Search Results

Abstract A number of studies have shown that driving an unfamiliar vehicle has the potential to introduce additional risk, especially for novice drivers. However, such studies have generally used statistical methods based on analyzing crash and near-crash data from a range of driver groups, and therefore the evaluation has the potential to be subjective and limited. For a more objective perspective, this study suggests that it would be worthwhile to consider vehicle dynamic signals obtained from the Controller Area Network (CAN-Bus) and smartphones. This study, therefore, is focused on the effect of driver experience and vehicle familiarity for issues in driver modeling and distraction. Here, a group of 20 drivers participated in our experiment, with 13 of them having participated again after a one-year time lapse in order for analysis of their change in driving performance.

Abstract Introduction: This study evaluates driving ability in those with Charcot Marie Tooth Disease Type 1A, a hereditary peripheral neuropathy. Methods: Individuals with Charcot Marie Tooth Disease Type 1A (n = 18, age = 42 ± 7) and controls (n = 19; age = 35 ± 10) were evaluated in a driving simulator. The Charcot Marie Tooth Neuropathy Score version 2 was obtained for individuals. Rank Sum test and Spearman rank correlations were used for statistical analysis. Results: A 74% higher rate of lane departures and an 89% higher rate of lane deviations were seen in those with Charcot Marie Tooth Disease Type 1A than for controls (p = 0.005 and p < 0.001, respectively). Lane control variability was 10% higher for the individual group and correlated with the neuropathy score (rS = 0.518, p = 0.040), specifically sensory loss (rS = 0.710, p = 0.002) and pinprick sensation loss in the leg (rS = 0.490, p = 0.054).

Abstract The use of simulation is a long-standing industry standard at every level of flight training. Historically, given the acquisition and maintenance costs associated with such equipment, full-motion devices have been reserved for advanced corporate and airline training programs. The Motion Cueing Seat (MCS) is a relatively inexpensive alternative to full-motion flight simulators and has the potential to enhance the fixed-base flight simulation in primary flight training. In this article, we discuss the results of an evaluation of the effect of motion cueing on pilot workload and performance during primary instrument training. Twenty flight students and instructors from a collegiate flight training program participated in the study. Each participant performed three runs of a basic circuit using a fixed-base Advanced Aviation Training Device (AATD) and an MCS.

Abstract Raceway Brinell damage is one major cause of wheel bearing (hub unit) noise during driving. Original Equipment Manufacturer (OEM) customers have asked continuously for its improvement to the wheel bearing supply base. Generally, raceway Brinelling in a wheel hub unit is a consequence of metallic yielding from high external loading in a severe environment usually involving a side impact to the wheel and tire. Thus, increasing the yielding strength of steel can lead to higher resistance to Brinell damage. Both the outer ring and hub based on Generation 3 (Gen. 3) wheel unit are typically manufactured using by AISI 1055 bearing quality steel (BQS); these components undergo controlled cooling to establish the core properties then case hardening via induction hardening (IH). This paper presents a modified grade of steel and its IH design that targets longer life and improves Brinell resistance developed by ILJIN AMRC (Advanced Materials Research Center).

Abstract Surface roughness is one of the important aspects in producing quality die. Wire Electrical Discharge Machine (WEDM) is commonly used in tool and die fabrication, since the die material is usually difficult to cut using traditional metal removal processes. Selection of optimal WEDM cutting parameters is crucial to obtain quality die finish. In this study, 2379 steel which equivalent to SKD 11 is selected as the die material. Four main WEDM cutting parameters, namely, pulse duration (A), pulse interval (B), servo voltage (C), ignition pulse current (D), were experimentally evaluated for both main cut and multiple trim cuts using Taguchi Method. Taguchi’s L9 orthogonal array is employed for experimental design and analysis of variance (ANOVA) was used in recognizing levels of significance of WEDM cutting parameters.

Abstract Sound ductile iron (DI) welded joints were performed using developed coated electrode and optimized welding parameters including post weld heat treatment (PWHT).Weldments consisting of weld metal, partially melted zone (PMZ), heat affected zone (HAZ) and base metal were austenitized at 900 °C for 2 hour and austempered at 300 °C and 350 °C for three different holding time (1.5 hour, 2 hour and 2.5 hour). In as-weld condition, microstructures of weld metal and PMZ show ledeburitic carbide and alloyed pearlite, but differ with their amount. Whereas microstructure of HAZ shows pearlite with some ledeburitic carbide and base metal shows only ferrite.

Abstract In this work, optimal modeling parameters for self-pierce riveting (SPR) were determined using a factorial design of experiments (DOE). In particular, we show statistically how each of the calibrating parameters used in modeling the SPR process through nonlinear finite element modeling can drastically change the geometry of the joint. The results of this study indicate that the degree of interlock, which is a key feature of a sound joint, is largely influenced by the friction between the die and bottom sheet as well as the friction between the rivet and top sheet. Furthermore, this numerical study also helped elucidate the role of friction in SPR and sheds light on how coatings with diverse friction coefficients can affect material deformation and ultimately structural integrity of the joint.

Abstract Management of expressway networks has been mainly focused on defect management without looking at the correlations with accidental risks. This causes unsustainability in expressway infrastructure maintenance since such defects may not be a contributing factor toward public safety. Thus it is necessary to incorporate accidental events for decision-making in infrastructure management. This study has developed a novel approach to machine learning (ML) that incorporates actual primary data from the last 10 years of single-vehicle accidents (SVA) by collisions with motorway facilities, or so-called single-vehicle collisions with fixed objects. The ML is firstly aimed at identifying the influential factors of SVA in relation to finding effective countermeasures for accidents by integrating the correlation analysis, multiple regression analysis, and ML techniques. The study reveals that wet pavement conditions have a significant effect on SVA.

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 Mechanical friction simulations offer a valuable tool in the development of internal combustion engines for the evaluation of optimization studies in terms of time efficiency. However, system modeling and evaluation of model performance may be highly complex. A high number of interacting submodels and parameters as well as a limited model transparency contribute to uncertainties in the modeling process. In particular, model calibration and validation are complicated by the unknown effect of parameters on the model output. This article presents an advanced and model-independent methodology for identifying sensitive parameters of engine friction. This allows the user to investigate an unlimited number of parameters of a model whose structure and properties are prior unknown.

Erratum

Abstract The development of automated driving systems (ADS) necessitates procedures to validate system safety. The reliability of an ADS’s environment perception provided by lidar, radar, and camera sensors is of special interest in this context, because perception errors can be safety-critical. In this article, we formalize the reliability-based validation of environment perception for safe automated driving and discuss associated challenges. We describe a potential solution to a perception reliability validation by deriving performance requirements at the sensor level. We then summarize statistical methods to learn sensor perception reliabilities in field tests, on proving grounds, and through virtual simulations. With the developed safety validation framework, we show that, potentially, one can validate the safety of an ADS with feasible test effort.

Abstract With the growing number of cross-sea flights, the occurrence of maritime-related accidents, which have a high fatality rate, has become increasingly critical. This study is aimed at highlighting the causes of maritime-related accidents and identifying the risk factors that led to fatal crashes in the period 2009-2019. A total of 207 maritime-related accidents, the final reports of which are available in the online database of the National Transportation Safety Board, were considered. The accident cause distribution was obtained from the final reports. A two-step approach, involving uni-variable and multi-variable analysis logistic regression, was implemented to select the significant risk factors from 27 parameters. Results showed that the four main causes of maritime-related accidents were personnel issues (69.6%), aircraft-related aspects (60.4%), environmental issues (36.7%), and organizational issues (3.9%).

Abstract Letter from the Guest Editors

Abstract Selective inhibition sintering (SIS) results in easy, flexible, fast, and cost-efficient fabrication of functional parts by using powder material for various applications. The functional part is important for operational examination by fabricating the part unswervingly from computer-aided design (CAD) data. However, poor surface quality is the major disadvantage in the SIS procedure. The selection procedure of optimal operating parameters plays a major role in the fabrication of end products. The present study discusses the effect of key contributing operating parameters on the surface quality of the polyamide parts fabricated by the SIS process. Parameters like heater power (HP), layer thickness (LT), heater feed rate (HFR), machine feed rate (MFR), and bed temperature (BT) were considered in this study.

Abstract The ongoing electrification and data-intelligence trends in logistics industries enable efficient powertrain design and operation. In this work, the commercial package delivery vehicle powertrain design space is revisited with a specific combination of optimization and control techniques that promise accurate results with relatively fast computational time. The specific application that is explored here is a Class 6 pickup and delivery truck. A statistical learning approach is used to refine the search for the most optimal designs. Five hybrid powertrain architectures, namely, two-speed e-axle, three-speed and four-speed automated manual transmission (AMT) with electric motor (EM), direct-drive, and dual-motor options are explored, and a set of Pareto-optimal designs are found for a specific driving mission that represents the variations in a hypothetical operational scenario. The modeling and optimization processes are performed on the MATLAB™-Simulink platform.

Abstract Objectified analysis and evaluation tools offer cost- as well as time-saving potentials regarding the calibration process of vehicle control units. To reduce the time required for the calibration effort, standardized processes including the frontloading of development tasks enable swift calibration procedures and can be used to develop a basis for the comparison of different vehicles and also the calibration quality. In this environment, objectified evaluation methods are also being developed for the investigation of the drivability of electric vehicles. This article presents a methodology for assessing the longitudinal drive behavior of battery electric vehicles during deceleration maneuvers. The aim is to objectively evaluate the vehicle deceleration by means of reproducible driving maneuvers. In addition to further measurement signals, the longitudinal acceleration signal serves as the main evaluation basis.

Abstract Welding is a dominant joining process employed in fabrication industries, especially in critical areas such as boiler, pressure vessels, and marine structure manufacturing. Online monitoring of welding processes using sensors and intelligent models is increasingly used in industries for predicting weld conditions. Studies are conducted in a Shielded Metal Arc Welding (SMAW) process using sound, current, and voltage sensors to predict the weld conditions. Sensor signatures are acquired from the good weld and defective weld conditions established in this study. Signal processing is carried out, and time-domain statistical features are extracted. Statistical features are also extracted from the power waveform derived from the current and voltage data for all the weld conditions. Classification And Regression Tree (CART) and Support Vector Machine (SVM) algorithms are used to build the statistical models to predict the weld conditions.

Abstract In this article, we present a rigorous modular statistical approach for arguing the safety or its insufficiency of an autonomous vehicle through a concrete illustrative example. The methodology relies on making appropriate quantitative studies of the performance of constituent components. We explain the importance of sufficient and necessary conditions at the component level for the overall safety of the vehicle, as well as the cost-saving benefits of the approach. A simple concrete automated braking example studied illustrates how the separate perception system and Operational Design Domain (ODD) statistical analyses can be used to prove or disprove safety at the vehicle level.

Abstract The millimeter-wave radar has good weather robustness, but currently lacks performance in object classification. With the advent of imaging radar, three-dimensional (3D) point clouds of objects can be obtained. Based on 3D radar point clouds, an support vector machine (SVM algorithm using 3D features is proposed to solve poor radar classification performance. First, a new 29-feature vector is proposed from many perspectives, such as shape features, statistical features, and other features. Then the SVM classifier with four different kernel functions and other machine learning methods are used to achieve multi-objective classification. Finally, experiments are carried out on three types of datasets collected by ourselves, and the results show that the algorithm achieves a 95.1% classification accuracy, which is 15.7% higher than the traditional 2D radar point cloud.