Article
Automation & Control Systems
Vincent Mussot, Guillaume Mercere, Thibault Dairay, Vincent Arvis, Jeremy Vayssettes
Summary: This paper presents a parametric method based on Monte-Carlo Markov Chain techniques to accurately determine the maximum tire-road friction coefficient. The method combines the advantages of a maximum likelihood method and an adaptive Metropolis algorithm, requiring friction measurements and a tire model for calculation.
CONTROL ENGINEERING PRACTICE
(2022)
Article
Engineering, Mechanical
Wei Liu, Xiaowei Wang, Shuisheng Yu, Zhihao Xu
Summary: This paper investigates the tire-road friction coefficient estimation using an adaptive singular value decomposition unscented Kalman filter (ASVD-UKF) with a noise estimator. The ASVD-UKF method significantly reduces the average absolute error compared to the traditional UKF method, improving estimation accuracy. Experimental results show that the proposed ASVD-UKF method is practical and can provide a theoretical basis and experimental foundation for tire-road friction coefficient estimation.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2023)
Article
Engineering, Mechanical
Jia Ye, Zhifei Zhang, Jie Jin, Ruiqi Su, Bo Huang
Summary: The tire-road friction coefficient is crucial for vehicle safety systems. Existing methods have limited accuracy, while the proposed estimation method improves accuracy by adaptively adjusting tire stiffness and accurately identifies tire damage.
NONLINEAR DYNAMICS
(2023)
Article
Computer Science, Information Systems
Jun-Young Han, Ji-Hoon Kwon, Suk Lee, Kyung-Chang Lee, Hyeong-Jun Kim
Summary: In this study, a tire tread wear detection system is proposed that utilizes machine learning to accurately detect tire wear under real-road driving conditions. The proposed system includes an intelligent tire, a preprocessing component, and a detection component, and achieves an accuracy of 95.51% in real-road driving conditions.
Article
Chemistry, Analytical
Dongwook Lee, Ji-Chul Kim, Mingeuk Kim, Hanmin Lee
Summary: This paper presents a real-time road surface classification algorithm based on a deep neural network, trained on a database collected through an intelligent tire sensor system with a three-axis accelerometer installed inside the tire. By analyzing the learning results, it was found that using a convolutional neural network to train longitudinal and vertical axis acceleration signals achieves the optimal classification accuracy for real-time road surface type classification.
Article
Engineering, Electrical & Electronic
Xunjie Chen, Hrishikesh Sathyanarayan, Yongbin Gong, Jingang Yi, Hao Wang
Summary: This article presents a new scheme using embedded sensors to estimate the tire/road interaction. By developing physics-based and sensor models, the rubber deformation, contact pressure distribution, and relationship between longitudinal stress and external friction force are evaluated. Experimental results demonstrate the feasibility of using force-sensitive sensors to predict tire/road friction characteristics.
IEEE SENSORS JOURNAL
(2023)
Article
Materials Science, Multidisciplinary
Malal Kane, Ebrahim Riahi, Minh-Tan Do
Summary: This paper discusses the modeling of rolling resistance and the analysis of pavement texture effect, with experimental validation showing a good correlation between the model and actual results. The research also highlights the positive correlation between mean profile depth of surfaces and rolling resistance. Furthermore, it suggests the possibility of simplifying the model by neglecting the damping part in the constitutive model of rubber.
Article
Engineering, Multidisciplinary
Zheng Zhang, Chun-guang Liu, Xiao-jun Ma, Yun-yin Zhang, Lu-ming Chen
Summary: This paper presents a driving force coordination control strategy with road identification for multi-wheel distributed electric drive vehicles. By estimating tire-road forces and identifying the road friction coefficient, this control strategy can achieve accurate driving force coordination control under different driving conditions.
DEFENCE TECHNOLOGY
(2022)
Article
Engineering, Multidisciplinary
Masahiro Higuchi, Yosuke Suzuki, Tomohiko Sasano, Hiroshi Tachiya
Summary: This study investigates a method for measuring road friction coefficients using strains on the sidewalls of tires. The proposed method is confirmed to be able to accurately measure the load acting on a tire and friction coefficient of the tire grounding surface at low speeds and under full-slip conditions.
PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY
(2023)
Article
Engineering, Mechanical
Shiqing Huang, Haidong Wu, Konghui Guo, Dang Lu, Lei Lu
Summary: This article introduces an in-plane dynamic tire model with real-time simulation capabilities, which can accurately and efficiently express the mechanical characteristics of the tire.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Lei Zhang, Pengyu Guo, Zhenpo Wang, Xiaolin Ding
Summary: This article proposes a particle filter-based tire-road friction estimation method for four-in-wheel-motor-drive electric vehicles, using dual global positioning system and low-cost inertia measurement units. The method includes independent estimators for straight driving and cornering conditions, and a decision scheme to update the friction estimate based on tire dynamics states and force characteristics. The proposed method shows high accuracy, robustness, and computational efficiency.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Engineering, Mechanical
Hongyan Guo, Xu Zhao, Jun Liu, Qikun Dai, Hui Liu, Hong Chen
Summary: An estimation framework that combines vision and vehicle dynamic information is established to accurately obtain the peak tire-road friction coefficient. The framework collects information for the road ahead from an image captured by a camera and uses a lightweight convolutional neural network to identify the road type and its corresponding range of tire-road friction coefficients. An unscented Kalman filter (UKF) method is then used to estimate the tire-road friction coefficient value directly based on the dynamic vehicle states. The results from the road-type recognition and dynamic estimation methods are synchronized, and a confidence-based fusion strategy is proposed to obtain an accurate peak tire-road friction coefficient. Virtual and real vehicle tests confirm the effectiveness of the proposed fusion estimation strategy, which outperforms both general vision-based estimation methods and dynamic-based estimation methods.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Review
Engineering, Mechanical
Yan Wang, Jingyu Hu, Fa'an Wang, Haoxuan Dong, Yongjun Yan, Yanjun Ren, Chaobin Zhou, Guodong Yin
Summary: This study provides a comparative analysis of different methods widely utilized for TRFC estimation, including off-board sensors-based, vehicle dynamics-based, and data-driven-based methods. The research suggests that accurate knowledge of TRFC is crucial for optimizing driver maneuvers and improving the safety of intelligent vehicles.
CHINESE JOURNAL OF MECHANICAL ENGINEERING
(2022)
Article
Mathematics, Interdisciplinary Applications
Takashi Kuraishi, Satoshi Yamasaki, Kenji Takizawa, Tayfun E. Tezduyar, Zhaojing Xu, Ryutaro Kaneko
Summary: This paper presents a space-time isogeometric analysis framework for car and tire aerodynamics. The framework addresses the complexities of geometries, tire rotation, accurate representation of boundary layers, turbulent flow, aerodynamic interaction, NURBS mesh generation, and mesh quality improvement. The framework integrates various methods and techniques to achieve accurate computations at high resolutions.
COMPUTATIONAL MECHANICS
(2022)
Article
Engineering, Electrical & Electronic
Zhenqiang Quan, Bo Li, Shaoyi Bei, Xiaoqiang Sun, Nan Xu, Tianli Gu
Summary: This paper proposes a tire-road friction coefficient estimation method based on intelligent tire technology. Through finite element analysis and control variable method, the influence of sideslip angle on the voltage signal of each piezoelectric film under the rolling state of tire is analyzed, and the influence of load, tire pressure, vehicle speed, and slip ratio on the voltage signal of each piezoelectric film is also analyzed. Based on signal response analysis, prediction models are built and input into the brush tire model to solve the tire-road friction coefficient. The result shows that the estimation error percentage with genetic algorithm optimization is 5.14%, indicating the practicality of the friction coefficient estimation method.
SENSORS AND ACTUATORS A-PHYSICAL
(2023)
Article
Automation & Control Systems
Francesco Basile, Roberto Cordone, Luigi Piroddi
Summary: This novel framework introduces a method for supervisory control of timed discrete event systems using Time Petri nets, which can handle both logical and temporal specifications.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Engineering, Electrical & Electronic
S. Gelmini, Marco Centurioni, Nicola Pivaro, Silvia Strada, Mara Tanelli, Sergio Savaresi
IEEE VEHICULAR TECHNOLOGY MAGAZINE
(2022)
Article
Engineering, Electrical & Electronic
Francesco Zinnari, S. Strada, Mara Tanelli, Simone Formentin, Sergio M. Savaresi
Summary: With increasing concerns about global warming and urban pollution, battery electric vehicles (BEVs) are gaining popularity worldwide. However, high purchase prices, limited battery range, and insufficient public charging infrastructure hinder their market uptake. This study uses a massive real-world dataset to evaluate the rationality of range anxiety and assess the electrification potential of a fleet of more than 50000 vehicles. The results show that BEVs have the potential to meet the range needs of the existing fuel-powered vehicle fleet without altering owners' routines, and identify potential charging station sites based on real charging demand.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Automation & Control Systems
Federico Bianchi, Luigi Piroddi, Alberto Bemporad, Geza Halasz, Matteo Villani, Dario Piga
Summary: In various classification problems with a large number of features, the paper proposes a method to incorporate domain expert's preferences in feature selection to increase the interpretability and trustworthiness of the machine learning model.
EUROPEAN JOURNAL OF CONTROL
(2022)
Article
Automation & Control Systems
Gianluca Papa, Pierdomenico Schiano, Mara Tanelli, Giulio Panzani, Sergio M. Savaresi
Summary: This article investigates whether slip control can provide the same performance improvement in the aeronautical braking context as it does in the automotive field, and whether wheel slip can be effectively estimated and employed in a closed-loop braking controller without the need for additional sensors on the landing gear. A data-driven approach using a neural network architecture is proposed to solve the problem of wheel slip estimation. The results demonstrate that the proposed control approach can achieve superior performance and robustness, paving the way for the adoption of wheel slip ABS strategies in aircraft braking.
EUROPEAN JOURNAL OF CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Jessica Leoni, Mara Tanelli, Andrea Palman
Summary: This work presents an effective diagnosis and monitoring system for early detection of mechanical degradation in critical helicopter components. It utilizes a convolutional autoencoder and an unsupervised classifier to classify faults based on specific health indexes and flight parameters. The proposed approach leverages reconstruction error information to determine the most probable cause of faults and reduces false alarms through post-processing filtering.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Civil
Davide Maffiola, Stefano Longari, Michele Carminati, Mara Tanelli, Stefano Zanero
Summary: This paper introduces a blockchain-based decentralized framework, GOLIATH, designed to collect real-time information exchange between vehicles through the In-Vehicle Infotainment (IVI) system, addressing limitations of existing centralized crowd-sourcing solutions. By collecting position and neighbor information through a decentralized network and validating it using a unique consensus mechanism, the framework's robustness and safety properties are demonstrated in a simulated environment.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Jessica Leoni, Francesco Zinnari, Eugenia Villa, Mara Tanelli, Andrea Baldi
Summary: This paper presents an unsupervised regime recognition system for helicopters that can better handle the actual usage spectrum. The system demonstrates outstanding capabilities in recognizing standard, mixed regimes, and transients based on experimental data.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Hardware & Architecture
Jose Joaquin Mendoza Lopetegui, Gianluca Papa, Marco Morandini, Mara Tanelli
Summary: In ground vehicles, braking actuator degradation and tire consumption are not significant maintenance costs. However, in the aeronautical context, particularly for aircraft, they significantly contribute to maintenance costs. This study proposes a data-driven model that explores the relationship between brake and tire degradation and antiskid controller parameters.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Engineering, Aerospace
Jose Joaquin Mendoza Lopetegui, Gianluca Papa, Marco Morandini, Mara Tanelli
Summary: This study establishes a link between main landing gear shock absorber leakage and aircraft lateral stability. A high-fidelity multibody nonlinear aircraft simulator is developed and validated against experimental data, and two analytical models are also developed to quantify the impact of shock absorber leakage on aircraft lateral stability. The analysis reveals that shock absorber leakage can significantly affect aircraft lateral stability, especially at high velocities and highly damped nose wheel steering conditions. The models developed in this work can assist aircraft control system designers in designing more effective lateral stability controllers in the event of main landing gear shock absorber leakage.
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
(2023)
Article
Automation & Control Systems
Valentina Breschi, Chiara Ravazzi, Silvia Strada, Fabrizio Dabbene, Mara Tanelli
Summary: Mobility will be the core focus of future smart cities, requiring novel models that are smart, sustainable, and energy efficient. Electric vehicles are crucial for mobility transition, but effective policies are needed for widespread adoption. This study proposes a framework for assessing the spread of electric vehicles and evaluating the cost and benefits of policies.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2022)
Article
Automation & Control Systems
Lorenzo Sabug, Gian Paolo Incremona, Mara Tanelli, Fredy Ruiz, Lorenzo Fagiano
Summary: This paper investigates the simultaneous design of active attitude control and passive attitude compensation mechanism for a spacecraft to satisfy practically-motivated mission objectives and constraints. The expressions of these fitness-related metrics with respect to the design variables are not analytically available, due to the nontrivial interactions between the spacecraft components and the interactions with the environment. The proposed black-box optimization (BBO)-based approach combines learning and optimizing the objective and constraint functions by design of experiments, and it shows the capability to provide a design with the best tracking performance while satisfying ground station communication requirements and power budget.
CONTROL ENGINEERING PRACTICE
(2023)
Article
Engineering, Aerospace
Francesco Zinnari, Giovanni Coral, Mara Tanelli, Gabriele Cazzulani, Andrea Baldi, Ugo Mariani, Daniele Mezzanzanica
Summary: Helicopters usage monitoring has become important due to safety and cost management implications. This study proposes a multivariate time-series segmentation framework using supervised learning algorithms, sliding windows, and stacking ensembles to accurately predict flight regimes. The approach is validated on a large dataset of labeled load flights from two helicopter models, demonstrating its efficacy in predicting 49 different maneuver types.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
Article
Computer Science, Information Systems
Tommaso Dolci, Fabio Azzalini, Mara Tanelli
Summary: The increasing use of semantic text analysis systems has made natural language understanding a crucial task. However, these systems often display social bias and lack transparency, particularly gender bias, which reinforces social stereotypes. This study proposes a new metric, called bias score, to measure gender bias in sentence embeddings. Experimental results show that the metric can identify gender-stereotyped sentences and aid in reducing bias in text corpora to improve fairness and accuracy in natural language understanding tasks. Additionally, the study compares the proposed approach with traditional methods for reducing bias in embedding-based language models.
DATA SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Biomedical
Jessica Leoni, Silvia Carla Strada, Mara Tanelli, Alice Mado Proverbio
Summary: MIRACLE is a novel BCI system that decodes patients' minds from elicited potentials using functional data analysis and machine-learning techniques. It recognizes 10 different semantic categories of imagined stimuli and has been validated on an extensive dataset collected from 20 volunteers. The importance of each EEG channel in the decision-making process of the classifier has been quantified.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
