Browse Topic: Neural networks

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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
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
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
A Framework for Predicting 3D Printed Part Defects Using MultiModal Deep Learning on Infrared Images2026-01-0140To be published on 04/07/2026
Fused filament fabrication (FFF) has gained popularity in recent years because it can produce prototypes and functional components with complex geometry. Because of inherent process variability, the components often exhibit defects such as warping, layer delamination, voids, and poor surface finish, as well as issues related to variable material strength and anisotropy. In-situ monitoring (ISM) of the FFF process is a promising technique to predict part performance, which in turn can support accept or reject decisions for printed parts. This paper proposes a framework for incorporating ISM-generated information, with a particular focus on infrared (IR) image analysis for this purpose. IR camera images, in conjunction with numerical features such as infill pattern and extruder nozzle temperature, serve as an input to a multimodal deep learning (MDL) model that predicts the mechanical performance of printed parts. In the framework, convolutional neural nets process image inputs, while a
Mollan, Calahan, Kulkarni, Saurabh, Malik, Ali Ahmad, Patterson, Albert E., Pandey, Vijitashwa
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
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
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
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
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
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
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
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
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
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
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
Prediction of Battery Self-Discharge Under Sparse Data Using Physics-Guided Machine Learning2026-01-0155To be published on 04/07/2026
Predicting battery self-discharge across wide temperature ranges and extended durations remains a significant challenge due to the scarcity of physical test data, which is typically limited to a few temperature points and short observation windows. This limitation complicates generalization and increases the risk of inaccurate extrapolation. To address this, the paper introduces a machine learning–based framework designed to predict self-discharge behavior under diverse thermal conditions and long time horizons. Multiple modeling strategies are examined, including feedforward neural networks, long short-term memory (LSTM) architectures, synthetic data generation, and physics-informed integration of governing equations. Particular emphasis is placed on hybrid and physics-regularized models that embed first-principles relationships to guide extrapolation beyond the observed data domain. This approach mitigates the inherent instability and potential errors associated with purely data
Chavare, Sudeep, Zeng, Yangbing, Muppana, Sai Siddhartha, Miao, Yong, Xu, Simon
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
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
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
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
Method of Estimating Electric Vehicle Charge Time Based on Neural Networks2026-01-0382To be published on 04/07/2026
Accurate prediction of electric vehicle charging time is critically hindered by dynamic, non-linear factors including battery aging which is indicated by the State of Health (SOH), substantial power diversion to thermal management systems in extreme temperatures, fluctuating user-defined accessory loads, and hardware limitations of the charging infrastructure. Traditional estimation methods, reliant on static models or predefined calibrations, fail to adapt to these real-world variables, leading to inaccurate predictions and user dissatisfaction. This paper presents a novel data-driven estimation framework utilizing a tailored feedforward neural network architecture specifically designed for this complex task. The model processes a sensitive set of inputs—including initial State of Charge (SOC), SOH, battery temperature, charging station power level and user-selected target SOC—to effectively capture the intricate, non-linear interdependencies governing the charging process. The
Xie, Zhentao, shojaei, Sina, Weslati, Feisel
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
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
IoT-Enabled Cloud-Integrated Smart Parking System with Real-Time Monitoring and AI-Based Space Optimization for Next-Gen Mobility2026-28-0113To be published on 02/12/2026
This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless
Deepan Kumar, Sadhasivam, S, Balakrishnan, Dhayaneethi, Sivaji, Boobalan, Saravanan, Abdul Rahim, Mohamed Arshad, S, Manikandan, R, Jamuna, L, Rishi Kannan
Dynamic Stiffness Prediction in Cracked Cantilever Beams Using Enhanced ANN Techniques2026-28-0050To be published on 02/12/2026
This research paper provides a comprehensive study on how Artificial Neural Networks (ANNs) can be deployed to predict the stiffness characteristics of a cantilever beam with a crack of various depths and positions. The most destructive source of failure is considered to be vibration, so the major focus of this paper will be on how the cracks affect the modal stiffness. This study has various applications, such as airplane wings, bridges, stadiums, and arenas. A common research gap was noticed amongst the existing studies; the position of the cracks in the cantilever wasn’t considered, but this paper discusses how the location of cracks severely affects the dynamic behaviour of the cantilever. This study was done by carrying out modal analysis on a cantilever of the same dimensions with different crack configurations. Various crack dimensions and orientations were analysed to understand the effects of the crack on the dynamic behaviour of the cantilever. From the modal analysis results
SB, Harshini, Rajkumar, Manjari, R, Krithika, K, Anusha, K, Divya, Bhaskara Rao, Lokavarapu
A Data-Driven Method for Typical Landing Gear Structure Optimization Based on Neural Networks01-19-01-00022/6/2026
The landing gear, as a crucial component of an aircraft, is pivotal for maintaining the safety and reliability of air travel. This study introduces a data-driven structural optimization method aimed at mitigating the peak strain on the landing gear’s rocker arm. The initial phase involves selecting nine design variables for parametric modeling to generate an initial dataset. Subsequently, the Maximum Information Coefficient (MIC) technique is used to conduct a parameter sensitivity analysis, enabling the identification and elimination of variables with minimal influence. A comparative analysis between the Genetic Algorithm–Backpropagation Neural Network (GA-BPNN) and BPNN reveals that GA-BPNN has a superior fitting capability on the enhanced dataset. By applying Particle Swarm Optimization (PSO), the optimal solution for GA-BPNN is identified. The implementation of this optimized method results in a 38.16% reduction in peak strain, validating its feasibility and reliability in
Chen, Hu, Shi, Shi, Wang, Meng, Fang, Xingbo, Wei, Xiaohui, Nie, Hong
RDAS: A Shared Control Strategy Considering Real-Time Driving Ability in Driver Takeover Tasks10-10-02-00141/24/2026
Autonomous vehicles require drivers to assume control of the vehicle in situations where the vehicle control system cannot perform its intended task. A shared control-based approach to driving authority transfer can effectively mitigate the driving risks associated with diminished driver capability due to prolonged disengagement, but it may readily precipitate human–machine conflicts—oscillatory steering behavior, excessive driver workload, and unstable control during weight transitions. Addressing the characteristics of driver capability variations during takeover tasks, a shared control strategy incorporating real-time driving ability, termed the real-time driving ability strategy (RDAS), is proposed. Initially, a real-time capability assessment strategy based on an expected steering angle model is developed. By collecting driving data under conditions of adequate driver capability to train an adaptive neuro-fuzzy inference system (ANFIS) neural network, the expected steering angle
Qi, Zhenliang, Liu, Ping, Duan, Haotian, Zhou, Zilong, Huang, Haibo
Transforming Gear Manufacturing: A Deep Learning Approach to Quality Prediction2026-26-06461/16/2026
The automotive industry is rapidly transitioning towards Industry 4.0, transforming vehicle manufacturing. To achieve a lower carbon footprint, it is crucial to minimize raw material wastage and energy consumption. Reducing component wastage, lead time, and automating gear manufacturing are key areas. Gear micro-geometry inspection is vital, as variations affect service life and NVH (Noise, Vibration, Harshness). Despite standards for permissible errors, manual evaluation of gear microgeometry inspection is often needed. This subjective evaluation approach will have a possibility that a gear with undesired variations gets assembled into the product. These issues can be detected during NVH testing, leading to replacement of part and re-assembly thus increasing lead time. This generates a need for an automated system which could reduce the human intervention and perform gear inspection. The research aims to develop a deep learning-based model to eliminate the ambiguity of manual
Ramakrishnan, Gowtham Raj, Baheti, Palash, PR, Vaidyanathan, Durgude, Ranjit, Bathla, Archana, R, Greeshmita, V, Rangarajan
Artificial Intelligence in Passenger Cars: Unlocking Intelligent Mobility and Human-Centric Vehicle Innovation2026-26-06721/16/2026
Artificial Intelligence (AI) is radically transforming the automotive industry, particularly in the domain of passenger vehicles where personalization, safety, diagnostics, and efficiency. This paper presents an exploration of AI/ML applications through quadrant of the key pillars: Customer Experience (CX), Vehicle Diagnostics, Lifecycle Management, and Connected Technologies. Through detailed use cases, including AI-powered active suspension systems, intelligent fault code prioritization, and eco-routing strategies, we demonstrate how AI models such as machine learning, deep learning, and computer vision are reshaping both the user experience and engineering workflow of modern electric vehicles (EVs). This paper combines simulations, pseudo-algorithms and data-centric examples of the combined depth of functionality and deployment readiness of these technologies. In addition to technical effectiveness, the paper also discusses the challenges at field level in adopting AI at scale i.e
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
NN FMU: Deep Learning Models for Next-Gen xiL Vehicle Simulation2026-26-04521/16/2026
Functional Mock-up Units (FMUs) have become a standard for enabling co-simulation and model exchange in vehicle development. However, traditional FMUs derived from physics-based models can be computationally intensive, especially in scenarios requiring real-time performance. This paper presents a Python-based approach for developing a Neural Network (NN) based FMU using deep learning techniques, aimed at accelerating vehicle simulation while ensuring high fidelity. The neural network was trained on vehicle simulation data and trained using Python frameworks such as TensorFlow. The trained model was then exported into FMU, enabling seamless integration with FMI-compliant platforms. The NN FMU replicates the thermal behavior of a vehicle with high accuracy while offering a significant reduction in computational load. Benchmark comparisons with a physical thermal model demonstrate that the proposed solution provides both efficiency and reliability across various driving conditions. The
Srinivasan, Rangarajan, Ashok Bharde, Pooja, Mhetras, Mayur, Chehire, Marc
Implementation of New Educational Technologies for Training Personnel for the Transport Industry2026-26-02411/16/2026
The article is devoted to a comprehensive analysis of the digital transformation of education using the example of a project to train engineering personnel for the innovative transport industry in Russia. Special attention is paid to the introduction of hybrid formats, digital platforms, inclusivity, issues of digital inequality, as well as the experience of the National Research Center of the Russian Federation FSUE NAMI and interaction with leading universities in the country. A comparative analysis with foreign initiatives, including modern AI solutions for inclusive education, is presented, as well as the impact of the project to create educational and methodological centers on the professional motivation of teachers.
Shishanov, Sergei, Kurmaev, Rinat, Revenok, Svetlana
Optimization of Frontal Impact Force Scheme to Accelerate the Target Setting Process for Development of Front-End Structures in Vehicles2026-26-00011/16/2026
A passenger vehicle's front-end structure's structural integrity and crashworthiness are crucial to ensure compliance with various frontal impact safety standards (such as those set by Euro NCAP & IIHS). For a new front-end architecture, design targets must be defined at a component level for crush cans, longitudinal, bumper beam, subframe, suspension tower and backup structure. The traditional process of defining these targets involves multiple sensitivity studies in CAE. This paper explores the implementation of Physics-Informed Neural Networks (PINNs) in component-level target setting. PINNs integrate the governing equations into neural network training, enabling data-driven models to adhere to fundamental mechanical principles. The underlying physics in our model is based upon a force scheme of a full-frontal impact. A force scheme is a one-dimensional representation of the front-end structure components that simplifies a crash event's complex physics. It uses the dimensional and
Gupta, Ishan, Bhatnagar, Abhinav, Kumar, Ayush
Driving Behavior Modelling with Graph Attention Networks2026-26-06611/16/2026
In automotive engineering, understanding driving behavior is crucial for decision on specifications of future system designs. This study introduces an innovative approach to modeling driving behavior using Graph Attention Networks (GATs). By leveraging spatial relationships encoded in H3 indices, a graph-based model constructed, which captures dependencies between various vehicle operational parameters and their operational regions using H3 indices. The model utilizes CAN signal features such as speed, fuel efficiency, engine temperature, and categorical identifiers of vehicle type and sub-type. Additionally, regional indices are incorporated to enrich the contextual information. The GAT model processes these heterogeneous features, learning to identify patterns indicative of driving behavior. This approach offers several significant advantages. Firstly, it enhances the accuracy of driving behavior modeling by effectively capturing the complex spatial and operational dependencies
Salunke, Omkar
A Mathematical Modelling Approach Based on Artificial Neural Network for Estimation of Key Parameters in Internal Combustion Engine2026-26-06591/16/2026
In a conventional powertrain driven by Internal combustion (IC) engines, various sensors are used to monitor engine performance and emissions. Along with physical sensors, virtual sensors or modelled values of key parameters play an important role for enabling various diagnostics strategies and engine monitoring. Conventional strategies for modelling incorporate the use of regression models, map-based models and physics-based models which have few drawbacks in terms of accuracy and model calibrations efforts. Data driven models or neural networks have fairly better accuracy and reliability for estimating complex parameters. Representing the neural network with a mathematics-based model would help to eliminate drawbacks associated with conventional modelling approach. The proposed methodology uses artificial intelligence technique called artificial neural network (ANN) for estimation of temperature at turbine inlet (TTI) in typical diesel engine. The data driven model is built in Python
Jagtap, Virendra Shashikant, Shejwal, Sanket, Mitra, Partha
Accelerating Engine Auxiliaries’ Development by Deploying ML Techniques in CFD Virtual Verification2026-26-03741/16/2026
The automotive and off-road industries are heavily investing in R&D to improve both physical and virtual verification and validation techniques. Recent software and hardware advancements have extended these techniques from simple component evaluations to complex system assessments such as involving multi-physics scenarios. Despite the benefits of virtual validation tools like structural analysis and CFD, they often come with high development costs, particularly in CFD applications. Virtual verification methodology, especially when combined with data science, offer significant advantages over traditional physical methods by enhancing CAE efficiency and reducing resource consumption which can greatly improve product design and validation efficiency across many industries. The success of machine learning applications depends on effective data processing, adequate computational resources, and the right algorithm selection. Key machine learning techniques impacting the CFD field include
Jadhav, Mitali, Kumbhar, Appaso, Tirumala, Bhaskar, Nisha, Kumari
AI-Driven Digital Twin for Enhanced Suspension Assembly Testing in Automotive Vehicles2026-26-04451/16/2026
This paper presents the development and implementation of a digital twin (DT) for the suspension assembly of automotive vehicles—an essential subsystem for assessing vehicle performance, durability, ride comfort, and safety. The digital twin, a high-fidelity virtual replica of the physical suspension system, is constructed using advanced simulation methodologies, including Finite Element Analysis (FEA), and enriched through continuous integration of empirical test data. Leveraging machine learning techniques, particularly Artificial Neural Networks (ANNs), the DT evolves into a dynamic and predictive model capable of accurately simulating the behaviour of the physical system under diverse operational conditions. The primary aim of this study is to enhance the precision and efficiency of suspension testing by enabling predictive maintenance, real-time system monitoring, and intelligent optimization of test parameters. The digital twin facilitates early detection of potential failures
Sonavane, Pravinkumar, Patil, Amol
Extreme Learning Machines for Fast Instantaneous Prediction of Emission Level of an Automobile2026-26-02261/16/2026
Addressing climate issues is a key aspect of good global governance today. A key aspect of managing the threats caused to the environment around is to ensure a sustainable transportation system so that humans exist in peace with nature. According to sources, in 2020 alone, cars accounted for approximately 23% of global CO2 emissions. In addition, they also emit dangerous pollutants thus damaging the ecosystem. To keep pollutants in check there are emission level testing strategies in place in each country. However, we can do better for a sustainable future. On one hand, the huge volume of vehicles around the world makes it an excellent choice and source for a vast emission level dataset comprising of input features as well as the target variable representing the emission band of the vehicle. In addition to the big data available as mentioned above, major advancements in the machine learning algorithms are done today. The advent of algorithms such as Artificial Neural Networks (ANN) has
Sridhar, Sriram, Aswani, Shelendra
Multi-Model Analysis of O-Ring Compression and Relaxation Force Predictions Using Machine Learning and CAE Simulations2026-26-04661/16/2026
O-rings play a critical role in ensuring leak-proof seals in a wide range of engineering systems. Accurate prediction of their compression and relaxation behavior under various material and geometric configurations is essential for optimal design and reliability. This study presents an analysis of machine learning techniques to predict two key performance outputs, compression force and relaxation force (after 10 minutes) trained on computer-aided engineering (CAE) simulation data. The experimental setup was represented in CAE simulation and the results were compared with experimental data conducted at ZF test facilities. Simulation results correlated well with the experimental data (deviation was less than the 5%). To create a dataset for training machine learning (ML) models, realistic ranges for the input parameters such as hardness and geometrical parameters were determined, and simulation data were generated using design of experiments (DOE). Multiple ML models were developed and
Kosgi, Durgaprasad, Alva, P Pancham, Dangeti, VenkataKrishna Pavan
Regenerative Braking Control Strategy for Electric Commercial Bus under Indian Road Condition Using Artificial Neural Network2026-26-06451/16/2026
The main focus of this paper is to create a more efficient regenerative braking control strategy for electric commercial buses operating under Indian road conditions. The strategy uses Artificial Neural Networks (ANNs) to optimize regenerative braking process. Regenerative braking helps to recover energy that would otherwise be lost during braking and convert it back into usable power for the vehicle. The challenge is to design a system that works effectively on the diverse and often challenging road conditions found in India, such as varying gradients, traffic patterns, and road surface types. This study begins by collecting data (which includes vehicle speed, traffic condition, etc.) from real-world driving conditions and aims to train an Artificial Neural Network (ANN) using a large set of driving data which is collected under various conditions to predict the most efficient regenerative braking settings for different driving scenarios. This research brings a new approach to the
Saurabh, Saurabh, Bhardwaj, Rohit, Patil, Nikhil, Gadve, Dhananjay, Amancharla, Naga Chaithanya
An Interpretable Deep Learning Decision-Making Framework for Autonomous Lane-Change Inspired by Human Driving Experiences2025-01-733912/31/2025
Lane change plays a critical role in autonomous driving and directly affects traffic safety and efficiency. Although deep learning-based lane-change decision-making frameworks have achieved promising results, they still face fundamental challenges in producing human-consistent and trustworthy behavior, mainly due to: 1) Inadequate psychology-informed personalization, as most frameworks focus on physical variables but neglect psychological factors (e.g., risk tolerance, urgency), limiting their ability to capture individual differences in lane-change motivations. 2) Limited holistic understanding of traffic context, most frameworks lack consideration of high-level and interpretable indicators (e.g., traffic pressure) in comprehensively assessing dynamic traffic scenarios, limiting their capacity for human-like contextual understanding. 3) Lack of transparent and interpretable decision logic, as many frameworks operate as black boxes with opaque reasoning processes, hindering human
Chen, Yanbo, Chen, Jiaqi, Yu, Huilong, Xi, Junqiang
Coordinate Attention-Driven Robust Multi-Object Tracking in Autonomous Vehicles: A Hybrid Framework of CA-YOLOv5 and ResNet-DeepSORT2025-01-730212/31/2025
With the increasing complexity of traffic conditions, the computational burden of multi-object tracking algorithms has grown, making it difficult to meet the requirements for tracking accuracy and real-time performance. In this paper, we proposed a road vehicle multi-object tracking method by improving and optimizing the YOLOv5 detection algorithm and the DeepSORT tracking algorithm. A Channel Attention(CA) mechanism is introduced into the existing YOLOv5 algorithm to construct the fusion algorithm CA-YOLOv5, and the feature extraction network structure of YOLOv5 is reconstructed by adding a prediction layer to improve the accuracy of vehicle detection. The ReID (Re-identification) network in DeepSORT algorithm is adopted as ResNet neural network to construct the fusion algorithm ResNet-DeepSORT. And it combined with data and feature enhancement, as well as high accuracy detection results of road vehicles. Thus, it improves the tracking accuracy and reduces the number of ID jumps to
Bo, Liu, Jing, Wu, Yanping, Zhou, Jing, Li
Real-Time Multidimensional Vehicle Dynamic Stability Domain Calculation and Its Application in Intelligent Vehicles2025-01-732412/31/2025
Vehicle stability is fundamental to the safe operation of intelligent vehicles, and real-time, high-accuracy calculation of the stability domain is crucial for maintaining control across the full range of driving conditions. Because the real stability domain is difficult to parameterize accurately and is shaped by multiple driving factors including vehicle-dynamics parameters and environmental conditions, existing approaches fail to capture the multidimensional couplings between time-varying driving inputs and the resulting stability boundaries. Moreover, these methods remain overly conservative owing to algorithmic limitations and cautious design assumptions, thereby restricting dynamic performance in complex scenarios. To address these limitations, this paper introduces a multidimensional vehicle dynamic stability region calculation framework under time-varying driving conditions and apply it into path tracking controller of intelligent vehicle. Sum-of-squares programming (SOSP) is
Wang, Chengye, Zhang, Yu, Hu, Xuepeng, Qin, Haipeng, Wang, Guoli, Qin, Yechen
Mode Identification and Collaborative Switch for the Front-Centralized and Rear-Distributed Electric Drive Vehicle Based on BP Neural Network2025-01-733512/31/2025
Distributed-drive electric vehicles (DDEVs) significantly enhance off-road maneuverability but suffer from compromised high-speed stability and robustness. This research introduces a front-centralized and rear-distributed (FCRD) architecture that synergistically leverages the advantages of each configuration. The electric-drive-wheel (EDW) on the rear suspension can provide three working modes: (a) Drive-connected mode, (b) Drive-disconnected mode, (c) Brake mode. It is the key actuator for vehicle mode-switching, which supports the vehicle with three driving modes: (a) DDEV, (b) front-wheel drive (FWD), (c) all-wheel drive (AWD). A hierarchical control architecture employs the upper-layer controller with Back Propagation Neural Network (BPNN) for mode identification and decision-making. The lower-layer controller enables the intelligent torque distribution and collaborative control of the motors. The control strategy is pre-trained in the VCU (vehicle control unit) with off-line data
Ding, Xiaoyu, Chen, Xinbo, Wang, Wei, Zhang, Jiantao, Kong, Aijing
CANet: A Lightweight Cross-Attention Guided Network for Unstructured Road Segmentation2025-01-730912/31/2025
To address the issues of multiple background interferences and blurred road boundaries in unstructured scene road segmentation tasks, a lightweight and precise unstructured road segmentation model based on cross-attention (CANet) is proposed. This model constructs an encoder using the lightweight neural network MobileNetV2. By doing so, it ensures light weight while enhancing the feature discrimination ability of unstructured roads, thus achieving efficient feature extraction. The decoder integrates the cross-attention mechanism and a low-level feature fusion branch. The attention mechanism improves the model’s perception of road boundaries by capturing long-distance context information in the feature map, thereby solving the problem of blurred edges. The low-level feature fusion branch enhances the detail accuracy and edge continuity of the segmentation results by incorporating high-resolution information from shallow features. Experimental results show that the proposed model attains
Wang, Xuewei, Cao, Guangyuan, Liang, Xiao, Li, Shaohua
Modeling on Self-Learning Driver Model under Extreme Driving Conditions2025-01-731512/31/2025
Ensuring the safe and stable operation of autonomous vehicles under extreme driving conditions requires the capability to approach the vehicle’s dynamic limits. Inspired by the adaptability and trial and error learning ability of expert human drivers, this study proposes a Self-Learning Driver Model (SLDM) that integrates trajectory planning and path tracking control. The framework consists of two core modules: In the trajectory planning stage, an iterative trajectory planning method based on vehicle dynamics constraints is employed to generate dynamically feasible limit trajectories while reducing sensitivity to initial conditions; In the control stage, a neural network enhanced nonlinear model predictive controller (NN-NMPC) is designed, which incorporates a self-learning mechanism to continuously update the internal vehicle model using trial-and-error data on top of mechanistic physical constraints, thereby improving predictive accuracy and path-tracking performance. By combining
Zhang, Xinjie, Xu, Long, Guo, Konghui, Zhuang, Ye, Hu, Tiegang, Mao, JingGang, Zeng, Qingqiang
Research Progress on Pavement Performance Prediction Models: A Review2025-99-024912/23/2025
Road maintenance plays a vital role in maintaining road conditions and ensuring safety, especially in a country with an extensive road network like China. To accurately predict pavement performance, optimize maintenance strategy, reduce cost and improve road efficiency, the paper systematically combed and evaluated the prediction model of pavement performance. Firstly, the importance of pavement maintenance and the background of pavement maintenance performance prediction model are described, and explicit models (mechanical-empirical model, stochastic process, time series analysis) and machine learning models (regression analysis, support vector machine, integrated learning, artificial neural network, deep learning) are introduced respectively. The basic principle, representative study, advantages and disadvantages of each model are introduced in detail. Comparative analysis shows that the traditional explicit model is simple and effective, easy to explain, but difficult to deal with
Ma, Muyun, Dong, Qiao, Lin, Yelong
Knowledge Modeling and Reuse of Concrete Structure Strengthening Solutions for Existing Buildings2025-99-020212/23/2025
Implementing knowledge modelling tools of concrete structure strengthening solutions for existing buildings addresses the urgent needs of urban renewal efforts. This paper thoroughly investigates the application of Natural Language Processing (NLP), and knowledge graphs for organizing and managing complex information related to building strengthening strategies. By developing an ontology model for solutions and supplementing it with methods for generating word vectors and annotating data, this study constructed a comprehensive framework for the management of strengthening solution knowledge. A case study on the partial structural strengthening validated the applicability of the proposed model in facilitating recommendations for similar cases and supporting solution design. This research under-scores the transformative impact of digital technologies and knowledge modelling on the efficiency and quality of urban renewal projects, contributing to the advancement of smart cities. The
Zhang, Zhuohao, Luo, Hanbin, Wu, Haozheng, Chen, Weiya
Experimental Research on MFL Detection Technology for Corrosion Damage of Prestressing Tendons in Bridges2025-99-020312/23/2025
Corrosion of prestressed tendons endangers the safety of bridges, but until now, there has been no effective method to solve the problem of detecting corrosion damage in prestressed tendons of concrete beams. To address this, a magnetic flux leakage detection experimental apparatus for corrosion damage in prestressed tendons based on the principle of magnetic flux leakage inspection has been developed. Using this apparatus, magnetic flux leakage tests were conducted on prestressed tendons after electrochemical corrosion, and the results were compared with simulation analysis to conduct a comparative study. In the experiments, the influence of corrosion severity, corrosion width, and the effect of stirrups on the characteristics of the magnetic flux leakage signals were studied. Magnetic signal feature values were extracted, and a quantification neural network model for corrosion damage was established, which is used to quantify the degree of corrosion damage in prestressed tendons. The
Wang, Peng, Gao, Min, Dong, Lei, Zhu, Junliang
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