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Abstract After a severe lane change, a wind gust, or another disturbance, the driver might be unable to recover the intended motion. Even though this fact is known by any driver, the scientific investigation and testing on this phenomenon is just at its very beginning, as a literature review, focusing on SAE Mobilus® database, reveals. We have used different mathematical models of car and driver for the basic description of car motion after a disturbance. Theoretical topics such as nonlinear dynamics, bifurcations, and global stability analysis had to be tackled. Since accurate mathematical models of drivers are still unavailable, a couple of driving simulators have been used to assess human driving action. Classic unstable motions such as Hopf bifurcations were found. Such bifurcations seem almost disregarded by automotive engineers, but they are very well-known by mathematicians. Other classic unstable motions that have been found are “unstable limit cycles.”

Abstract Noise, vibration, and harshness (NVH) is a key aspect in the vehicle development. Reducing noise and vibration to create a comfortable environment is one of the main objectives in vehicle design. In the literature, many theoretical and experimental methods have been presented for improving the NVH performances of vehicles. However, in the great majority of situations, physical prototypes are still required as NVH is highly dependent on subjective human perception and a pure computational approach often does not suffice. In this article, driving simulators are discussed as a tool to reduce the need of physical prototypes allowing a reduction in development time while providing a deep understanding of vehicle NVH characteristics. The present article provides a review of the current development of driving simulator focused on problems, challenges, and solutions for NVH applications.

Abstract Autonomous driving systems (ADS) have been widely tested in real-world environments with operators who must monitor and intervene due to remaining technical challenges. However, intervention methods that require operators to take over control of the vehicle involve many drawbacks related to human performance. ADS consist of recognition, decision, and control modules. The latter two phases are dependent on the recognition phase, which still struggles with tasks involving the prediction of human behavior, such as pedestrian risk prediction. As an alternative to full automation of the recognition task, cooperative recognition approaches utilize the human operator to assist the automated system in performing challenging recognition tasks, using a recognition assistance interface to realize human-machine cooperation.

Abstract Prediction of the surface finish of hardened bearing steels was estimated in machining with ceramic uncoated cutting tools under various process parameters using two statistical approaches. A second-order (quadratic) regression model (MQR, multiple quantile regression) for the surface finish was developed and then compared with the artificial neural network (ANN) method based on the coefficient determination (R 2), root mean square error (RMSE), and percentage error (PE). The experimental results exhibited that cutting speed was the dominant parameter, but feed rate and depth of cut were insignificant in terms of the Pareto chart and analysis of variance (ANOVA). The optimum surface finish in machining bearing steel was achieved at 100 m/min speed, 0.1 mm/revolution (rev) feed rate, and 0.6 mm depth of cut.

Abstract Connected and automated vehicles (CAVs) can bring safety, mobility, and energy benefits to transportation systems. Ideally, CAV applications would be fully evaluated and validated prior to real-world implementation. However, many technical challenges in both software and hardware hinder the process. To comprehensively evaluate all aspects of CAV applications, an integrated evaluation environment is needed with various simulation tools from different domains. In the current literature, there lacks a well-developed interface to enable multi-resolution simulation of vehicle, traffic, virtual environment, and hardware-in-the-loop (HIL) simulation. In this work, a modular and flexible interface is developed to enable multi-resolution vehicle and traffic co-simulation for CAV applications.

Abstract Every year the demands on the electric grid increase, but the ability to deliver power where needed remains problematic because of transmission and distribution losses, vulnerabilities to natural disasters, and struggles to meet peak load requirements in an increasing number of regions. To meet these increasing demands, especially with emerging electric vehicles, it becomes ever more important to develop integrated demand and response systems. One such promising technology is the use of a micro Combined Heat and Power (mCHP)-based distributed energy system that addresses both electricity and thermal demands (i.e., electricity, hot water, and space heating demands) by using a single unit. However, one major problem with this technology at the residential level is the optimal sizing and maximizing the operational time of mCHP systems in meeting electrical and thermal demands.

Abstract Modeling, prediction, and evaluation of personalized driving behaviors are crucial to emerging advanced driver-assistance systems (ADAS) that require a large amount of customized driving data. However, collecting such type of data from the real world could be very costly and sometimes unrealistic. To address this need, several high-definition game engine-based simulators have been developed. Furthermore, the computational load for cooperative automated driving systems (CADS) with a decent size may be much beyond the capability of a standalone (edge) computer. To address all these concerns, in this study we develop a co-simulation platform integrating Unity, Simulation of Urban MObility (SUMO), and Amazon Web Services (AWS), where Unity provides realistic driving experience and simulates on-board sensors; SUMO models realistic traffic dynamics; and AWS provides serverless cloud computing power and personalized data storage.

Abstract Autonomous vehicles (AVs) are being rapidly introduced into our lives. However, public misunderstanding and mistrust have become prominent issues hindering the acceptance of these driverless technologies. The primary objective of this study is to evaluate the effectiveness of a driving simulator to help the public gain an understanding of AVs and build trust in them. To achieve this aim, we built an integrated simulation platform, designed various driving scenarios, and recruited 28 participants for the experiment. The study results indicate that a driving simulator effectively decreases the participants’ perceived risk of AVs and increases perceived usefulness. The proposed methodologies and findings of this study can be further explored by auto manufacturers and policymakers to provide user-friendly AV design.

Abstract A reproducible analytical test method was developed to quantify the fouling resistance and cleanability of camera lens covers for autonomous vehicles (AVs). Reproducible fouling and cleaning cycles were achieved using a custom-built laboratory test stand. The impact of fouling/cleaning on image quality was quantified using digital image analysis. Three optically transparent, fluorine-containing functional coatings on lens covers were used to validate the test method. Accelerated weathering was employed to deliberately degrade the functional coatings. Coating degradation was characterized using water contact angle and X-ray photoelectron spectroscopy. The effect of coating degradation on cleaning performance was studied using this test method. This analysis method was able to characterize differences in coating performance and can be used as a tool to evaluate next-generation functional coatings for autonomous vehicles.

Abstract Formula Student Driverless (FSD) challenges engineering students to develop autonomous single-seater race cars in a quest to bring about more graduates who are well prepared to solve the real-world problems associated with autonomous driving. In this article, we present the software stack of KA-RaceIng’s entry to the 2019 competitions. We cover the essential modules of the system, including perception, localization, mapping, motion planning, and control. Furthermore, development methods are outlined, and an overview of the system architecture is given. We conclude by presenting selected runtime measurements, data logs, and competition results to provide an insight into the performance of the final prototype.

Abstract This article presents the Formula Student Objects in Context (FSOCO) dataset, a collaborative dataset for vision-based cone detection systems in Formula Student Driverless (FSD) competitions. It contains human-annotated ground truth labels for both bounding boxes and instance-wise segmentation masks. The data buy-in philosophy of FSOCO asks student teams to contribute to the database first before being granted access, ensuring continuous growth. By providing clear labeling guidelines and tools for a sophisticated raw image selection, new annotations are guaranteed to meet the desired quality. The effectiveness of the approach is shown by comparing the prediction results of a network trained on FSOCO and its unregulated predecessor. The FSOCO dataset can be found at fsoco-dataset.com.

Abstract Electrolytic hydrogen production equipment has numerous hydrogen pipelines and high-pressure hydrogen storage tanks which may leak hydrogen which can lead to explosions causing damage to the nearby personnel and equipment. The present work modeled hydrogen explosions in a skid-mounted electrolytic hydrogen production unit. The model was first used to predict the area affected by an explosion without protective walls. The effects of protective walls on the flame and overpressure were then studied by modeling explosions with various protective walls at various distances from the opening on the side of the unit. The results show that the protective walls effectively reduced the damage behind the wall. However, the reflected shock waves may cause secondary damage in front of the wall if the protective wall is too close to the opening. Moreover, the protective wall blocks the hydrogen diffusion which increases the flammable gas mass.

Erratum.

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.

Abstract In an automated machining process, monitoring the conditions of the tool is essential for deciding to replace or repair the tool without any manual intervention. Intelligent models built with sensor information and machine learning techniques are predicting the condition of the tool with good accuracy. In this study, statistical models are developed to identify the conditions of the abrasive grinding wheel using the Acoustic Emission (AE) signature acquired during the surface grinding operation. Abrasive grinding wheel conditions are identified using the abrasive wheel wear plot established by conducting experiments. The piezoelectric sensor is used to capture the AE from the grinding process, and statistical features of the abrasive wheel conditions are extracted in time and wavelet domains of the signature. Machine learning algorithms, namely, Classification and Regression Trees (CART) and Support Vector Classifiers (SVC), are used to build statistical models.

Abstract Today’s explosive condition of the market is compelling the manufacturing organizations to switch from traditional manufacturing (TM) to lean manufacturing (LM) to create a footprint in this competitive era. In this article, 16 critical success factors (CSFs) for LM implementation are identified through a vast literature review, the opinion of academicians and industry experts and interpretive structural modeling (ISM) is used to create interrelationships among the identified CSFs, and interpretive ranking process (IRP) rank these CSFs based on dominance with respect to performance dimensions. Leadership and management made the foundation of an ISM model while the training and people development have secured the first rank in the IRP model. Implementation of such ISM- and IRP-based models of CSF would give a clear understanding of these CSFs so that LM researchers, decision-makers, managers, and practitioners of LM will use their resources more efficiently.

Abstract Collision alert and avoidance systems (CAS) could help to minimize driver errors. They are instrumental as an advanced driver-assistance system (ADAS) when the vehicle is facing potential hazards. Developing effective ADAS/CAS, which provides alerts to the driver, requires a fundamental understanding of human sensory perception and response capabilities. This research explores the premise that external stimulation can effectively improve drivers’ reaction and response capabilities. Therefore this article proposes a light-emitting diode (LED)-based driver warning system to prevent potential collisions while evaluating novel signal processing algorithms to explore the correlation between driver brain signals and external visual stimulation. When the vehicle approaches emerging obstacles or potential hazards, an LED light box flashes to warn the driver through visual stimulation to avoid the collision through braking.

Abstract Effects of process parameters such as rotational speed, feed rate, and drill diameters on the drilling behavior of basalt-epoxy-based composites including 2.5 wt.% Al2O3 particles manufactured by mixing and compression method were investigated by Taguchi’s technique. The experimental results showed that the burr height (BH) increased considerably almost linearly with an increase in the drill diameter, while it remained stable with speed and decreased the feed rate slightly. There was an excellent correlation between the control factors and responses, BH of basalt fiber-reinforced plastics (BFRPs) through the Taguchi approach. The model had an adjusted R2 value of 96.3%. Generally, the inclusion of Al2O3 particles in BFRP increased its cutting force properties. Optimized drilling conditions for the input variables to produce the lowest response of the BH for composites were rotational speed of 560 rpm and feed rate of 0.28 mm/rev and a drill diameter of 4.5 mm.

Abstract To solve the problem that it is difficult for drivers to control the vehicle at low speed, a new setting scheme of pedal map is proposed to ensure that the vehicle has the speed controllability in the full speed range. In this scheme, based on obtaining the maximum and minimum driving characteristics of the vehicle and the driving resistance characteristics of the vehicle, the pedal map is divided into a sensitive area and insensitive area. In the insensitive area, acceleration hysteresis is formed, which ensures that the throttle is slightly fluctuated and has good speed stability. At the same time, the sensitive area of the accelerator pedal is formed far away from the driving resistance curve to ensure that the vehicle has a great acceleration ability. To verify the effectiveness of the proposed scheme, the data of a commercial vehicle is selected for the design of the pedal map, and the driver-vehicle closed-loop test based on the driving simulator is conducted.

Abstract Aircraft lift bag is the equipment used for the recovery of an aircraft and is considered as a lifting equipment. Boeing 737 is a domestic aircraft considered for designing this bag. The aircraft lift bag is made of composite material, and the most widely used materials are nylon and neoprene. A composite material is used to make the bag lightweight and easy to handle. For calculation of properties and the engineering constant of the respective composite materials, micromechanics approach is used, in which the method of Representative Volume Element (RVE) is taken into consideration. The loading and boundary conditions are the exact replica of the working conditions. The operation of this bag is completely pneumatic. The stresses induced in the bag are analyzed in finite element software and are compared with the calculated theoretical values. CATIA is used to model the bag, and ABAQUS is used for the finite element calculations.