc plasma (GAP) demonstrates exceptional promise for on-board crackers given its decent hydrogen conversion rate and transient response capability. Prevailing research predominantly employs experimental methodologies wherein macroscopic parameters inadequately proxy microscopic electric field characteristics, thereby impeding mechanistic insight. This study establishes a quasi-1D numerical model to interrogate how electric field parameters modulate ammonia-to-hydrogen conversion yield and system energy efficiency in
Dong, Guangyu, LI, Xian, ZHOU, Yanxiong, XU, Jie, Li, Liguang
Development and Trial of a Non-Dominated Sorting Genetic Algorithm for Rapid Engine Calibration2026-01-0285To be published on 04/07/2026
In the quickly changing automotive powertrain landscape, calibration processes are a major bottleneck to production and new technology development. The implementation of rapid powertrain calibration processes can reduce development resource requirements, improve performance, and reduce risk. In previous works the author successfully developed an internal combustion engine calibration strategy using a Lagrange-Multiplier genetic algorithm for multi-constraint optimization. In the present work, this previous algorithm is expanded to create and demonstrate a new non-dominated sorting genetic algorithm which is aimed at identifying the entire Pareto front of the relationship between two engine performance parameters. Accurate and rapid identification of Pareto fronts enables designers to effectively and efficiently balance performance tradeoffs and identify optimal calibration settings. The algorithm developed here optimized the calibration of three combustion control parameters
Mansfield, Andrew
Development of Tilt Test Facility2026-01-0265To be published on 04/07/2026
Mobility, fire power and protection are the three most important characteristics of an Armoured Fighting Vehicle (AFV). Transmission system facilitates mobility to the fighting vehicle. Combat Vehicles Research and Development Establishment (CVRDE) is developing Automatic Transmission for Armoured Fighting vehicles which includes gearbox, steering system, braking system and control system. In the battle field, AFVs have to operate at different gradients & side slopes. The Automatic Transmission system being developed by CVRDE is a hydro-mechanical automatic transmission in which hydraulic oil is used for lubrication & control of various systems of it. A Tilt Test Facility (TTF) has been designed, developed, and installed at CVRDE to simulate different angles of gradients from +35° to - 35° & side slopes from +20° to -20°. The TTF enables to evaluate the functional aspects of the transmission system at different angles. The TTF consists of two tiltable platforms, Rotary actuators
V, Uma, Naik, Sheshu
Leveraging Machine Learning for Occupant Body Size Estimation via B-Pillar Seatbelt Systems2026-01-0559To be published on 04/07/2026
Occupant body size in vehicles varies significantly, encompassing differences in height, weight, 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 was used to collect data from 47 seated participants. Key seatbelt-related parameters, including D-ring angle, belt payout length, lap belt length, and buckle tension, were measured. These measurements were used to train machine learning models to predict occupant characteristics: height, weight, sitting height. The results demonstrate that low-cost sensors embedded in the seatbelt system can provide sufficiently accurate occupant body size estimations to inform adaptive restraint systems. The prediction errors across both training and test datasets were as follows
Wang, Da, Ahmed, Jawwad, Rowe, Mike, Brase, Dan
Attention and Intrinsic Curiosity-Enhanced Deep Reinforcement Learning for Path Planning in Dynamic Environments2026-01-0044To be published on 04/07/2026
Efficient and reliable path planning remains a core challenge for autonomous vehicles operating in dynamic and crowded environments. Although Deep Reinforcement Learning (DRL) has shown considerable potential in autonomous decision-making, it still faces challenges such as insufficient feature extraction, sparse rewards, and low obstacle avoidance efficiency in complex scenarios. To address these issues, this paper proposes an end-to-end path planning framework, PPO-ICM-Attn. Built upon the Proximal Policy Optimization (PPO) algorithm, the framework incorporates a dual-channel attention convolutional neural network module (Attention-CNN) to enhance spatial and semantic understanding of dynamic obstacles, and introduces an Intrinsic Curiosity Module (ICM) to promote active exploration in sparse reward settings. Furthermore, a reactive avoidance reward function based on velocity obstacle theory is designed and embedded to achieve real-time proactive collision avoidance in highly dynamic
Shen, Shiquan, Liu, Jiahao, Chen, Zheng, Li, Zongdian, Zhao, Yuting, Wu, Minggong, Zhao, Jie, Qin, Zongquan, Wang, Yanfeng
From Prediction to Design: Using Context-Aware Graph Neural Networks and Explainable AI to Anticipate Transfer-of-Control2026-01-0049To be published on 04/07/2026
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 that adapts proactively to the operating context, enhances comfort, and improves safety. We present a context-aware framework that generates a unified driver–vehicle–environment representation by fusing data from videos of the in-cabin and forward road with vehicle kinematics, and driver behaviors (annotations of driver glance, hands-on-wheel, and secondary-task engagement). This representation is encoded in a hierarchical Graph Neural Network that classifies driver-initiated TOC to: (i) Manual-to-Automation (M2A) and (ii) Automation-to-Manual (A2M) and predicts 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
Zhao, Zhouqiao, Gershon, Pnina
An Updated Correlation Study between the Full Size Wind Tunnels of General Motors, Ford Motor Company, and FCA US LLC2026-01-0612To be published on 04/07/2026
As automotive aerodynamic testing facilities evolve to capture more real-world behavior, updating the correlation between old and new technologies is essential. Recently, the three-member consortium of the United States Council for Automotive Research (USCAR) - FCA US LLC, Ford Motor Company, and General Motors - transitioned from full-size static ground plane facilities to 5-belt moving ground plane wind tunnel facilities. The primary objective of this study was to update the correlation data sets to maintain consistent and robust data sharing among companies, which is the cornerstone of USCAR efforts. To achieve this, a set of updated correlation data sets were calculated to replace the original correlation study results from 2008. Additionally, the methodology for applying correlation equations was revised from using averaged wind tunnel data to employing direct wind tunnel-to-wind tunnel correlation equations. In a two-phase correlation effort conducted in 2022 and 2025, the three
Nastov, Alexander, Lounsberry, Todd, Madin, Trevor, Langmeyer, Gregory, Fadler, Gregory, Skinner, Shaun, Horton, Damien
Advancing Vehicle Safety with Onboard Smart Technology for Real-Time Occupant Protection2026-01-0554To be published on 04/07/2026
Keeping passengers safe during accidents is a top priority for modern vehicles. Most current safety systems rely on preset responses or cloud services to react during emergencies, which can sometimes cause delays. This study explores a new way to protect occupants using smart processors built right into the vehicle. These processors work with sensors placed in seats, such as pressure pads and small cameras, to watch how passengers sit and move in real time. When a crash or sudden movement happens, the system quickly understands who might be in danger and adjusts safety features like airbags and seatbelts to better protect each person. By using detailed computer models to design and test the system before building physical prototypes, the research ensures that it can respond effectively and quickly. The setup is designed to work independently without relying on outside connections, which makes it reliable even in critical situations. Initial tests show this smart, in-car system can
Saraswat, Shubham, Vishe, Prashant
Advanced Damping Force Modeling Using Machine Learning for Next-Generation Electric Vehicle Suspensions2026-01-0586To be published on 04/07/2026
The rapid evolution of electric vehicles (EVs) has necessitated innovative approaches to optimize ride comfort, handling, and overall suspension performance. Unlike conventional internal combustion engine vehicles, EVs introduce unique challenges due to their distinct weight distribution, powertrain dynamics, and noise characteristics. This paper presents an advanced damping force modeling methodology leveraging machine learning (ML) techniques to enhance the suspension design process for next-generation EVs. The study employs data-driven ML algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, to model the nonlinear and frequency-dependent behavior of dampers under various operational conditions. A comprehensive dataset, generated through simulation and experimental testing, captures the effects of road profiles, vehicle dynamics, and damping settings. The proposed ML-based approach predicts damping forces with high accuracy, outperforming traditional analytical
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
Automotive Crash Dynamics Modeling Accelerated with Machine Learning2026-01-0568To be published on 04/07/2026
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
From CFD Simulations to Machine Learning: A Comparative Study of ML Tools for Aerodynamic Drag Prediction2026-01-0598To be published on 04/07/2026
This study presents the development of machine learning frameworks for predicting aerodynamic drag coefficients in automotive vehicle designs using data derived from computational fluid dynamics (CFD) simulations. The input data includes both structured tabulated design parameters and unstructured geometric representations in the form of point clouds, enabling a multi-modal approach to aerodynamic modeling. A range of machine learning models was explored, from traditional regression techniques to advanced deep learning architectures such as graph neural networks (GNNs), each tailored to handle specific data formats. These frameworks were designed to integrate seamlessly with CFD workflows, supporting efficient training and evaluation using standard regression metrics. The work aims to establish a foundation for data-driven aerodynamic analysis in automotive design, emphasizing the role of machine learning in enhancing simulation-based engineering processes.
Kumar, Gaurav, Khanna, Susheel
Data-driven Study on Chassis Suspension Performance Degradation2026-01-0582To be published on 04/07/2026
The performance of chassis suspension mechanisms critically affects vehicle handling, ride comfort, and safety. Under severe operating conditions, health monitoring of chassis suspension mechanisms can detect potential problems in time, thereby reducing safety risks and optimizing maintenance costs. With the rapid development of sensing and data analytics technologies, data-driven approaches are increasingly used in health monitoring. This study aims to achieve real-time monitoring of chassis suspension performance degradation using a deep neural network (DNN). First, a semi-vehicle suspension model is constructed to simulate the vehicle's degraded state under bumpy road conditions, generating a dataset of key suspension parameters. Subsequently, a DNN model comprising three hidden layers is developed to assess suspension performance degradation. To optimize model performance, the effects of different numbers of neurons and hidden layers on model accuracy are explored. Experimental
Liao, Yinsheng, Lei, Yisong, Su, Ailin, Wang, Zhenfeng
Adaptive Torque Vector Control for Distributed Drive Intelligent Electric Vehicles Using Model and Data Driven Approaches2026-01-0628To be published on 04/07/2026
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 roughness and adhesion is proposed, which optimizes the comprehensive performance of vehicle chassis. First, combined with the characteristic of the unbalance dynamic force caused by the air gap between the stator and
Wang, Zhenfeng, Zhao, Gaoming, Zhang, Zhijie
LiDAR-Driven A* Path Planning with Dynamic Obstacle Avoidance for Autonomous Navigation in ROS2026-01-0030To be published on 04/07/2026
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
Ensemble of Experts for Generating Digital Parking Maps from Remote Sensing Images2026-01-0107To be published on 04/07/2026
Being a vital component of transportation, parking and parking-related research are gaining increasing attention from the intelligent transportation and automotive industry research communities. A digital parking map with precise parking spot geospatial information is crucial for research such as automatic valet parking, parking spot recommendations, and parking route optimization. Currently, these maps are generated through costly and resource-intensive human annotation. To address the challenges of insufficient parking maps and the high costs of manual map creation, researchers have proposed the following two strategies for generating parking maps. The SLAM-based map generation for indoor parking lots and the remote sensing image-based map generation for outdoor parking lot. Our work mainly focuses on the outdoor high-definition parking map generation from remote sensing images. These images often suffer from occlusion, inconsistent resolution, and varying luminosity conditions. To
Shukla, Ajitesh, Cao, Xiaofei, Liu, Yongkang, Takeuchi, Yusuke, Sisbot, Akin
Simulating Cryptographic Primitives in Model Based Systems Engineering Projects2026-01-0083To be published on 04/07/2026
Security has become an increasingly critical concern for large off-road vehicle manufacturers due to the growing threats posed by hackers, heightened awareness around data privacy, the implementation of anti-tamper technologies, and stringent emissions regulations. These pressures necessitate the development of robust security measures that can effectively safeguard vehicle systems while promoting responsible and sustainable innovation. To address these challenges, cybersecurity teams and engineers can leverage tools such as Model-Based Systems Engineering (MBSE), which facilitates the rapid and cost-effective development of comprehensive security mechanisms. This paper explores the application of systems engineering techniques to create a solution for ensuring integrity and authenticity in Controller Area Network (CAN) systems, which are commonly utilized in heavy-duty diesel engines. The deployment of solutions to secure the CAN bus will likely depend on cryptography. However
DiValentin, Maria, Daily, Jeremy
Implementation and evaluation of a method to defend in-vehicle networks using SAE J1939-91C to authenticate network messages between multiple ECUs.2026-01-0092To be published on 04/07/2026
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. The evaluation results provide a benchmark for network system performance that can be used in a system test bench to assess compliance with the SAE J1939-91C standard and to test the standard's effectiveness in ambiguous edge cases. In the future, this benchmark could be utilized in "Plugtests" to assign test scores to non-uniform SAE J1939-91C
Zachos, Mark, Medam, Krishna Teja
Autonomous Racing Control Using Reinforcement Learning with Racing Line Guidance2026-01-0031To be published on 04/07/2026
In recent years, with the emergence of autonomous racing competitions such as Roborace, autonomous racing cars have become a prominent research focus and have undergone rapid development. This paper establishes a reinforcement learning-based decision-making and control framework for autonomous racing cars, achieving cooperative optimization between high-speed racing and safety control under extreme operating conditions. By integrating a Control Barrier Function constrained reward function for dynamic stability and a racing line-guided path optimization, the proposed framework achieves a synergistic balance between minimum lap time and operational safety. Dynamic stability domain constraints based on a two-degree-of-freedom single-mass rigid-body model and racing line optimization based on track geometric modeling are established. The phase plane stability boundary is constructed using the yaw rate method and double-line method, upon which Control Barrier Function constraints are
peng, tao, Zhang, Weiqi, Li, Zengwen, sudong, jiang
RRT*_Heuristic_Adaptiv_maxR: A practical path planning method based on novel adaptive framework for autonomous mobile agent2026-01-0038To be published on 04/07/2026
Aim at addressing the problem of random sampling and low efficiency of rapidly-exploring random tree (RRT) path planning algorithm, a practical path planning method is proposed for autonomous mobile agents, named RRT*_Heuristic_Adaptiv_maxR. This method is based on a new adaptive framework, balancing the search efficiency and convergence accuracy of path planning by dynamically adjusting the maximum radius parameters in the search process. Combining the heuristic search strategy, guiding the sampling direction, accelerating convergence to the target area, the obstacle detection mechanism is used to optimize the path in real time to avoid collisions. Experimental results show that the proposed method, RRT*_Heuristic_Adaptiv_maxR, has higher planning success rate and path quality in complex environments compared with various RRT algorithms, and it is verified the effectiveness of the adaptive framework and the maximum radius dynamic adjustment strategy in mobile agent path planning.
Dong, Xiaomei, Zeng, Dequan, Petitti, Antonio, You, Jingyi, xiong, min, GAO, Letian, Jiang, Xiaomeng, Yu, Yinquan, CARBONE, GIUSEPPE, Hu, Yiming, Nawaz, Aamir, Linh, Nguyen Vu
Mitigation of Sensor-Level Cyber-Physical Attacks on Electric Vehicle Powertrains: A Simulation-Based Study2026-01-0091To be published on 04/07/2026
Electric vehicles (EVs) rely extensively on sensor feedback for safe and efficient powertrain operation. However, this dependency introduces cyber-physical vulnerabilities, especially when sensor signals are maliciously manipulated. This paper presents a simulation-based investigation into sensor-level cyberattacks on a mid-sized EV powertrain model developed in MATLAB/Simulink. The study quantifies mechanical consequences and evaluates mitigation strategies to enhance system resilience. Four representative attack scenarios were simulated. Speed sensor spoofing led the controller to misinterpret vehicle velocity, causing a 41% overshoot beyond the 50 km/h setpoint. False data injection into torque/current sensors triggered an unintended torque surge of approximately 20%, resulting in inverter current saturation within 2 seconds. Battery temperature spoofing delayed thermal protection, allowing a deviation of 1.5 °C/min beyond safe operating limits. A hybrid attack combining frozen
Tariq, Usama, Sahandabadi, Sahereh, Dianat, Ali
Ford Vehicle Lines Comprehensive Sales Forecasting: A Machine Learning Approach with Price Sensitivity Analysis2026-01-0108To be published on 04/07/2026
The automotive industry faces constant challenges in accurate demand prediction due to market volatility, macroeconomic fluctuations, and the complexity of consumer purchasing decisions. This project develops a comprehensive forecasting system for all Ford vehicle lines and their respective catalogs, implementing an advanced machine learning methodology that integrates endogenous and exogenous variables to generate 12-month sales predictions with price sensitivity analysis. The primary objective is to develop a robust predictive model capable of forecasting sold units for each Ford vehicle catalog under different price variation scenarios, providing strategic insights for commercial and pricing decision-making. The system enables evaluation of price increases or decreases impact on projected demand, facilitating pricing strategy optimization and revenue maximization. The model architecture incorporates a hybrid approach combining time series, neural networks, and advanced machine
Hernandez, Alejandro, Hernandez Cervantes, Oscar, Landa, Ruth
Roundabout Dilemma Zone Data Mining and Forecasting with Trajectory Prediction and Graph Neural Networks2026-01-0029To be published on 04/07/2026
Traffic roundabouts, as complex and safety-critical road scenarios, present significant challenges for autonomous vehicles. In particular, predicting and managing dilemma zone (DZ) encounters at roundabout intersections remains a pivotal concern. This paper introduces an AI-driven system that leverages advanced trajectory forecasting to anticipate DZ events, specifically within traffic roundabouts. At the core of our framework is a modular, graph-structured recurrent architecture powered by graph neural networks (GNNs). By modeling agent interactions as a dynamic graph, our approach integrates heterogeneous data sources, including semantic maps, while capturing agent dynamics with high fidelity. This GNN-based forecasting model enables accurate prediction of DZ events and supports safer, data-driven traffic management decisions for both autonomous and human-driven vehicles. We validate our system on a real-world dataset of roundabout intersections, where it achieves high precision with
Lu, Duo, Farhadi, Mohammad, Satish, Manthan, Yang, Yezhou, Chakravarthi, Bharatesh
Bias and uncertainty in the time, position, and speed data of consumer-grade GPS devices2026-01-0544To be published on 04/07/2026
The goal of this study is to quantify the accuracy and uncertainty of position and speed measurements acquired by a wide range of consumer-grade GPS-enabled devices while road cycling. We acquired position and speed data simultaneously from 13 consumer devices (e.g., phones, watches, bicycle computers) at their maximum sampling rate (typically 1 Hz) during 10 hours (thirty 20 minute sessions) and 150 km of road cycling in a suburban environment. The position data from these devices were compared to real-time kinematic (RTK) data acquired using a differential GPS system and the speed data from these devices were compared to speed data acquired with high-resolution wheel rotation sensors (23,040 pulses per revolution) synchronized with the RTK data. Based on these comparisons, we generated probability distributions for the errors in the position and speed measured by each of the consumer devices. This study provides foundational data needed to quantify the accuracy and uncertainty across
Booth, Gabrielle R., Mitchell, Alan L., Siegmund, Gunter P.
Determining Video Frame Capture Rate through Reverse Engineering2026-01-0543To be published on 04/07/2026
The timing of video recordings, along with the spatial positioning of objects, is a fundamental parameter for calculating the speed time history. If the task involves determining an object’s average speed over several to a dozen frames, the average inter-frame interval can be used as the basic time unit. However, if the objective is to compute speed from individual frames, the reliability of the timing becomes crucial. Without access to DVR hardware documentation, proprietary algorithms, or software – and considering the frequent hardware modifications and software updates - the most effective way to solve the problem is through a reverse-engineering approach. This study discusses several aspects of timing analysis, including: (1) making a test recording of a calibrated LED lightboard; (2) analyzing the relationship between the lightboard time and the presentation time stamp (pts) extracted from the file metadata; (3) investigating frequency interference occurring when recordings are
Wach, Wojciech
Lap Time Performance Sensitivity to Tire Model Parameters: An Optimal Control Framework for Model Selection and Calibration2026-01-0214To be published on 04/07/2026
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
Real-Time Hardware-in-the-Loop Validation of Hybrid and Electric Vehicle Powertrain Systems2026-01-0409To be published on 04/07/2026
This paper presents applied methodologies and results from real-time Hardware-in-the-Loop (HIL) testing of electrified powertrain systems, based on experience gained during advanced development programs in the electric vehicle (EV) sector. It outlines how HIL accelerated control software validation, enhanced fault coverage, and improved development cycles for critical powertrain components such as Vehicle Control Units (VCU), Inverters (INV), Chargers, DC-DC Converters (DCDC), and Electric Motors (EM). As electrified propulsion systems grow in complexity to meet performance, efficiency, and safety demands, robust and scalable validation techniques are essential. Modern EV powertrains integrate multiple ECUs managing distinct subsystems, requiring thorough testing of both individual components and their interactions. To address this, a real-time HIL test bench was developed to simulate a broad range of driving conditions, powertrain operating scenarios, and fault cases. The test bench
Sathiyamoorthy, Pradeepraja
Accelerated Computed Tomography for Spray Characterization2026-01-0341To be published on 04/07/2026
Computed tomography (CT) is a valuable diagnostic technique for visualizing 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-jet 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, often requiring dozens of 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 Engine Combustion
Yi, Junghwa, Wan, Kevin, Pickett, Lyle
Multi‑Level Driving Behavior Modeling and Hydrogen Refueling Analysis in Fuel Cell Vehicles Using Big Data2026-01-0467To be published on 04/07/2026
This study shows a new data-driven framework for analyzing driver behavior using high-frequency data from fuel cell vehicles (FCEVs). The proposed pipeline involves data collection and processing, trip segmentation, segment-level preprocessing, segment-level features extraction and processing, and unsupervised clustering across both dynamic and stationary behavior. PCA–UMAP–KMeans pipeline has been used on the driving behavior research by classifying drivers into different driving styles. Stationary behaviors including the hydrogen refueling characteristics is researched by integrating dynamic signal and related location date. Finally, both driving and stationary behaviors are integrated and combined as individual driver profile for FCEVs for future vehicle product development.
Chen, Jiawen, wu, guangquan, lin, chengbo, xu, lin, li, changyu
ANN and RSM-Driven Optimization of Tribological Characteristics of CuO-Doped Castor Oil Biolubricant2026-01-0353To be published on 04/07/2026
The development of renewable and eco-friendly biolubricants 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 biolubricants 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, acting as a catalyst, converts 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 behavior of the bio-lubricant was evaluated using a four-ball tribometer test. In the present test, pressure, temperature
Prabhakaran, J, Pali, Harveer Singh, SINGH, Nishant Kumar
Optimization of a Feedforward Neural Network Machine Learning Battery Model for an Automotive Pouch Cell2026-01-0386To be published on 04/07/2026
Modern battery management systems have a critical need for highly accurate battery terminal voltage models, which are a key component of algorithms which estimate or predict power capability, range, temperature, and other factors. While electro-chemical and equivalent circuit models are widely used for this purpose, they typically struggle to capture the complex, non-linear dynamics and degradation effects inherent in real-world battery operation, necessitating frequent recalibration. This study addresses these limitations by proposing a robust, data-driven approach for terminal voltage estimation using a feedforward neural network (FNN) machine learning model. An LG E66 cell, a high-performance lithium-ion battery with a Nickel Cobalt Manganese (NCM) cathode, was selected for this study due to its prevalent use in light duty electric vehicles. Data collected at temperatures ranging from 40 °C to -20 °C is used to train and test the models, with model error reported for HWFET, UDDS
Dehury, Biswanath, Nahidmobarakeh, Lucas, Panchal, Satyam, Gross, Oliver, Kollmeyer, Phillip
Machine Learning Modeling for Compressive Property Prediction of Foams2026-01-0512To be published on 04/07/2026
Foam material models for automotive structural analysis typically require tensile and compressive data at multiple strain rates. The testing is costly and may require a long time to complete. For many applications, foams of similar chemistry are used and the foam structural responses, such as stiffness and compression force deflection, are controlled by the foam density. In such cases, Machine Learning (ML) lends itself as an ideal tool to detect the trends in material response based on density and strain rate. In this paper, five microcellular polyurethane (PU) foams of different densities were tested at four strain rates ranging from 0.01/s to 100/s. A ML model capable of predicting compressive stress-strain response for a range of densities was developed. The model demonstrated good prediction capability for intermediate strain rates at all foam densities and in extrapolating stress-strain curves at higher densities at all strain rates. The strain rate trends for a density outside
M, Gokula Krishnan, Kavimani, Harish, Muppana, Sai Siddhartha, Savic, Vesna, Chavare, Sudeep, V S, Rajamanickam
Design and Evaluation of a Deep Neural Network Model for Prediction of Diesel Equilibrium Combustion Products and Characteristics2026-01-0293To be published on 04/07/2026
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
Impact of User-Adjustable Vehicle-Dynamics Tuning on Vehicle Durability2026-01-0194To be published on 04/07/2026
Software-defined vehicles offer customers a greater degree of customization on vehicle operation. One such feature is a user-adjustable tuning of vehicle ride and handling, where customers can vary the ride height, the active damper stiffness (affecting ride comfort), front-rear torque balance (affecting ride handling), and other vehicle dynamics features. While promising a great customer experience, such a feature can expose the vehicle to a wider range of structural loads than before, particularly when such tuning is extended to cover spirited “sport” mode driving, offroad 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
Demiri, Albion, White, Dylan, Ramakrishnan, Sankaran, Borton, Zackery, Khapane, Prashant
A Latent Space Framework for Modeling Transient Engine Emissions Using Joint Embedding Predictive Architectures2026-01-0423To be published on 04/07/2026
Accurately modeling exhaust emissions during transient events, such as rapid acceleration and deceleration, remains a long-standing challenge in the automotive field and is a prerequisite for real-time control and optimization in modern powertrain systems. The nonlinear emission behavior is difficult to capture, and early mathematical models frequently relied on approximations that limited their accuracy. With the rise of data-driven methods, especially deep neural networks, including multilayer perceptrons and long short-term memory (LSTM) networks, modeling capabilities have improved significantly. However, to achieve meaningful accuracy, these models often require deep architectures, which increase computational load and inference time. This makes their application in real-time control systems challenging, as controllers must run at high sampling rates while maintaining accuracy, which is often a contradictory requirement. This paper addresses the trade-off with a new framework
Sundaram, Ganesh, Gehra, Tobias, Ulmen, Jonas, Heubaum, Mirjan, Görges, Daniel, Günthner, Michael
Development of A Tire Blowout Experimental Platform for Dynamics and Control Validation2026-01-0202To be published on 04/07/2026
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. A custom-built blowout device integrated with a data acquisition system has been developed to address this gap. It consists of multiple solenoid valves mounted on the wheel surface and powered by a 12V supply. All valves are triggered at the same time using an RF remote, producing rapid and synchronized deflation. An Arduino-based actuation system is being developed for individual valve actuation and custom
KANTHALA, Maha Vishnu Vardhan Reddy, Krishnakumar, Ashwin, Lin, Wen-Chiao, Chen, Yan
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