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Highlights Polypropylene‐reinforced composites mostly exhibit a trade‐off between stiffness and toughness. In this study, the impact of incorporating polyolefin elastomer (POE) and Polyvinyl Alcohol (PVA) fibers as individual and hybrid reinforcement to glass fiber‐reinforced polypropylene composite in the presence of a compatibilizer was explored. The main focus was on achieving an optimal stiffness/toughness balance. Several ternary and quaternary hybrid composite systems were developed and analyzed. All components were melt‐blended using a twin‐screw extruder and then injection molded for subsequent mechanical and morphological analysis. A significant stiffness of approximately 2.4 GPa Young's modulus, and notched izod impact strength ranges between 250 and 400 J/m can be attained with the quaternary hybrid system, PP/GF/POE/PVA fibers. Its performance can be controlled by altering the POE/PVA fibers ratio to achieve the targeted stiffness/toughness balance. These values represent a remarkable increase, with Young's modulus being 120% higher and the izod impact strength surpassing that of the control sample by over 800%. The achieved results aligned with the requirements of the automotive industry, so the developed composites could have a high potential for automotive parts. Ternary and quaternary hybrid systems were fabricated by a twin‐screw extruder. An improvement of 120% in stiffness and 800% in toughness was observed. Distinct micromechanical deformation mechanisms were identified. Remarkable stiffness‐toughness balance was achieved. Reduction in weight makes the system suitable for automotive applications. [ABSTRACT FROM AUTHOR]

The transition to sustainable mobility is progressing rapidly, with electric vehicles (EVs) playing a pivotal role in lowering greenhouse gas emissions and reducing the reliance on fossil fuels. At the core of this transformation are lithium-ion batteries (Li-ion), valued for their high energy density and long cycle life. However, the increasing demand for EVs necessitates continuous improvements in battery technology and the integration of advanced systems to ensure safe, efficient, and reliable performance. This review offers a clear and comprehensive summary of the latest innovations in Li-ion battery chemistry, battery pack design, and Battery Management System (BMS) functionalities. Unlike other reviews, this work emphasizes practical considerations, such as voltage, power, size, and weight for commercial vehicles. It also addresses integrated safety solutions, including disconnection systems and pre-charge circuits, which are vital for enhancing battery safety and lifespan. Additionally, it explores key BMS functions, like cell monitoring, balancing, and thermal management, all crucial for maximizing battery performance and ensuring safe operation. By consolidating current research and industry practices, this article provides essential information in a concise yet accessible format. It enables researchers to quickly gain a solid understanding of the field, distinguishing itself from reviews that focus on narrower aspects of battery technology. Its holistic approach delivers valuable insights for improving EV charging systems' safety and performance, making it a highly useful resource for researchers and industry professionals alike. [ABSTRACT FROM AUTHOR]

Identifying objects in real-time is a technology that is developing rapidly and has a huge potential for expansion in many technical fields. Currently, systems that use image processing to detect objects are based on the information from a single frame. A video camera positioned in the analyzed area captures the image, monitoring in detail the changes that occur between frames. The You Only Look Once (YOLO) algorithm is a model for detecting objects in images, that is currently known for the accuracy of the data obtained and the fast-working speed. This study proposes a comprehensive literature review of YOLO research, as well as a bibliometric analysis to map the trends in the automotive field from 2020 to 2024. Object detection applications using YOLO were categorized into three primary domains: road traffic, autonomous vehicle development, and industrial settings. A detailed analysis was conducted for each domain, providing quantitative insights into existing implementations. Among the various YOLO architectures evaluated (v2–v8, H, X, R, C), YOLO v8 demonstrated superior performance with a mean Average Precision (mAP) of 0.99. [ABSTRACT FROM AUTHOR]

This paper introduces a new hybrid tool to classify manufacturing errors in production processes. Our tool is a hybrid procedure based on the combination of the parsimonious AHP method with a clustering method. Parsimonious AHP permits to use a much smaller number of pairwise comparisons with respect to classical-AHP. At the same time, the use of a clustering method, allows for the assignment of the alternatives on the basis of their closeness to each other and not on the basis of some reference profiles as happens in several other methods. Furthermore, some veto systems help the decision maker to better define how the errors belong to different classes in a participative way. The approach has been tested in a largest automotive plants in Italy. This approach has provided the company with a participative and robust theoretically funded tool that supports the understanding of the seriousness of the manufacturing errors. From a methodological point of view, the hybrid tools, for the first time, combine the newly developed parsimonious AHP with a clustering method. From a practical point of view, this paper has introduced a new tool to classify and improve the production quality of the processes to an extremely competitive sector. [ABSTRACT FROM AUTHOR]

Accurate dimensional characterization of automotive components is extremely critical, as assorted components are assembled to develop the final product with structural complexities. This article demonstrates the significance of equitable dimensional measurements in the automotive sector; a considerable economic and industrial force for the socio-economic growth of a country. It emphasizes the requirement of precise dimensional measurements for optimal quality assurance in automotive industries, leading toward extensive customer satisfaction, elevated productivity, and immense employment opportunities. This article elaborates on the momentousness of dimensional metrology in the automotive sector through substantial case studies and explicates the importance of intercomparisons in metrology. The present manuscript deliberates the evolution of industrial dimensional metrology for in-line/near-line measurements and fast automation in the automotive segment and delineates the role of CSIR-National Physical Laboratory India (CSIR-NPLI); The National Measurement Institute of India, in catering to the ever-augmenting dimensional metrology demands of the automotive sector. [ABSTRACT FROM AUTHOR]

The presence of plastics in the automotive industry is increasingly significant due to their lightweight nature, which contributes to reducing fuel consumption and CO2 emissions while improving versatility and mechanical properties. Polypropylene (PP) and other polyolefins are among the most commonly used materials, especially for components such as bumpers. The use of composite materials, i.e., a combination of different polymers, improves the properties through synergistic effects, thereby also improving the performance of the final product. In the automotive industry, PP reinforced with 20% talc or CaCO3 is commonly used. The mechanical recycling of polypropylene bumpers is the most common type of recycling. However, challenges arise during this process, such as the presence of impurities like paint, chemical contaminants from previous use, and polymeric impurities from different polymers mixed into the polymer matrix, among others. Paint affects both the aesthetic quality and the mechanical and intrinsic properties of the recycled material. This review aims to analyze the main methods reported in the literature, focusing on those with low environmental impact. Furthermore, these methods are classified according to their capacity, effectiveness, substrate damage, environmental hazards, and economic feasibility. It also aims to offer a comprehensive overview of the mechanical recycling of plastic waste in the automotive industry. [ABSTRACT FROM AUTHOR]

Copyright of Coatings & Protection / Tuceng yu Fanghu is the property of Coating & Protection Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To quantify the head and chest injury metrics associated with a pediatric anthropomorphic test device (ATD) in rearward-facing infant child restraint system (CRS) models positioned directly behind a center console during frontal impact sled tests. Methods: Sled tests using the Federal Motor Vehicle Safety Standard (FMVSS) 213 frontal crash pulse were performed. The test buck comprised a second row middle seat and center console from the same 2023 model mid-size SUV spaced as per the in-vehicle relative dimensions, a force plate covered with an automotive floor mat, a post-mounted shoulder belt simulating the in-vehicle roof-mounted seatbelt and an array of high-speed cameras. The 12-month-old Child Restraint/Air Bag Interaction (CRABI-12) ATD was seated in one of two rearward-facing infant CRS models (model A, rigid lower anchors; model B, flexible lower anchors), which was installed with either the base (support leg or no support leg; attached using lower anchors or the seatbelt) or without the base (attached using the European or US belt path). Conductive foil was attached to the rear surface of the center console and to the shell of the CRS and/or base to quantify contact. The vehicle seat was replaced every second test and the center console was replaced when damaged. Results: For sled tests of the CRS models with a base attached using lower anchors, there was no contact of the CRS with the center console when the support leg was used, and all head and chest injury metrics were reduced compared to the tests of CRS with no support leg. However, there was contact between the CRS and the center console when the base of the CRS models was attached using the seatbelt, which typically increased head and chest injury metrics compared to the lower anchor attachment method. For CRS model B with the base attached using either the lower anchors or the seatbelt but no support leg, head acceleration 3 ms clip exceeded the injury assessment reference value (IARV) of 80 g. All tests resulted in HIC36 values below the IARV of 1000. The tests of the CRS models without a base using the European belt path did not result in contact and had the lowest head and chest injury metrics of all tests, which were all below IARVs. For the tests of the CRS models with the base attached using the seatbelt and tests using the US belt path, chest acceleration 3 ms clip values exceeded the IARV of 60 g. Peak normal support leg reaction forces in this study ranged from 3.6 to 4.3 kN. Conclusions: The rearward-facing CRS models with a base and a support leg attached using lower anchors, or without a base using the European belt path, resulted in the lowest head and chest injury metrics due to not contacting the center console. [ABSTRACT FROM AUTHOR]

Numerous attempts have been made to create a secure system that meets the criteria and requirements of the automotive vehicle development life cycle. However, a critical gap exists in the secure development lifecycle, particularly concerning the development and maintenance of software after the vehicle has been sold by the manufacturer. This step is often overlooked by original equipment manufacturers (OEMs), especially after the expiration of the vehicle warranty period, given the cost that it will require to update and test the software in their vehicles. This paper addresses the issues that affect current and future vehicle cybersecurity, during the maintenance of cybersecurity, and how the neglect of it could end up creating hazards for the vehicle owner or other road users. To accomplish this, we will employ the technology adoption model (TAM) as a theoretical framework, which is used to understand and predict how organizations adopt technology. Thus, through qualitative and quantitative research, including text mining, we identify the challenges in the adoption and diffusion of cybersecurity maintenance in the automotive sector and its supply chain. In addition, we propose possible solutions on how to maintain a level of security that will benefit road users, OEMs and regulators, covering the cybersecurity needs for the vehicle's usable life, taking into account the vehicle's heterogeneity of components and technology, connectivity, environmental impact and cost of production and maintenance of a vehicle. [ABSTRACT FROM AUTHOR]

The automotive industry is under significant pressure to curb greenhouse gas emissions from their products as the negative impacts on the environment are increasingly apparent. As such, there is significant interest within the industry to develop low-carbon footprint materials. Natural fiber composites are therefore appealing due to both their decreased carbon footprint, as well as their relatively low cost, low density, and beneficial acoustic properties. This research specifically focuses on the use of paper fibers as composite reinforcements. Paper offers additional benefits over other natural fiber types in that it is globally available, does not compete with food resources, and has well-established quality control techniques. The goal of this work was to study the manufacturing of polypropylene (PP)-paper fiber composites using a hybrid wet-lay/compression molding processing. The processing parameters studied included molding temperature, molding duration, and molding pressure. Paper composites were characterized for properties including flexural strength, impact strength, and water uptake rate. An optimal set of processing parameters was identified in which cycle time and energy inputs (e.g. temperature and pressure) were minimized while retaining desired material properties. [ABSTRACT FROM AUTHOR]

This review article provides a deep dive into the diverse landscape of Additive Manufacturing (AM) technologies and their significant impact on the automotive and aviation sectors. It starts by exploring various AM methodologies such as Fused Deposition Modeling (FDM), Stereolithography (SLA), Digital Light Processing (DLP), Selective Laser Sintering (SLS), Metal Jet Fusion (MJF), Binder Jetting (BJ), and Directed Energy Deposition (DED), with a specific focus on their applicability, strengths, and challenges within these industries. The article then delves into the practical applications of AM in rapid prototyping, functional part production, and component repair. The results highlight the versatility and precision of SLA and DLP, the strength and durability of SLS, and the potential of metal-based technologies like LPBF, SLM, EBM, and DMLS in manufacturing critical components. The integration of AM with automotive and aviation design underscores the transformative nature of these technologies, driving advancements in lightweight, intricate, and high-performance components. The review concludes by emphasising AM's significant opportunities and acknowledging the ongoing challenges in material properties, post-processing, and production scalability, thereby underscoring the necessity for future research and innovation in these sectors. [ABSTRACT FROM AUTHOR]

The wide spread adoption of heat pipes in automotive and aviation application is hampered by the fact that they require protection against frost damage caused by the repetitive exposure to subzero temperatures. Here, we present heat pipe with ethylene glycol and water as working fluid that can prevent damage to the copper container on exposure to cyclic freezing and thaw. In this research, different mass concentrations of water and ethylene glycol have been tested for freeze protection of heat pipes. Experimental investigations have shown that 3.5% by mass concentration of ethylene glycol in water is able to sustain the cyclic freezing and avoid any damage to the copper pipe. In this research, the freezing and heating cycle is set to operate between -40 °C and 90 °C. Heat pipes used for testing in this research were 6 mm in diameter and 150 mm long with copper fiber spiral wick. Thermal performance tests were carried out on these heat pipes with charging ratio of working fluid varying between 10% to 30% by volume. It is observed that the thermal resistance of heat pipe with working fluid charging ratio in range of 10% to 25% by volume varies from 0.6 °C/W to 0.2 °C/W for the rate of heat transfer of 10 W-50 W. While the charging ratios beyond 25% have shown the higher thermal resistance in the range of 0.8 to 1.0 °C/W for similar rate of heat transfer. All the samples were subjected to 150 freezing and thaw cycles and did not show any signs of frost damage. Accelerated life tests were performed on these heat pipes for up to 500 h and did not presented any signs of degradation in their thermal performance. [ABSTRACT FROM AUTHOR]

The article presents a detailed perspective on project management in the automotive industry, with an emphasis on effective coordination of resources to meet quality and safety standards. During the work, the APQP (Advanced Product Quality Planning) method is presented and analyzed, an essential process in the automotive industry for quality planning and defect prevention, structured in stages of planning, design, development and validation. The V3P (Vehicle Performance Plan Process) framework created and adopted by the Renault company is explained as a project planning method based on milestones, intended to ensure the performance and efficiency of the final product, consistent with cost, quality and schedule objectives. The document also includes a case study related to the management of a nonconformity, exemplifying the use of the 8D problem-solving method to identify and correct root causes, thus preventing the recurrence of non-conformities in production. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Technological progress is leading to the incorporation of digital twinning and artificial intelligence, causing engineering design and scientific procedures to transition into an AI-driven age. Digital twinning and modeling have been increasingly included into engineering design optimization, particularly via processes like topology optimization and generative design, to provide modern design solutions efficiently. The integration of topology optimization with additive manufacturing is revolutionizing the design optimization process in the automotive industry, where there is a pressing demand for lightweight design and improving production efficiency. A design optimization methodology has been developed to optimize an Automotive Seatbelt Bracket subjected to dynamic load and fatigue. The innovative design is lighter and consolidates the entire assembly into a single body that can be manufactured using additive manufacturing. [ABSTRACT FROM AUTHOR]

As the number of electronic control units (ECUs) in vehicles continues to grow, exchanging physical components between original equipment manufacturers (OEMs) and ECU suppliers presents logistical challenges, impeding the pace of development and leading to accumulation of costs. In this work, we introduce a conceptual framework that enables remote testing of geographically dispersed ECUs over the Internet. Pertinent to the Internet of Things (IoT) sphere, where interconnection of distributed components is hampered by the inherent challenges of latency, we propose a hybrid synchronous methodology that combines asynchronous test management with time synchronization mechanisms to mitigate the delay impact within the distributed environment. Additionally, we discuss challenges and prospects associated with this approach. [ABSTRACT FROM AUTHOR]

The automotive industry is undergoing a major transformation due to digital advancements. This paper examines the global market for automotive digital transformation technologies, focusing on how digital technology is reshaping manufacturing, operations, and consumer experiences, with projections to 2030. By analyzing trends, growth drivers, challenges, and opportunities, the study provides a comprehensive view of the evolving landscape. Combining insights from industry reports, market studies, and expert analyses, it explores advancements in AI, blockchain, robotics, and cybersecurity, assessing potential models that could influence the future. Findings show that these technologies significantly enhance vehicle connectivity, safety, and production efficiency.

In response to the escalating global demand for energy spurred by industrialization, nations are increasingly turning to alterna-tive fuel sources. Among these alternatives, hydrogen stands out for its remarkable efficiency and environmental benefits. With its potential to significantly reduce fuel consumption and air pollution, particularly in the transportation sector, hydrogen has garnered widespread attention as a promising solution to our energy challenges. This study focuses on the development of a dynamic model for an internal combustion engine powered by hydrogen (H2-ICE). By intricately dissecting and modeling the various components of the engine and their interactions, we aim to create a comprehensive simulation platform. This platform will enable us to accurately predict and analyze the performance of H2-ICE under different operating conditions. Through the utilization of advanced simulation tools like Matlab Simulink, we can validate the efficacy and reliability of our model, providing valuable insights into the behaviour of hydrogen-powered engines. Furthermore, the implications of this research extend beyond mere simulation. The developed model opens up avenues for further exploration and innovation in the field of hydrogen propul-sion. For instance, it lays the groundwork for the development of sophisticated control systems tailored specifically for H2-ICE applications. Additionally, the effectiveness of the proposed model in terms of air intake dynamics, pressure, temperature, fuel injection response, combustion efficiency, and overall engine performance, such as power output, torque, and engine velocity are illustrated through simulation results using Matlab Simulink.

A packaged transmit array (TA) antenna is designed for automotive radar applications operating at 77 GHz. The compact dimensions of the proposed configuration make it compatible with standard quad flat no-lead package (QFN) technology. The TA placed inside the package cover is used to focus the field radiated by a feed placed in the same package. The unit cell of the array is composed of two pairs of stacked patches separated by a central ground plane. A planar patch antenna surrounded by a mushroom-type EBG (Electromagnetic Band Gap) structure is used as the primary feed. An analytical approach is employed to evaluate the primary parameters of the suggested TA, including its directivity, gain and spillover efficiency. The final design has been refined using comprehensive full-wave simulations. The simulated gain is 14.2 dBi at 77 GHz, with a half-power beamwidth of 22°. This proposed setup is a strong contender for highly integrated mid-gain applications in the automotive sector.

This article focuses on a thorough review of the utilisation of the wavelet transform in the analysis of durability and structural health monitoring in automotive components. The evolving trend in research in automotive engineering structural integrity analysis has shifted from traditional laboratory experiments to a focus on signal processing and simulation-based methodologies over the past 30 years. This shift has motivated researchers to explore the application of the wavelet transform for signal processing in the analysis of structural integrity. The study is divided into five sections: the first section provides an introduction to durability analysis in automotive applications and highlights the advancement of research trends. The following section discusses the time and frequency domain signal processing approaches and their limitations. Next, the third section provides the theoretical background of the wavelet transform, including the continuous and discrete wavelet transforms. The fourth section emphasises the applications of wavelet transform as a signal processing tool in the durability and structural health monitoring of automotive components. Lastly, the summary highlights the importance of wavelet transform as a signal processing tool in automotive applications.

Modern vehicles are increasingly interconnected through various communication channels, which requires secure access for authorized users, the protection of driver assistance and autonomous driving system data, and the assurance of data integrity against misuse or manipulation. While these advancements offer numerous benefits, recent years have exposed many intrusion incidents, revealing vulnerabilities and weaknesses in current systems. To sustain and enhance the performance, quality, and reliability of vehicle systems, software engineers face significant challenges, including in diverse communication channels, software integration, complex testing, compatibility, core reusability, safety and reliability assurance, data privacy, and software security. Addressing cybersecurity risks presents a substantial challenge in finding practical solutions to these issues. This study aims to analyze the current state of research regarding automotive cybersecurity, with a particular focus on four main themes: frameworks and technologies, standards and regulations, monitoring and vulnerability management, and testing and validation. This paper highlights key findings, identifies existing research gaps, and proposes directions for future research that will be useful for both researchers and practitioners. [ABSTRACT FROM AUTHOR]

Recent advancements in vehicle technology have stimulated innovation across the automotive sector, from Advanced Driver Assistance Systems (ADAS) to autonomous driving and motorsport applications. Modern vehicles, equipped with sensors for perception, localization, navigation, and actuators for autonomous driving, generate vast amounts of data used for training and evaluating autonomous systems. Real-world testing is essential for validation but is complex, expensive, and time-intensive, requiring multiple vehicles and reference systems. To address these challenges, computer graphics-based simulators offer a compelling solution by providing high-fidelity 3D environments to simulate vehicles and road users. These simulators are crucial for developing, validating, and testing ADAS, autonomous driving systems, and cooperative driving systems, and enhancing vehicle performance and driver training in motorsport. This paper reviews computer graphics-based simulators tailored for automotive applications. It begins with an overview of their applications and analyzes their key features. Additionally, this paper compares five open-source (CARLA, AirSim, LGSVL, AWSIM, and DeepDrive) and ten commercial simulators. Our findings indicate that open-source simulators are best for the research community, offering realistic 3D environments, multiple sensor support, APIs, co-simulation, and community support. Conversely, commercial simulators, while less extensible, provide a broader set of features and solutions. [ABSTRACT FROM AUTHOR]

Adhesive bonds, including car putty coatings, are used in the construction of modern motor vehicles. Therefore, it is important to improve methods that allow nondestructive evaluation of the properties of these joints. The main objective of this study was to evaluate selected properties of putty coatings such as the width of the applied coating and adhesion to the substrate based on changes in ultrasonic wave parameters. The research was carried out in two stages. In the first, the values of the surface wave amplitude were determined as a function of the width of the coating to the substrate. It was found that as the width of the coating increases, the amplitude of the surface wave pulse decreases. The second stage involved correlation studies to relate the reflection coefficient |r| to the adhesion of the coating to the substrate. Based on the results, it was found that as the value of the reflection coefficient decreases, the value of the coating's adhesion to the substrate increases. The determined values of this parameter range from 0.30 to 0.86, which correspond to the adhesion of the range 1.51 to 18.97 MPa. The obtained test results have practical significance and can be used in evaluating the condition of coatings in vehicle body repair shops. [ABSTRACT FROM AUTHOR]

Although scholars often portray Mexico as caught in the "middle-income trap," this description obscures important subnational variation in patterns of economic development. We leverage this variation by examining the case of Querétaro, a state with high levels of structural transformation and economic growth since the 1980s. We reconstruct the historical trajectory of its two leading industries, automotive and aerospace. We observe that while both have cumulatively delivered real developmental gains, each also exhibits a complex mix of strengths and weaknesses. We further find that these industries' advancements and hindrances over time appear closely related to the kinds of "partial" coalitions — groups of actors from the state, private firms, and civil society organizations such as labor unions — which participate in industry-level decision-making. Both industries saw periods of "upgrading" when the partial coalitions supporting them were expanding their cognitive diversity while avoiding gross imbalances in the distribution of power among their members. They likewise experienced periods of stagnation when either of those coalition conditions underwent major change. These findings suggest that debates on escaping the middle-income trap should consider how changes in partial political coalitions affect policy models and options. [ABSTRACT FROM AUTHOR]

Structural automotive components are extensively made of aluminum alloy forgings, due to the elevate strength and low weight required. These products are frequently subjected to recrystallization. Recrystallization, often limited to surface or forging portions, is expected to reduce its tensile strength and corrosion resistance, but the literature is scarce on this subject. For a more comprehensive understanding, the present research studied the corrosion behavior of samples collected from EN AW 6082‐T6 forged components, designed to expose both recrystallized and not recrystallized surfaces to the corrosive environment. Several standardized corrosion tests (i.e., PV 1113, ISO 11846, and VW 96380) were applied to assess the most representative with respect to real field exposure. Tensile tests were performed in four different conditions, recrystallized and not recrystallized specimens in an as‐forged state or after corrosion. The recrystallization led to a reduction in tensile properties, but this gap was compensated by a higher corrosion resistance than the not recrystallized samples. Consequently, the mechanical properties became comparable after the corrosion test. [ABSTRACT FROM AUTHOR]

Connected components in automobiles face cybersecurity threats if not adequately protected. The Controller Area Network (CAN), the primary communication protocol in vehicular systems, lacks essential security mechanisms, necessitating heightened cybersecurity awareness in the automotive industry. This paper presents a methodology to enhance cybersecurity within the CAN network, including a comparative study with benchmark results. Our research focuses on improving multiclass Intrusion Detection Systems (IDSs) for the CAN network. We introduce a hybrid deep learning model tailored for precise multiclass classification of attacks, an Artificial Neural Network (ANN)-based Light Gradient Boosting Machine (LightGBM) IDS that incorporates a comprehensive set of features. The evaluation on diverse cyber-attacks from the Car-Hacking dataset demonstrates the model's effectiveness. This study contributes to the field by offering a brief assessment of current state-of-the-art methods and presenting a novel approach to bolster cybersecurity. The proposed IDS serves as a valuable reference for ensuring the cybersecurity of automotive systems. [ABSTRACT FROM AUTHOR]

Climate change originating from anthropogenic activities is a key challenge of our time, with unsustainable mobility patterns representing one of the main contributors to polluting emissions in the atmosphere. The situation is particularly critical in urban areas, where traditional mobility paradigms based on private mobility and endothermic vehicles lead to road congestion, environmental degradation, health issues, and economic loss. Many policies are being implemented worldwide to support the envisaged transition to cleaner mobility paradigms, like the EU Fit for 55 Package, which is bound to ban sales of gasoline- and diesel-powered vehicles by 2035. In this context, electrification emerges as the most promising alternative, although battery electric vehicles represent a growing yet small niche, and the market is still expressing many concerns. Through a case study of Italy, a country with a strong automotive tradition and severe air pollution in urban centers, this research investigates consumer attitudes and barriers to electric vehicle adoption, providing crucial insights for future policy and market strategies. This study consists of a survey-based empirical investigation (N = 1533). The findings suggest that skepticism is still strong and requires a joint effort (carmakers, government, media) to frame sound communicational strategies and policies addressing market apprehensions about actual environmental performance, economic convenience, range anxiety, and availability of adequate charging infrastructures. [ABSTRACT FROM AUTHOR]

The present paper aims to broaden the field of application of the phenomenological model proposed by the authors in a previous study (ICP model) and to assess the shear properties of a recycled 30 wt.% talc-filled polypropylene (TFPP) and a recycled 30 wt.% short glass fiber-reinforced polypropylene (SGFPP), used in the automotive industry. The materials were produced by injection molding employing post-industrial mechanical shredding of recycled materials. In particular, Iosipescu shear tests adopting the American Standard for Testing Materials (ASTM D5379) at three different operating temperatures (−40, 23 and 85 °C) were performed. The strain was acquired using a Digital Image Correlation (DIC) system to determine the map of the strain in the area of interest before failure. Lower operating temperatures led to higher shear chord moduli and higher strengths. Recycled SGFPP material showed higher mechanical properties and smaller strains at failure with respect to recycled TFPP. Finally, the ICP model also proved to be suitable and accurate for the prediction of the shear behavior of 30 wt.% SGFPP and 30 wt.% TFPP across different operating temperatures. [ABSTRACT FROM AUTHOR]

Monitoring the psychophysical conditions of drivers is crucial for ensuring road safety. However, achieving real-time monitoring within a vehicle presents significant challenges due to factors such as varying lighting conditions, vehicle vibrations, limited computational resources, data privacy concerns, and the inherent variability in driver behavior. Analyzing driver states using visible spectrum imaging is particularly challenging under low-light conditions, such as at night. Additionally, relying on a single behavioral indicator often fails to provide a comprehensive assessment of the driver's condition. To address these challenges, we propose a system that operates exclusively in the far-infrared spectrum, enabling the detection of critical features such as yawning, head drooping, and head pose estimation regardless of the lighting scenario. It integrates a channel fusion module to assess the driver's state more accurately and is underpinned by our custom-developed and annotated datasets, along with a modified deep neural network designed for facial feature detection in the thermal spectrum. Furthermore, we introduce two fusion modules for synthesizing detection events into a coherent assessment of the driver's state: one based on a simple state machine and another that combines a modality encoder with a large language model. This latter approach allows for the generation of responses to queries beyond the system's explicit training. Experimental evaluations demonstrate the system's high accuracy in detecting and responding to signs of driver fatigue and distraction. [ABSTRACT FROM AUTHOR]

This paper explores and presents the importance and application of the Design of Experiments (DOE) method in scientific and industrial research. It analysed the theoretical foundations of the method in detail and then illustrated concrete examples as well as benefits of its use in an automotive environment. The introduction outlined the key principles and objectives of the DOE method, including optimisation of experimental processes and minimisation of variability of results. The publication focused on the significant impact of this method in improving the efficiency of experiments and achieving reliable and reproducible results. The practical part of the paper presented concrete examples of the application of the DOE method in the automotive industry. Through this case study, it was highlighted how the DOE method enables the systematic investigation of the influence of different factors on experimental results, leading to a better understanding of the phenomena under investigation and an increase in process efficiency. Emphasis was also placed on the practical aspects of using the DOE method, including the design of experimental plans and the analysis of the data obtained. The case study outlined the challenges and benefits that can arise when implementing this method and presents new perspectives for future research in statistical experimental analysis. Overall, this research paper can be considered an important resource for researchers and practitioners seeking effective methods for planning and conducting experiments to achieve significant research results in various disciplines. [ABSTRACT FROM AUTHOR]

Variable valve actuation (VVA) systems based on electromechanical valve actuators (EMVAs) enable a flexible operation and management of internal combustion engines that results in a reduction of pollutant emission and fuel consumption while improving engine power and efficiency. However, to achieve the benefits promised by EMVAs, it is of utmost importance to guarantee the catch of the valve by the system magnets with an acceptable impact speed of the valve at the end‐strokes. Valve catching failures can severely damage engine valves while high impact speeds induce mechanical wear and unacceptable noise. The soft landing control of the valve at the end‐strokes is challenging because of system nonlinearities, parameter uncertainties, and external disturbances. The complexity of the control design increases further when industrial cost‐effective solutions are sought. This paper presents a novel solution for reducing the impact speed of the valve without the use of costly position valve sensors. The proposed solution leverages on a small hydraulic damper (HD) embedded into the EMVA and a feedback cycle‐by‐cycle controller. The passive element reduces the impact speed while the controller adjusts at every engine cycle the EMVA coil currents based on pressure peaks detected in the HD to guarantee valve catching while steering the impact speed toward its minimum. The design of the controller exploits a physics‐based model that maps the pressure peak in the HD onto the impact speed and a gradient descent algorithm is adopted searching the minimum. The closed‐loop permanence is assessed in simulation by using an experimentally validated EMVA model and considering detailed dynamics of the HD. Numerical results show the ability of the proposed control architecture to reduce the impact speed and its robustness to parameters variations (oil temperature) and external disturbances (gas pressure forces). [ABSTRACT FROM AUTHOR]

Purpose: This paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19). Design/methodology/approach: A qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities. Findings: Results highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed. Research limitations/implications: The study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample. Practical implications: Findings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach. Originality/value: This research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation. [ABSTRACT FROM AUTHOR]

With advancements in autonomous driving technology, the variety of activities that can be performed in a vehicle has increased. This improves the possibility of watching virtual reality (VR) content on a head-mounted display (HMD). However, unlike VR used in stationary environments, in-car VR can lead to discomfort and motion sickness due to the vehicle movements. Additionally, the obstruction of the outside view during driving may cause user anxiety. In this study, we investigated, for the first time, the effect of dynamic road environments, such as turns, stops, and speed bumps, on the in-car VR experience. Based on our findings, we included situational awareness (SA) cues in the in-car VR content to help users perceive their surroundings and improve the user experience. We conducted a user study with thirty participants to validate the impact of these cues. Consequently, we discovered that the Dynamics cue, which provides SA information while maintaining the context of the VR content, improves user immersion and trust while easing VR motion sickness. [ABSTRACT FROM AUTHOR]

A packaged transmit array (TA) antenna is designed for automotive radar applications operating at 77 GHz. The compact dimensions of the proposed configuration make it compatible with standard quad flat no-lead package (QFN) technology. The TA placed inside the package cover is used to focus the field radiated by a feed placed in the same package. The unit cell of the array is composed of two pairs of stacked patches separated by a central ground plane. A planar patch antenna surrounded by a mushroom-type EBG (Electromagnetic Band Gap) structure is used as the primary feed. An analytical approach is employed to evaluate the primary parameters of the suggested TA, including its directivity, gain and spillover efficiency. The final design has been refined using comprehensive full-wave simulations. The simulated gain is 14.2 dBi at 77 GHz, with a half-power beamwidth of 22°. This proposed setup is a strong contender for highly integrated mid-gain applications in the automotive sector. [ABSTRACT FROM AUTHOR]

Close following to lead vehicles is associated with increased risk of rear-end crashes in road traffic. One way to reduce instances of close following is through increased use of the Advanced Driver Assistance System (ADAS) Adaptive Cruise Control (ACC), which is designed to adjust vehicle speed to maintain a safe time headway. Since the activation of ACC is driver-initiated, there is a need to influence the propensity of drivers to use the function. This research aimed to explore whether in-vehicle nudging interventions could be effective for this purpose. A field trial was conducted to consecutively assess the effects of two nudges on drivers' utilization of ACC, compared to baseline usage. Exposing the participants (n = 49) to the first ambient design nudge resulted in a 46% increase in ACC usage on average. Following the introduction of the second nudge (a competitive leaderboard nudge), the average increase among participants (n = 48) during the complete treatment period reached 61%. The changes in ACC utilization varied between individual drivers, highlighting the need to monitor behavioral outcomes of nudges and adapt them when needed. In conclusion, this research shows that utilizing in-vehicle nudging is a promising approach to increase the use of vehicle functions contributing to improved traffic safety. [ABSTRACT FROM AUTHOR]

In this paper, the experimental results on microphone virtualization in realistic automotive scenarios are presented. A Temporal Convolutional Network (TCN) was designed in order to estimate the acoustic signal at the driver's ear positions based on the knowledge of monitoring microphone signals at different positions—a technique known as virtual microphone. An experimental setup was implemented on a popular B-segment car to acquire the acoustic field within the cabin while running on smooth asphalt at variable speeds. In order to test the potentiality of the TCN, microphone signals were recorded in two different scenarios, either with or without the front passenger. Our experimental results show that, when training is performed in both scenarios, the adopted TCN is able to robustly adapt to different conditions and guarantee a good average performance. Furthermore, an investigation on the parameters of the Neural Network (NN) that guarantee the sufficient accuracy of the estimation of the virtual microphone signals while maintaining a low computational complexity is presented. [ABSTRACT FROM AUTHOR]

The widespread use of advanced driver assistance systems in modern vehicles, together with their integration with the Internet and other road nodes, has made vehicle more vulnerable to cyber-attacks. To address these risks, the automotive industry is increasingly focusing on the development of security solutions: formal methods and software verification techniques, which have been successfully applied to a number of safety-critical systems, could be a promising approach in the automotive area. In this work, we concentrate on in-vehicle communications, provided by many Electronic Control Units (ECUs) that work together thanks to serial protocols such as Controller Area Network (CAN). However, increasing connectivity exposes the internal network to a variety of cyber-risks. Our aim is to formally verify the AUTOSAR-based Basic Software module called CINNAMON, designed to ensure confidentiality, integrity, and authentication at the same time for traffic exchanged over CAN protocol. More precisely, it adds confidentiality guarantees to the Secure Onboard Communication (SecOC) module. We formally analyze CINNAMON with the verification tool Tamarin. Our analysis shows that CINNAMON could be an effective security solution, as it can ensure the desired properties, in particular, confidentiality in a send-receive scenario between two ECUs. Finally, we describe a potential application scenario. [ABSTRACT FROM AUTHOR]

The rapid development of active safety systems in the automotive industry and research in autonomous driving requires reliable, high-precision sensors that provide rich information about the surrounding environment and the behaviour of other road users. In practice, there is always some non-zero mounting misalignment, i.e., angular inaccuracy in a sensor's mounting on a vehicle. It is essential to accurately estimate and compensate for this misalignment further programmatically (in software). In the case of radars, imprecise mounting may result in incorrect/inaccurate target information, problems with the tracking algorithm, or a decrease in the power reflected from the target. Sensor misalignment should be mitigated in two ways: through the correction of an inaccurate alignment angle via the estimated value of the misalignment angle or alerting other components of the system of potential sensor degradation if the misalignment is beyond the operational range. This work analyses misalignment's influences on radar sensors and other system components. In the mathematically proven example of a vertically misaligned radar, pedestrian detectability dropped to one-third of the maximum range. In addition, mathematically derived heading estimation errors demonstrate the impact on data association in data fusion. The simulation results presented show that the angle of misalignment exponentially increases the risk of false track splitting. Additionally, the paper presents a comprehensive review of radar alignment techniques, mostly found in the patent literature, and implements a baseline algorithm, along with suggested key performance indicators (KPIs) to facilitate comparisons for other researchers. [ABSTRACT FROM AUTHOR]

This article conducts a 20-year review of studies investigating the utilization of artificial intelligence in HVAC (Heating, Ventilation, and Air Conditioning) system control, with a particular focus on automotive HVAC systems, renowned for their unique challenges due to the highly dynamic nature of automotive cabins. Artificial neural networks (ANNs) provide adaptive modeling and control capabilities to enhance energy efficiency, thermal comfort, and air quality in HVAC systems. Nevertheless, ANNs exhibit limitations, primarily their performance being constrained by training data, impeding adaptability to unforeseen scenarios. To overcome these constraints, researchers have explored hybrid approaches that integrate ANNs with PID (Proportional, Integral, and Derivative) or PI (Proportional and Integral) algorithms, resulting in systems referred to as Neuro-PID or Neuro-PI. These systems can effectively handle unanticipated situations by absorbing errors introduced by ANNs. Nonetheless, the applicability of Neuro-PID varies depending on the specific system. It becomes infeasible in scenarios with substantial delays, prompting the choice of the appropriate approach based on the controllability factor, a ratio between system delay and time constant. A controllability factor less than 2 indicates the suitability of employing ANNs for PID controller tuning. Furthermore, the intricate interaction between temperature and humidity in HVAC control systems diminishes the effectiveness of traditional PID. To proficiently regulate both variables, a viable alternative is the amalgamation of ANNs with specific control logic or the deployment of distinct ANN algorithms for varying scenarios. The heightened variability and unpredictability within automotive HVAC systems, driven by continuous vehicle movement and door/window operations, present additional challenges. In such contexts, reinforcement learning techniques have found application, allowing systems to dynamically adapt to unforeseen events. In summary, the choice of HVAC control approach hinges on the primary goal—thermal comfort or specific variable control—as well as the system's noise level, controllability factor, and system nature. While ANNs hold significant potential, combining them with other techniques, such as PID, reinforcement learning, or specific control logic, may prove essential in addressing the intricacies of HVAC systems [ABSTRACT FROM AUTHOR]

Choosing the right metal AM equipment and material is a highly intricate process that forms a crucial part of every manufacturing company's strategic plan. This study undertakes a comprehensive comparison of the performance and material properties of three Metal Additive Manufacturing (AM) technologies: Powder Bed Fusion (PBF), Metal Filament Deposition Modeling (MFDM), and Bound Metal Deposition (BMD). An automotive nozzle was selected and manufactured using all three technologies and three metallic materials to understand their respective advantages and disadvantages. The samples were then subjected to a series of tests and evaluations, including dimensional accuracy, mechanical properties, microstructure, defects, manufacturability, and cost efficiency. The nozzle combinations were PBF in aluminum, MFDM in stainless steel, and BMD in hard tool steel. The results underscore significant differences in functionality, material characteristics, product quality, lead time, and cost efficiency, all of which are crucial factors in making equipment investment decisions. The conclusions drawn in this paper aim to assist automotive industry equipment experts in making informed decisions about the technology and materials to use for parts with characteristics like these. Future studies will delve into other technologies, automotive components, and materials to further enhance our understanding of the application of metal AM in manufacturing. [ABSTRACT FROM AUTHOR]

Waste management has become a major concern due to the extensive use of commodity polymers. Nowadays, one of the most widely used commodity polymers is nonwoven PP. The extensive utilization of polypropylene produces a large amount of waste, making their upcycling and recycling the biggest challenge. This research aims to develop an economical nanocomposite by upcycling nonwoven waste for utilization in the automotive and electronic sectors. A two-step melt blending technique was used to prepare polypropylene waste/silica nanocomposites. The nanocomposites formed were characterized by their morphological, mechanical, thermal, rheological, chemical, and electrical properties. From the results, it was concluded that the optimum mechanical, thermal, and chemical resistance properties were achieved for PP-01 formulation showing a 9.95% increase in heat deflection temperature, a 27.57% decrease in the rate of burning, a 5.4% increase in shore D hardness, 26.09% increase in flexural strength, 11.6% increase in melt flow index, and 66.25% increase in solvent resistance as compared to waste polypropylene. At the same time, the best electrical results were obtained at 0.5 wt.% with a 5.36% increase in the breakdown strength. The resistance value increases from 4.67 × 1012 Ω to 2 × 1013 Ω. The overall research shows that optimum mechanical and thermal properties were achieved at 1 wt.% so that the PP-01 formulation can be used effectively for automotive applications. In contrast, maximum electrical resistance was achieved at the PP-0.5 formulation so that this formulation could be effectively used for electrical insulation applications. [ABSTRACT FROM AUTHOR]

The rapid evolution of deep learning techniques, applied in the smart cities context, has revolutionized computer vision applications, with particular significance in the field of object detection. In this paper, we explore the application of deep learning for real-time car detection in visual data, such as images or video streams. The proposed deep learning model is trained on large-scale datasets containing diverse images of cars, encompassing various lighting conditions, weather patterns, and traffic scenarios. Moreover, once the presence of car(s) is detected, we consider finding the distance between the detected car(s) and the camera: in this way, it is possible to understand the distance to the vehicle to take a certain countermeasure (for example braking or slowing down). The experimental analysis, performed on a dataset composed of 25532 different images confirms the effectiveness of the proposed method for real-world car detection. Moreover, examples of the distance computed between the camera and the detected cars are shown to provide samples of the adoption of the proposed method in the real-world environment. [ABSTRACT FROM AUTHOR]

This study investigates active filling friction stir repair (AF-FSR) and passive filling friction stir repair (PF-FSR) for repairing AISI 304 stainless steel sheets, focusing on addressing the challenges posed by high melting point metals. The research involved repairing overlapping 2 mm thick sheets with pre-drilled holes of 2, 4, and 6 mm diameters, simulating broken components. Various process parameters, including rotational speed, dwell time, and the use of metal fillers, were tested to evaluate their impact on repair quality. The results demonstrated that PF-FSR provided superior mechanical strength to AF-FSR, particularly for larger pre-hole diameters. PF-FSR achieved higher shear tension strength due to better defect filling and reduced void formation, with shear tension strengths exceeding 25 kN for larger pre-holes and lower variability in strength measurements. AF-FSR was less effective for larger pre-holes, resulting in significant voids and reduced strength. Microstructural analysis revealed that PF-FSR facilitated more efficient material mixing and filling, minimizing unrepaired regions. However, excessive rotational speeds and dwell times in PF-FSR led to deformation and flash formation, highlighting the need for optimal parameter selection. Although further studies are needed, this study confirms the feasibility of FSR techniques for repairing small defects in AISI 304 steels, offering valuable insights for sustainable manufacturing practices in industries such as automotive and aerospace, where efficient and reliable repair methods are critical. [ABSTRACT FROM AUTHOR]

In an attempt to increase the biodiversity of natural fibers, new sources of natural fibers should be discovered. Long textile-grade date palm (DP) fibers were used in the manufacturing of 50:50 polypropylene nonwoven composite. DP fibers are considered newcomers to the natural fiber library. The main aim of this work was to benchmark different types of DP fiber composites in comparison to other commercial leaf fiber composites, namely, sisal, abaca, and banana, in addition to FlexForm automotive composites. The composites were mechanically and physically characterized in order to determine their properties. The results showed that the void content in DP composites was lower than that in sisal and abaca by 33% and 40% respectively. DP composites have tensile strength within the same range as sisal composites and higher than that of banana by nearly 33%. The modulus of elasticity and failure strain of DP composites were nearly 3 GPa and up to 3% respectively. The flexural strength of DP composites (35 MPa) was in the same range as that of sisal and abaca. The normalized impact energy of DP composites was higher than that of banana by 50%. The dynamic mechanical analysis of the six composites showed similar behavior with a glass transition temperature around 10 °C. Finally, the water absorption behavior of DP composites was better than the other composites (lower than sisal by 63%). The results showed that DP fibers are good candidates for applications in automotive interior composites, given their competitive performance and high potential availability. [ABSTRACT FROM AUTHOR]