Browse Topic: Machine learning

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Machine Learning-Based Gear Defect Detection in Electric Vehicle Drive Units Using End-of-Line Vibration Analysis2026-01-0637To be published on 04/07/2026
The final assembly of electric vehicle (EV) drive units includes an essential End-of-Line (EOL) test to ensure both component integrity and Noise, Vibration, and Harshness (NVH) quality. This screening process, which uses dynamometers to measure vibration signals, is critical for identifying defects before a drive unit is installed in a vehicle. A significant source of failure during this test is gear defects, which can arise from manufacturing or handling issues. Traditional EOL testing methods rely on time-domain analysis and the impulsiveness of vibration signatures to detect these defects, a technique with inherent limitations in accuracy. This paper introduces and evaluates a novel approach using Machine Learning (ML) to analyze vibration signals for improved gear defect detection. We discuss the methodologies of both the traditional time-domain and the proposed ML-based techniques. Finally, we provide a comprehensive comparison of their respective efficiency and accuracy
Arvanitis, Anastasios, Michaloliakos, Anargyros
Modular Smart Corner Systems for Next-Generation Electric Vehicle Architecture2026-01-0639To be published on 04/07/2026
The transition to software-defined vehicles (SDVs) necessitates a paradigm shift in both control strategies and vehicle architecture. The EU-funded SmartCorners project addresses this challenge by developing integrated smart corner systems (SCS) that combine in-wheel motor propulsion, brake-by-wire, active suspension, and steer-by-wire functionality in a single module. These SCS are embedded within a highly adaptable skateboard chassis architecture, enabling scalable deployment across diverse vehicle platforms. The central approach of this paper is the utilization of artificial intelligence and machine learning to implement multi-layer, data-driven control strategies, facilitating real-time actuation, fault mitigation, and a user-centric vehicle architecture. The SmartCorners project targets to demonstrate significant advancements, including improvement in real-world driving range, reduction of component and system costs, and development time drop of the EV systems enabled by digital
Ratz, Florian, Armengaud, Eric, Formento, Cecilia, Moscone, Giulia, Sorrentino, Gennaro, Bisciaio, Giorgio, Sorniotti, Aldo, Amati, Nicola, Braun, Daniel, Deibler, Bernd, Boxberger, Valerius, Sottile, Salvatore, Ivanov, Valentin, Fuse, Hiroyuki, Kompara, Tomaž
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
Energy-Aware Traffic Signal Optimization for Sustainable Urban Networks Using Valid Action Policies2026-01-0462To be published on 04/07/2026
Optimizing signal control across urban networks is crucial for reducing energy consumption and improving traffic efficiency. Advances in Vehicle-to-Everything (V2X) technologies have enabled deep reinforcement learning (DRL) to demonstrate significant promise in traffic signal control. However, many existing studies focus primarily on agent performance while neglecting practical operational constraints, which can lead to fairness issues in traffic control. Moreover, prior work often treats energy optimization as a by-product of efficiency. To address these gaps, this study proposes a multi-agent DRL framework to mitigate peak-hour congestion and reduce excessive emissions in urban networks. Building on this framework, a variant of Independent Proximal Policy Optimization (IPPO), termed Global State Independent PPO (GS-IPPO), is developed to enhance IPPO's coordination capability. To further improve practical applicability and fairness, this study incorporates constraints imposed by the
Qian, Wangrui, Jiang, Shuyan, Qi, Hao, Ou, Shiqi(Shawn)
Peak SCR Efficiency for Ultra-Low NOx Using Electrically Heated Mixer for EU-VII and China-VII2026-01-0363To be published on 04/07/2026
Achieving ultra-low NOx emissions remains a major challenge in diesel emission control worldwide, especially as increasingly stringent regulations are introduced globally. Selective Catalytic Reduction (SCR), the leading NOx reduction technology in diesel systems, performs best when both heat and ammonia are available. At the same time, any proposed solution must also be cost-effective and economically viable. In this work, we present a low-cost Electrically Heated Mixer (EHM). EHM not only significantly reduces urea deposit formation, thereby lowering warranty costs and mitigating failure modes, but also enhances SCR efficiency, especially in challenging low-load conditions having low exhaust temperature. EHM is also ideal for rapid heat-up and urea injection during engine cold-start conditions ( <100 °C) enabling swift NH₃ formation and storage in the SCR catalyst. Additionally, it enables early urea injection (~ 130 - 150 °C), improving SCR efficiency in low temperatures below 200
Masoudi, Mansour, Poliakov, Nick
A Frequency-Aware Reinforcement Learning Framework for Suspension Control with Integrated Temporal Features and Expert Guidance2026-01-0209To be published on 04/07/2026
Active suspension systems play a crucial role in improving vehicle ride comfort and handling stability. However, most existing studies focus on the low-frequency range below 20 Hz, leaving the suppression of high-frequency vibrations within 50–500 Hz largely unexplored, even though these vibrations strongly affect in-cabin noise and ride quality. To address this gap, this study introduces a quarter-car suspension model incorporating both bushing dynamics and a rigid-ring tire within a reinforcement learning (RL) framework. A major challenge for RL-based suspension control is its degradation in high-frequency performance. To overcome this issue, we design an innovative training framework that integrates multiple synergistic strategies. First, frequency-domain rewards are incorporated as auxiliary signals to explicitly guide policy optimization in the high-frequency band. Second, long short-term memory (LSTM) networks are embedded in both the Actor and Critic to capture the sequential
zhu, Zhehui, Zhang, Lijun, Meng, Dejian, Hu, Xingyu
In-situ Decoupling and Stiffness Optimization of Resiliently Coupled Assemblies Using FRF-Based Substructuring2026-01-0227To be published on 04/07/2026
Road shake remains one of the most critical NVH challenges in light- and heavy-duty body-on-frame trucks, directly shaping ride comfort and perceived vehicle quality. At the heart of this issue are the body mounts, which govern the transfer of vibration between frame and body. Optimizing these mounts for improved road shake performance is therefore a key objective in NVH engineering. Traditionally, such optimization has relied on finite element analysis (FEA). While highly accurate, FEA—especially when performed with commercial solvers such as NASTRAN—demands enormous computational resources, with complete simulations often requiring hundreds of hours. Each design iteration multiplies this cost, restricting the pace and efficiency of NVH development. Recent methods such as response surface modeling (RSM) and machine learning (ML) have aimed to reduce the burden, yet they remain dependent on large libraries of pre-computed FEA data, inheriting much of the same expense and inflexibility
Haider, Syed, Abbas, Ahmad, Jahangir, Yawar, Maddali, Ramakanth
From LLM to Deep Learning: Efficient Simulation of Last-Mile Energy Behavior in Campus Communities2026-01-0461To be published on 04/07/2026
Communities are critical nodes in the urban energy network, integrating energy, transportation, and building systems to enhance overall energy efficiency. Accurate management of these systems depends on characterizing individual energy-demand behaviors. However, traditional last-mile behavioral models often fail to capture the complex and non-linear nature of human decision-making, creating a need for advanced simulation techniques. Although agent-based simulations powered by Large Language Models (LLMs) have demonstrated considerable efficacy across diverse domains, their substantial computational demands—specifically in terms of token consumption—pose pronounced constraints in large-scale simulations involving tens of thousands of agents, considerably limiting their practical applicability. To address this challenge, a deep learning-based approach is proposed for single-step prediction of last-mile behavior. A hybrid architecture, consisting of an MLP encoder and a Transformer
Yang, Zhifeng, Chen, Yongjian, Ou, Shiqi(Shawn)
Study on the Regulation of the Three-Phase Microenvironment in Low-Platinum MEA2026-01-0443To be published on 04/07/2026
With the growth of energy demand, fuel cells as efficient and clean energy devices, have attracted increasing attention. However, the high cost of membrane electrode assembly (MEA) restricts their large-scale application. Therefore, reducing the platinum usage and improving performance have become key research point. In this work, MEA was prepared and excellent performance of 1.52 W·cm-2 was achieved at a low platinum loading. The influence of different ionomer/carbon (I/C) ratio on the performance of fuel cells was systematically investigated. It was found that the performance of the MEA was the highest when the I/C ratio is 0.6. Quantifying hydrophilic and hydrophobic characteristics of catalyst layers with varying ionomer contents revealed that the proton conduction efficiency is optimal when the I/C ratio is 0.6. This balance established efficient proton conduction pathways, from the results of proton conduction impedance testing. SEM analysis demonstrated that pore structure
Li, Xin, Cai, Xin, Lin, Rui
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
CAE Acceleration With Machine Learning For Results Prediction2026-01-0488To be published on 04/07/2026
Industries are following a tedious product development cycle for developing their product. In product development major steps includes design ideas, Drawings, CAD, CAE, Testing and design improvement cycle. This is a monotonous process and takes time which impacts on its time to deliver product and cost on development. Now a days industries are fast growing and targeting to reduce development cycle time and cost. AIML is impacting almost all areas in the industry and significantly reducing efforts time and cost. To make use of AIML in CAE, Altair Physics AI is an effective tool. To ensure the design of product traditional way is to develop a CAD of the product, develop, perform CAE and analyze performance. If we consider CAE procedure it is time consuming process which includes FEA model build, applying boundary conditions, running simulation and analyzing results which could take minutes to hours. By using ML with Physics AI we can make predictions on new design of the product in
Dangare, Anand Manohar, Kulkarni, Mandar
Computational Modeling and Optimization of Shape Memory Polymer-Based Energy Absorbers2026-01-0573To be published on 04/07/2026
Shape memory polymers (SMPs) provide tunable thermomechanical properties and enable the design of recoverable crash structures for automotive applications. This paper introduces a computational framework for the design and optimization of SMP-based crash absorbers with periodic auxetic microstructures. A finite element (FE) model is developed and validated against experimental data reported in the literature. The model is used to simulate crushing and recovery behavior under different temperature conditions. A parametric study is performed by varying key microstructural features, including wall thickness, cell size, and cell shape. Structural performance is evaluated in terms of specific energy absorption (SEA), peak force, and recoverability under axial crush loading. To efficiently explore the high-dimensional design space, surrogate models based on machine learning are constructed, and multi-objective optimization is carried out to identify Pareto-optimal designs that balance
Zhu, Yingbo, Zhu, Feng, Deb, Anindya
Extreme Scale Automotive Aerodynamic Simulations2026-01-0597To be published on 04/07/2026
Achieving an optimal balance between simulation accuracy and computational efficiency remains a central challenge in automotive aerodynamics. While the adoption of AI and machine learning (ML) methods in vehicle development is expected to grow significantly, the demand for highly scalable, computationally efficient, and accurate computational fluid dynamics (CFD) methods persists. The emergence of GPU technology presents new opportunities to deliver cost-effective, high-fidelity, scale-resolving simulations to industrial users. A comprehensive evaluation of Simcenter STAR-CCM+’s parallel scalability and accuracy across extensive CPU and GPU resources was executed on the Frontier supercomputer at Oak Ridge National Laboratory. Steady-state and transient aerodynamic scalability simulations were executed using the DrivAer notchback vehicle configuration. Simulation accuracy was evaluated through transient simulations employing the SST-DDES turbulence model on an initial mesh of 200
Larsson, Torbjörn, Grover, Ronald O., Landi, Simone, Altmann, Peter, McManus, Liam, Dowding, Steven
Virtual Sensor Development for Real-Time Estimation of Transient NOx Emissions for Internal Combustion Engine2026-01-0370To be published on 04/07/2026
Accurately measuring NOx emissions under transient engine conditions is becoming increasingly important with upcoming Euro 7 and EPA 2027 regulations. Traditional physical sensors often struggle with cost and response time, especially with aging of sensors in dynamic operation. This paper introduces a machine-learning–based virtual NOx sensor that can provide real-time emission estimates while reducing reliance on hardware sensors. The approach uses multiple machine-learning methods (Random Forest, Bootstrap Aggregating, Adaptive Boosting, Gradient Boosting, Extreme Gradient Boosting) and selected best one to establish correlations between engine operating parameters, measured steady-state data, and transient duty cycle NOx emissions. Validation across different duty cycles has shown strong alignment with physical sensor readings, with R² values above 0.9995 for known data sets and above 0.9534 for unseen conditions. The model needs to be trained with larger training samples to further
Kumar, Chandan, Dahodwala, Mufaddel, Thawrani, Kiran
Development of a System for Regression Model Enhancement Utilizing Shape Generation AI2026-01-0499To be published on 04/07/2026
This study proposes a method to enhance regression models by shape generation AI. The approach focuses on automatically identifying regions within the design space where the model’s prediction accuracy is low. Once these regions are identified, new and diverse sample shapes are automatically generated by the shape generation AI and incorporated into the training dataset. The regression model is then retrained to improve its performance. By iteratively repeating this cycle of exploration, shape (FE mesh) generation, and model updating, the model’s reliability and accuracy across the entire design space are progressively enhanced. This method addresses data sparsity issues common in complex design tasks and enables better generalization to underrepresented regions. The effectiveness of the proposed system was demonstrated through a case study involving hood outer panels in automotive design. The results showed that adding AI-generated shapes improved prediction accuracy, particularly in
Taniguchi, Mashio
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
Evaluating the Impact of Drag Coefficient Changes on Driving Energy Using a Probability Distribution Model of Yaw Angle Based on Empirical Data from North American Highways2026-01-0615To be published on 04/07/2026
This study estimates the impact on driving energy of differences in aerodynamic characteristics for yaw angle from natural wind during North American Highway mode driving. A previous study[1] clarified the potential to estimate the fuel consumption impact of natural wind by integrating the CD yaw characteristics and yaw angle occurrence frequency. The natural wind was measured on a vehicle while driving a representative North American Highway test course[2]. The driving energy is predicted from the obtained yaw frequency and CD yaw sweep data in a wind tunnel. Measurements were conducted every weekday for 8 hours in 2023, covering 70% of the traffic volume. The validity of the measurement period was evaluated by the deviation from the annual average of wind direction and speed. Since yaw frequency varies depending on the road environment, it is necessary to weight the road environment type frequency when calculating the driving energy. The frequency was calculated using machine
Onishi, Yasuyuki, Nucera, Fortunato, Nichols, Larry, Metka, Matt
Enhancing Vehicle DMU Processes Through Artificial Intelligence (AI) (Revolutionizing Packaging Design)2026-01-0102To be published on 04/07/2026
Abstract- Digital Mock-Up (DMU) is a vital process in the automotive industry, enabling teams to visualize, analyze, and optimize vehicle designs before production. Artificial Intelligence (AI) in the Digital Mock-Up (DMU) processes used in vehicle development. DMU is a virtual representation of a vehicle or component that allows for design, analysis, and simulation without the need for physical prototypes. By introducing artificial intelligence into vehicle DMU, manufacturers can leverage advanced analytics, automation, and predictive modelling to streamline workflows and enhance design accuracy. AI in digital mock-up helps optimizing space, resulting in smaller car design. This explores how AI-driven approaches are transforming DMU processes, improving efficiency, and shaping the future of automotive innovation. Key Benefits of AI in Vehicle DMU: - Enhanced Design Analysis: AI-enhanced design analysis enables teams to rapidly assess complex automotive models, detecting issues early
Nyati, Mohit Ashok, pathak cEng, Amit
Transformer-Based Deep Learning Framework for Efficient Prediction of Automotive Wind Noise2026-01-0222To be published on 04/07/2026
Aerodynamic wind noise is a critical challenge in modern automotive development, particularly with the rise of vehicle electrification and intelligent mobility, where cabin acoustic comfort is a key quality metric. While reliable, traditional methods like wind tunnel experiment and CFD simulations are both costly and time-consuming. To address this, we propose a novel transformer-based framework for rapid and accurate wind noise prediction. Several model improvements, including the physical attention, geometry wave number embedding, hybrid FPS-random down sampling method and frequency separation output heads are properly employed to reduce the GPU mermory cost and improve the prediction accuracy. This framework is pre-trained on a large-scale transient flow field dataset of 1,000 diverse vehicles generated using Delayed Detached Eddy Simulation (DES). From a vehicle's point-cloud coordinates, the model directly predicts the surface pressure spectrum on the driver-side window and the
TANG, Weishao, Liu, Mengxin, QIN, Ling, Duan, Menghua, wang, chengjun
Multi-Physics Topology Optimization and Machine Learning Surrogates for Lightweight Cast Automotive Parts2026-01-0510To be published on 04/07/2026
This paper proposes an integrated optimization framework for lightweight cast vehicle structures by combining Multi-Physics Topology Optimization (MPTO) with machine learning (ML)-driven surrogate modeling. Conventional structural optimization approaches often rely on simplified load cases—such as 3 g inertial loading, frame torsion, or bending—rather than capturing the true complexity of performance-critical conditions like crashworthiness and fatigue durability. As a result, designs optimized for a single criterion frequently degrade in other performance areas, leading to costly redesigns or over-engineered solutions. The proposed methodology introduces MPTO through four structured steps that progressively build design fidelity. Step 1 establishes a baseline primitive topology optimization workflow using a standardized package checklist, ensuring that the design domain, boundary conditions, constraints, and key manufacturing rules are consistently defined as the foundation for
Kim, Hyosig, Senkowski, Andres, Gona, Kiran, Saroha, Lalit, Boraiah, Mahesh
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
Machine Learning based Meta-Analysis of IIHS Moderate Overlap Frontal Crash Test for Rear Seat Occupant Safety Performance2026-01-0579To be published on 04/07/2026
A machine learning (ML)-based meta-analysis was conducted to evaluate rear seat occupant safety performance in the Insurance Institute for Highway Safety (IIHS) Moderate Overlap Frontal (MOF) 2.0 crash test. ML models were trained on historical IIHS crash test data to predict rear passenger injury metrics using vehicle architecture, restraint system characteristics, crash pulse parameters, and vehicle kinematics as input features. The models demonstrated high predictive accuracy and were subsequently used in a Sobol sensitivity analysis to identify critical design parameters influencing injury outcomes. The analysis revealed that rear passenger injury metrics were most sensitive to restraint system parameters. Specifically, crash pulse magnitude was the dominant factor for head injury metrics, pretensioner activation time for neck tension force, and lap belt force for the Neck Injury Criterion (Nij). For chest-related metrics—sternum deflection, dynamic belt position, and maximum belt
Lalwala, Mitesh, Kim, Wonhee, Furton, Lisa, Song, Jay
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
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
Machine Learning-Based Prediction of Wind-Induced Interior Noise in Ground Vehicles2026-01-0599To be published on 04/07/2026
In response to increasing customer demand for enhanced passenger comfort and perceived vehicle quality, OEMs in automotive and commercial vehicles are placing significant emphasis on reducing the interior cabin noise. At highway speeds, wind noise is a primary contributor to the overall noise within the vehicle cabin. Conventional approaches to predict vehicle wind noise rely on physical testing, which can only be conducted in the later stages of the design process once a physical prototype is available. Increased adoption of established computational fluid dynamics (CFD) methods has enabled earlier assessment. However, such simulations require several hours to complete, posing a challenge in the context of rapid design iteration cycles. With the growing adoption of artificial intelligence in engineering, machine learning (ML) approaches have been proposed to predict a vehicle’s aerodynamics performance. Nevertheless, development of ML techniques in the context of aeroacoustics
Higgins, John, Fougere, Nicolas, Sondak, David, Senthooran, Sivapalan, Moron, Philippe, Jantzen, Andreas, Bi, Jing, Oancea, Victor
AI-Enhanced Functional Safety in ADAS Controllers: Predictive Fault Management under ISO 262622026-01-0036To be published on 04/07/2026
Ensuring ISO 26262 functional safety in advanced driver assistance systems (ADAS) is increasingly complex as these platforms adopt artificial intelligence for perception and control. Traditional safety mechanisms are deterministic, but AI introduces non-determinism that challenges verification and validation. This paper presents a predictive fault management framework that enhances functional safety in ADAS controllers using AI-driven models. Real-time vehicle and sensor data are processed through machine learning algorithms that forecast hardware and software faults before they escalate into hazardous conditions. These predictive insights are coupled with ISO 26262 safety mechanisms to enable adaptive diagnostics, fault isolation, and recovery strategies. Hardware-in-the-loop (HIL) validation demonstrates up to a 25% improvement in diagnostic coverage compared to conventional monitoring, while maintaining ASIL-D compliance and real-time performance. By showing how AI can complement
Abdul Karim, Abdul Salam
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
A Real-Time Repositioning Strategy for SAEV Fleets Using a Simulation-Informed Deep Learning Model2026-01-0035To be published on 04/07/2026
Shared Autonomous Electric Vehicles (SAEVs) can enhance urban mobility and efficiency. However, their operational performance is often hindered by the spatio-temporal imbalance between vehicle supply and passenger demand, leading to long wait times. This paper develops a novel repositioning framework where a lightweight CNN, informed by computationally intensive multi-agent simulations, enables real-time strategy deployment. The results show that: (1) An optimized repositioning policy, calibrated via multi-agent simulation, effectively cuts the mean passenger waiting time from 12.0 to 3.0 minutes (a 75% reduction). (2) A lightweight CNN surrogate model enables real-time deployment, reducing the policy computation time from ~4 hours to ~5 minutes (>98% faster). (3) The deep learning surrogate achieves this speed with a negligible performance trade-off, increasing the waiting time by only 0.156 minutes (4.9%) compared to the full optimization.
Shang, Kai, Wang, Ning
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
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
Real-time Parameter Optimization for Eco-driving Control in Connected and Automated Vehicles Using Reinforcement Learning2026-01-0041To be published on 04/07/2026
This paper introduces a novel methodology to enhance the energy efficiency of eco-driving controllers in Connected and Automated Vehicles (CAVs) by leveraging Reinforcement Learning (RL) techniques for real-time parameter optimization. Traditional eco-driving strategies rely on fixed control parameters, which limit adaptability across diverse traffic and road conditions. To address this, we apply continuous action space RL algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO), to dynamically tune four key parameters within a model predictive control framework that is grounded in Pontryagin’s Maximum Principle (PMP). These parameters influence acceleration, braking, cruising, and intersection-approach behaviors, making them critical for achieving optimal eco-driving performance. Our study employs Argonne National Laboratory’s RoadRunner simulator, a Simulink-based environment designed for high-fidelity CAV analysis, incorporating
Zhang, Yaozhong, Ammourah, Rami, Han, Jihun, Moawad, Ayman, Shen, Daliang, Karbowski, Dominik
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
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
Predicting Tensile Properties of Cast Stainless Steels via Machine Learning2026-01-0237To be published on 04/07/2026
In the category of cast stainless steels, there are several variants per different level of addition of chromium (Cr), vanadium (V) along with some minor elements, such as molybdenum (Mo), niobium (Nb), tungsten (W) to meet the requirement of corrosion and oxidation resistance. However, the influence of chemical composition variations on the mechanical properties of cast SS continues to lack a clear understanding. In the present study, via machine learning (ML), the effects of each element on the tensile properties of the cast stainless steel are studied. The ML model is then used to predict how variations in elements affect tensile behavior, with the predictions validated through physical testing.
Mishra, Neelam, Biswas, Surjayan, V S, Rajamanickam, Aluru, Phani, Liu, Yi, Akbari, Meysam, Coryell, Jason
A Cooperative Hand Gesture Recognition System for Human Machine Interface to Control Robotic Arms2026-01-0252To be published on 04/07/2026
In the context of Industry 5.0, effective collaboration between humans and machines demands seamless and natural interaction. Hand-Gesture Recognition (HGR) is a promising technology for creating intuitive human-machine interfaces (HMIs) that allow users to control robots without physical devices or wearables. This research introduces a real-time HGR system designed to control a 6-Degree-of-Freedom (DoF) robotic arm using YOLOv10, a state-of-the-art deep learning model for hand gesture recognition. While YOLOv10 offers high accuracy, its computational demands exceed the capabilities of edge devices typically attached to robotic arms, posing a significant hardware challenge. To overcome this, we propose a cooperative client-server architecture that distributes the workload between the edge device and a more powerful remote server. An RGB camera mounted on the robotic arm captures hand gesture images and sends image data to the server using User Datagram Protocol (UDP). The server
DeHaven, Aaron Lee, Park, Jungme
Dual-loop voltage control of electric vehicle DWPT systems based on robust right coprime factorization and deep Q-networks2026-01-0208To be published on 04/07/2026
This paper proposes an adaptive robust voltage-control framework for dynamic wireless power transfer (DWPT) systems, explicitly targeting the problem of voltage stabilization under time-varying and uncertain load resistance typical in vehicular charging scenarios. The proposed architecture adopts a dual-loop structure: an inner loop that enforces provable stability via operator-theoretic robust right coprime factorization, and an outer loop that provides high-precision tracking through a data-driven deep reinforcement learning policy. In the inner-loop design, the receiver-side BUCK converter within an LCC-S DWPT topology is modeled in operator form and decomposed into right coprime factors. A controlled operator representation that accounts for unknown disturbances is formulated, and nonlinear control operators are synthesized based on the Bézout identity together with Lipschitz-type conditions. This design ensures bounded-input–bounded-output (BIBO) stability of the inner closed loop
Chen PhD, Jingjun, Zhang PhD, Yizhe, Dai, Yunlong, Wang PhD, Xingyu, Wu, Guangqiang
Development of Markov-Chain Prediction Model for Work-Stealing Scheduler in Dynamic Scheduling for Multicore Embedded Software Systems2026-01-0066To be published on 04/07/2026
Designing embedded software that achieves effective utilization of the fast-growing multicore embedded hardware should help to reduce their execution time and power consumption and improve their reliability. AI and machine learning algorithms are making their way into such rapidly enhanced multicore embedded hardware. We have developed a Markov-chain prediction model and integrated it into a work-stealing scheduler within a dynamic scheduling runtime layer (DSRL). Dynamic scheduling with a work-stealing scheduler was adapted from MIT’s Cilk framework. Dynamic scheduling allows independent computations to be spawned so they can be scheduled dynamically and executed in parallel on available cores. Cilk used a random model in its work-stealing scheduler where an idle core randomly selects other cores to steal computations from them. However, our Markov-chain-based scheduler allows idle cores to make informed decisions about which cores are better to steal their computation to increase
Sadeh, Waseem, Ganesan, Subramaniam, Qu, Guangzhi, Rawashdeh, Osamah
CFD-Guided DoE-ML Optimization Methods in a Heavy-Duty Hydrogen Engine2026-01-0326To be published on 04/07/2026
This study introduces a CFD-guided Design of Experiments (DoE) and Machine Learning (ML) framework for the co-optimization of piston and pre-chamber geometries in a passive pre-chamber heavy-duty hydrogen engine operating at medium and low loads. Starting from a reference configuration—an omega-type piston and a methane-optimized pre-chamber—the design space was parameterized using seven geometric variables. A Sobol sequence was employed to generate 96 randomized design variants in the DoE, each evaluated through high-fidelity 3D-CFD simulations to capture key combustion and performance metrics. The resulting dataset served as the foundation for developing and evaluating several ML regression models. A rigorous ML workflow was adopted, featuring 5-fold cross-validation and hyperparameter tuning via Bayesian optimization to ensure generalization and robustness. Model selection was based on multi-metric performance criteria including prediction accuracy, error stability, and sensitivity
Menaca, Rafael, Shakeel, Mohammad Raghib, Liu, Xinlei, Mohan, Balaji, AlRamadan, Abdullah, Cenker, Emre, Silva, Mickael, Zhang, Anqi, Pei, Yuanjiang, Im, Hong
Plug-in Electric Vehicles Adoption: A Review of Forecasting Methodologies, Policy Impacts, and Future Trajectories in Major Global Markets2026-01-0455To be published on 04/07/2026
Accurate forecasting of Plug-in Electric Vehicle (PEV) market sales share is vital for evidence-based policymaking, yet existing studies employ diverse and often fragmented methodologies, creating a need for systematic review to clarify their analytical foundations and comparative strengths. This study classifies mainstream forecasting methodologies into theory-driven and data-driven approaches, providing a review of five representative models' merits, limitations, and potential directions. Analysis reveals that leading approaches increasingly employ cross-scale model coupling, theory-data fusion, and modular design to harness complementary strengths, improving model robustness and predictive accuracy. Furthermore, the study compares PEV policies and market forecasts in China, the United States, and Europe—the world's three largest automotive markets. The findings indicate a strong linkage between forecast convergence and policy stability. China demonstrates the highest policy
Luo, Wei, Ou, Shiqi(Shawn), Zhou, Pan, Wang, Tianpeng, Qian, Xiaodong
Clustering-Based Segmentation of Off-Road Vehicle Activity into Productive and Non-Productive States from GPS Data2026-01-0169To be published on 04/07/2026
Accurate identification of Productive and Non-Productive States or Off-Road vehicle duty cycles—comprising working, idle, and transport states—is critical for performance analysis, fuel optimization, and emissions modeling in agriculture machinery and fleet operations. This study explores the application of unsupervised machine learning techniques to classify duty cycles using GPS-derived parameters such as speed, location variance, and temporal patterns. Unlike supervised approaches, the proposed method does not rely on labeled datasets, making it scalable and adaptable across diverse operational contexts. Clustering algorithms, including K-means and DBSCAN, are employed to segment GPS data into distinct behavioral states. Feature engineering focuses on extracting motion signatures and spatial-temporal features that correlate with operational modes. Validation against manually annotated datasets demonstrates high accuracy in distinguishing idle, working, and transport phases. The
Maharana, Devi prasad, Gangsar, Purushottam, Dharmadhikari, Nitin, Pandey, Anand Kumar
CrashML Analyzer: A Data-Driven Approach for Investigation and Fault Assignment in Autonomous Driving Collisions2026-01-0160To be published on 04/07/2026
This study investigates factors contributing to autonomous vehicle (AV) accidents and proposes an automated fault determination framework. A total of 563 accident reports from the State of California Department of Motor Vehicles spanning from 2019 to 2024 were analyzed by converting unstructured standardized reports into structured data using custom extraction tools. Analysis of these reports reveal that AVs were not at fault in 69.4% of cases, and were fully at fault for 22.6% of the cases. The proposed method uses these reports to provide an early indicator of fault likelihood and potentially replaces tedious manual review. Machine Learning (ML) and Natural Language Processing techniques were used to replicate the reported faults, achieving 96% average accuracy across three models: Gradient Boosting, Linear Regression, and Random Forest. Through feature engineering techniques in semantic feature extraction from narrative accident descriptions, quantifiable variables were obtained and
Rwejuna, Florida Perfect, Majid, Nishatul, Goutham, Mithun, Loukili, Alae
AI-Based Prediction of Motion Sickness in Autonomous Vehicles Using Passenger Profiles and Driving Route Data2026-01-0154To be published on 04/07/2026
Autonomous Vehicles (AV) are expected to significantly reshape travel experiences. Drivers will no longer be responsible for operating the vehicle and will instead take on the role of occupants, free to engage in personal activities such as reading or using phones. Consequently, motion sickness arises from a mismatch between the motion perceived by the body and the motion expected by the brain. Motion sickness detection in AVs has typically been approached through sensor-driven methods that monitor vehicle dynamics as well as passenger conditions, requiring continuous streams of data for reliable predictions. However, while vehicle-mounted sensors can be seamlessly integrated into the system, passenger-oriented solutions often rely on wearable devices, which may not always be practical for wide use in everyday settings. To address this gap, we introduce a ProfileFusion model, a multimodal deep learning model providing a more user-friendly solution that requires minimal user input. It
Khosravifard, Mina, Malik, Usama Zaid, Nguyen, Trang, Grosseruschkamp PhD, Marc, Lichtenberg, Frank
Regression Algorithms for Intelligent Estimation of the Selling Price of Used Vehicles – A Machine Learning Approach2026-01-0151To be published on 04/07/2026
Transportation is undergoing a major change in this rapidly growing world. Technological progress has made automobiles a possibility making this small world even smaller. This means progress and advancements are leading to many people switching vehicles frequently in anticipation of better features. Sadly, in many countries, they are left in the dark about the best price their vehicle can fetch in the market and therefore are forced to settle for a price decided by the used-car outlets or their best instincts. This means that the owners of the vehicles may not always end up getting the best deal for their cars while selling. A second drawback to the existing approach, the selling price of their cars may not always be consistent with different dealers as they may offer different prices for the same car. A third drawback is the current scenario is not user friendly as the car owners have to travel from dealer to dealer to get to know the selling price of the car. This opens up the
Sridhar, Sriram
Predictive Maintenance of EV Drive Unit Oil Pumps via Machine Learning on Fleet Telemetry2026-01-0161To be published on 04/07/2026
The reliability of Drive Unit (DU) oil pumps is critical to the performance and safety of electric vehicles, as these pumps provide essential lubrication and thermal management. However, direct physical sensors for real-time pump health monitoring are absent in current architectures, making early fault detection particularly challenging. To address this limitation, we present a novel, data-driven anomaly detection framework that leverages large-scale customer fleet telemetry and advanced machine learning to identify incipient pump degradation that traditional diagnostic methods often fail to capture. The core of the approach is an XGBoost regression model trained on time-series features—including commanded pump speed, oil temperature, and historical pump current—to predict expected current behavior under nominal conditions. Deviations are quantified using the Mean Absolute Percentage Error (MAPE) between predicted and actual currents, providing a continuous and interpretable measure of
Li, Jingman, Yao, Mengqi, Rahimi, Sahil, Lin, Joanne
A Real-Time Vehicle Detection and Counting System Using a GUI Interface.2026-01-0156To be published on 04/07/2026
The growing need for intelligent traffic monitoring and environmental control has fuelled the importance of automated vehicle detection and counting systems. Fuel consumption estimation, vehicular emissions calculations, and traffic pattern comprehension- all play a vital role in shaping sustainable environmental policies. Therefore, accurate identification and counting of vehicles is essential. In this paper, we have shown the development of a real-time vehicle detection and counting application that uses computer vision and deep learning techniques to effectively detect, categorise, and count several types of vehicles on Indian roads. The computer vision vehicle counting application uses the YOLO (You Only Look Once) deep learning model for real-time identification and categorisation of diverse vehicle types, including cars, buses, trucks, and motorbikes [1]. A tracking module based on the SORT (Simple Online and Real-time Tracking) algorithm ensures unique identification of vehicles
Gusain, Ashna, Maurya, Akshay, PATHAK, SUNIL, Khan, Tuhin
Machine Learning for Predicting Rotor and Brake Fluid Temperatures in Hill Descent Events2026-01-0159To be published on 04/07/2026
High thermal loads on brake systems during extended descents followed by vehicle soak pose significant safety and durability risks. Excessive rotor or fluid temperatures can cause loss of braking efficacy, fluid degradation or evaporation, thermal fade, and accelerated component wear. This study uses time history data of brake disc and fluid temperatures collected during controlled hill descent events and subsequent soak (park) periods, and simultaneously logs environmental conditions (ambient temperature, humidity, wind speed and direction) and brake specifications (rotor, caliper geometry, pad material, fluid type, and vehicle mass). Seventy six test runs from a dedicated validation program are used, each providing time history records that form the basis of our analysis. From these records we extract phase specific samples (descent and soak phase) and engineer compact descriptors, such as start and peak temperatures, environmental factors, rolling statistics and contextual metadata
poojari, Uday Kumar, Westphalen, Jan, Venugopal, Narayana
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