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Abstract Additive manufacturing (AM) technologies can produce lighter parts; reduce manual assembly processes; reduce the number of production steps; shorten the production cycle; significantly reduce material consumption; enable the production of prostheses, implants, and artificial organs; and produce end-user products since it is used in many sectors for many reasons; it has also started to be used widely, especially in the field of aerospace. In this study, polylactic acid (PLA) was preferred for the antenna substrate because it is environmentally friendly, easy to recycle, provides convenience in production design with a three-dimensional (3D) printer, and is less expensive compared to other available materials. Copper (Cu) tape and graphene filament were employed for the antenna patch component due to their benefits.

Abstract In the automotive domain, the overall complexity of technical components has increased enormously. Formerly isolated, purely mechanical cars are now a multitude of cyber-physical systems that are continuously interacting with other IT systems, for example, with the smartphone of their driver or the backend servers of the car manufacturer. This has huge security implications as demonstrated by several recent research papers that document attacks endangering the safety of the car. However, there is, to the best of our knowledge, no holistic overview or structured description of the complex automotive domain. Without such a big picture, distinct security research remains isolated and is lacking interconnections between the different subsystems. Hence, it is difficult to draw conclusions about the overall security of a car or to identify aspects that have not been sufficiently covered by security analyses.

Abstract The United Nation Economic Commission for Europe (UNECE) Regulation 155—Cybersecurity and Cybersecurity Management System (UN R155) mandates the development of cybersecurity management systems (CSMS) as part of a vehicle’s lifecycle. An inherent component of the CSMS is cybersecurity risk management and assessment. Validation and verification testing is a key activity for measuring the effectiveness of risk management, and it is mandated by UN R155 for type approval. Due to the focus of R155 and its suggested implementation guideline, ISO/SAE 21434:2021—Road Vehicle Cybersecurity Engineering, mainly centering on the alignment of cybersecurity risk management to the vehicle development lifecycle, there is a gap in knowledge of proscribed activities for validation and verification testing.

Abstract Performing an uncertainty analysis for complex measurement tasks, such as those found in engine research, presents unique challenges. Also, because of the excessive computational costs, modeling-based approaches, such as a Monte Carlo approach, may not be practical. This work provides a traditional statistical approach to uncertainty analysis that incorporates the uncertainty tree, which is a graphical tool for complex uncertainty analysis. Approaches to calculate the required sensitivities are discussed, including issues associated with numerical differentiation, numerical integration, and post-processing. Trimming of the uncertainty tree to remove insignificant contributions is discussed. The article concludes with a best practices guide in the Appendix to uncertainty propagation in experimental engine combustion post-processing, which includes suggested post-processing techniques and down-selected functional relationships for uncertainty propagation.

Abstract This article explores the value of simulation for autonomous-vehicle research and development. There is ample research that details the effectiveness of simulation for training humans to fly and drive. Unfortunately, the same is not true for simulations used to train and test artificial intelligence (AI) that enables autonomous vehicles to fly and drive without humans. Research has shown that simulation “fidelity” is the most influential factor affecting training yield, but psychological fidelity is a widely accepted definition that does not apply to AI because it describes how well simulations engage various cognitive functions of human operators. Therefore, this investigation reviewed the literature that was published between January 2010 and May 2022 on the topic of simulation fidelity to understand how researchers are defining and measuring simulation fidelity as applied to training AI.

Abstract In recent years, countries worldwide have framed policies for faster adoption of electric vehicles. To meet the requirements of electric vehicles, research activities in academia as well as in industry have intensified. One of the significant areas of research is low-cost and high-efficiency electric drive for these vehicles, and their control over a wide range of operations. In this article, an electric vehicle drive with direct torque control of induction motor is presented. This article addresses the impact of reference flux linkage on the operation of induction motor for direct torque control over a wide speed range. A nonlinear equivalent circuit model of an induction motor is considered to obtain values of reference flux linkage. The method uses the nonlinear equivalent circuit parameters to do the offline calculation to determine the reference flux linkage, and a lookup table is generated.

Abstract With the introduction of advanced lightweight materials with complex microstructures and behaviors, more focus is put on the accurate determination of their forming limits, and that can only be possible through experiments as the conventional theoretical models for the forming limit curve (FLC) prediction fail to perform. Despite that, CAE engineers, designers, and toolmakers still rely heavily on theoretical models due to the steep costs associated with formability testing, including mechanical setup, a large number of tests, and the cost of a stereo digital image correlation (DIC) system. The international standard ISO 12004-2:2021 recommends using a stereo DIC system for formability testing since two-dimensional (2D) DIC systems are considered incapable of producing reliable strains due to errors associated with out-of-plane motion and deformation.

Abstract Artificial neural networks (ANNs) have found increasing usage in regression problems because of their ability to map complex nonlinear relationships. In recent years, ANN regression model applications have rapidly increased in the engine calibration and controls area. The data used to build ANN models in engine calibration and controls area generally consists of noise due to instrument error, sensor precision, human error, stochastic process, etc. Filtering the data helps in reducing noise due to instrument error, but noise due to other sources still exist in data. Furthermore, many researchers have found that ANNs are susceptible to learning from noise. Also ANNs cannot quantify the uncertainty of their output in critical applications. Hence, a methodology is developed in the present manuscript which computes the noise-based confidence interval using engine test data. Moreover, a method to assess the risk of ANN learning from noise is also developed.

Abstract Nowadays, the need for a more sustainable mobility is fostering powertrain electrification as a way of reducing the carbon footprint of conventional vehicles. On the other side, the presence of multiple energy sources significantly increases the powertrain complexity and requires the development of a suitable Energy Management System (EMS) whose performance can strongly affect the fuel economy potential of the vehicle. In such a framework, this article proposes a novel methodology to reverse engineer the control strategy of a test case P2 Plug-in Hybrid Electric Vehicle (PHEV) through the analysis of experimental data acquired in a wide range of driving conditions. In particular, a combination of data obtained from On-Board Diagnostic system (OBD), Controller Area Network (CAN)-bus protocol, and additional sensors installed on the High Voltage (HV) electric net of the vehicle is used to point out any dependency of the EMS decisions on the powertrain main operating variables.

Abstract Automotive interior coatings for flexible and rigid substrates represent an important segment within automotive coating space. These coatings are used to protect plastic substrates from mechanical and chemical damage, in addition to providing colour and design aesthetics. These coatings are expected to resist aggressive chemicals, fluids, and stains while maintaining their long-term physical appearance and mechanical integrity. Designing such coatings, therefore, poses significant challenges to the formulators in effectively balancing these properties. Among many factors affecting coating properties, the cross-link density (XLD) and solubility parameter (δ) of coatings are the most predominant factors.

Abstract This article presents a novel approach to optimize the placement of light detection and ranging (LiDAR) sensors in autonomous driving vehicles using machine learning. As autonomous driving technology advances, LiDAR sensors play a crucial role in providing accurate collision data for environmental perception. The proposed method employs the deep deterministic policy gradient (DDPG) algorithm, which takes the vehicle’s surface geometry as input and generates optimized 3D sensor positions with predicted high visibility. Through extensive experiments on various vehicle shapes and a rectangular cuboid, the effectiveness and adaptability of the proposed method are demonstrated. Importantly, the trained network can efficiently evaluate new vehicle shapes without the need for re-optimization, representing a significant improvement over classical methods such as genetic algorithms.

Abstract Many consider hydrogen to be the automobile fuel of the future. Indeed, it has numerous characteristics that makes it very attractive. Hydrogen has a much higher energy density than gasoline, can be produced from water, and its only emission is water. However, there are numerous challenges associated with hydrogen. In particular, the production of hydrogen is a key issue. Currently, most hydrogen is developed from methane, resulting in hydrogen having a carbon footprint. New investments into electrolysis from renewable energy sources is showing promise as an alternative for generating hydrogen. Further, the distribution of hydrogen poses many problems, requiring substantial infrastructure to support a hydrogen economy. Additionally, hydrogen storage is a key issue since most conventional storage mechanisms are overly bulky. If these three issues can be addressed, hydrogen is posed for being a key fuel as the world tries to move away from fossil fuels.

Abstract Connected autonomous vehicles that employ internet connectivity are technologically complex, which makes them vulnerable to cyberattacks. Many cybersecurity researchers, white hat hackers, and black hat hackers have discovered numerous exploitable vulnerabilities in connected vehicles. Several studies indicate consumers do not fully trust automated driving systems. This study expanded the technology acceptance model (TAM) to include cybersecurity and level of trust as determinants of technology acceptance. This study surveyed a diverse sample of 209 licensed US drivers over 18 years old. Results indicated that perceived ease of use positively influences perceived usefulness, perceived ease of usefulness negatively influences perceived cyber threats, and perceived cyber threats negatively influence the level of trust.

Abstract The Noise, Vibration, and Harshness (NVH) performance of automotive powertrain (PT) mounts involves the PT source vibration, PT mount stiffness, road input, and overall transfer path design. Like safety, performance, and durability driving dynamics, vehicle-level NVH also plays a major contributing factor for electric vehicle (EV) refinement. This article highlights the recent research on PT mounting-related NVH controls on electric cars. This work’s main contribution lies in the comparative study of the internal combustion engine (ICE)-based PT mounting and EV-based PT mounting system (PMS) with specific EV challenges. Various literature on PT mounts from the passive, semi-active, and active mounting systems are studied. The parameter optimization technique for mount stiffness and location by various research papers is summarized to understand the existing methodologies and research gap in EV application.

Abstract Precise vehicle state and the surrounding traffic information are essential for decision-making and dynamic control of intelligent connected vehicles. Tremendous research efforts have been devoted to developing state estimation techniques. This work investigates the research progress in this field over recent years. To be able to describe the state of multiple traffic elements uniformly, the concept of a vehicle neighborhood system is proposed to describe the system composed of vehicles and their surrounding traffic elements and to distinguish it from the traditional macroscopic traffic research field. In this work, the vehicle neighborhood system consists of three main traffic elements: the host vehicle, the preceding vehicle, and the road. Therefore, a review of state estimation methods for the vehicle neighborhood system is presented around the three traffic objects mentioned earlier.

Abstract While machine-learning-based methods suffer from a lack of transparency, rule-based (RB) methods dominate safety-critical systems. Yet the RB approaches cannot compete with the first ones in robustness to multiple system requirements, for instance, simultaneously addressing safety, comfort, and efficiency. Hence, this article proposes a decision-making and control framework which profits from the advantages of both the RB and machine-learning-based techniques while compensating for their disadvantages. The proposed method embodies two controllers operating in parallel, called Safety and Learned. An RB switching logic selects one of the actions transmitted from both controllers. The Safety controller is prioritized whenever the Learned one does not meet the safety constraint, and also directly participates in the Learned controller training.

Abstract Trajectory tracking control, as one of the core technologies of intelligent driving vehicles, determines the driving performance and safety of intelligent driving vehicles and has received extensive attention and research. In recent years, most of the research results of trajectory tracking control are only applicable to conventional working conditions; however, the actual operating conditions of intelligent driving vehicles are complex and variable, so the research of trajectory tracking control algorithm should be extended to the high-speed low-adhesion coefficient, large curvature, variable curvature, and other compound limit working conditions. This requires more consideration of the vehicle dynamics in the controller design.

Abstract Autonomous vehicle technology has become an unprecedented trend in the development of the automobile industry, which can ensure highly efficient use of resources, effectively improve the driving experience, and greatly reduces the driver’s burden. As one of the key technologies of autonomous vehicles, path planning has an important impact on the practical applications of autonomous vehicles. Planning a proper and efficient path is a prerequisite, which can improve the driving experience of autonomous vehicles. Therefore, in-depth research and development on applications of AI technology in path planning definitely have significant value in academic research. In this article, we will introduce a variety of path planning approaches for autonomous vehicles. We summarize the attributes of these path planning algorithms; simultaneously, we analyze the improvements to these algorithms. Then, we have a preliminary discussion on the applications in vehicle positioning and navigation.

Abstract The innovations of vehicle connectivity have been increasing dramatically to enhance the safety and user experience of driving, while the rising numbers of interfaces to the external world also bring security threats to vehicles. Many security countermeasures have been proposed and discussed to protect the systems and services against attacks. To provide an overview of the current states in this research field, we conducted a systematic mapping study (SMS) on the topic area “security countermeasures of in-vehicle communication systems.” A total of 279 papers are identified based on the defined study identification strategy and criteria. We discussed four research questions (RQs) related to the security countermeasures, validation methods, publication patterns, and research trends and gaps based on the extracted and classified data. Finally, we evaluated the validity threats and the whole mapping process.

Abstract The ability of senior citizens as well as other members of the general population to engage in an effective manner with technology is of increasing importance as new and innovative technologies become available. While recognizing the challenges that technologies can have on different populations, the ability to interact successfully with new technologies will, for seniors, have important consequences that can affect their quality of life and those of their families in numerous and important ways. This study, building upon previous research, examines the major dimensions of decision-making regarding attitudes toward autonomous vehicle technologies (ATVs) and their use. The study utilized data from a study of senior citizens in the Dallas-Fort Worth (DFW) area and compared the results with a sample of graduate students from a local university.