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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 It is reasonable to use a two-phase heat transfer loop (TPL) in a thermal control system (TCS) of spacecraft with large heat dissipation. One of the key elements of TPL is a heat-controlled accumulator (HCA). The HCA represents a volume which is filled with vapor and liquid of a single working fluid without bellows. The pressure in a HCA is controlled by the heater. The heat and mass transfer processes in the HCA can proceed with a significant nonequilibrium. This has implications on the regulation of TPL. This article presents a mathematical model of nonequilibrium heat and mass transfer processes in an HCA for microgravity conditions. The model uses the equations of mass and energy conservation separately for the vapor and liquid phases. Interfacial heat and mass transfer is also taken into account. It proposes to use the convective component k for the level of nonequilibrium evaluation.

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 A two-dimensional model of three elements, high-lift airfoil, was designed at a Reynolds number of ?????? using computational fluid dynamics (CFD) to generate downforce with good lift-to-drag efficiency for a formula student open-wheel race car basing on the nominal track speeds. The numerical solver uses the Reynolds-averaged Navier-Stokes (RANS) equation model coupled with the Langtry-Menter four-equation transition shear stress transport (SST) turbulence model. Such model adds two further equations to the ?? − ?? SST model resulting in an accurate prediction for the amount of flow separation due to adverse pressure gradient in low Reynolds number flow. The ?? − ?? SST model includes the transport effects into the eddy-viscosity formulation, whereas the two equations of transition momentum thickness Reynolds number and intermittency should further consider transition effects at low Reynolds number.

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 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 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 A new method that allows data-enabled (empirical) models, commonly used for automotive engine calibration, to extrapolate beyond the range of training data has been developed. This method used a physics-based system-level one-dimensional model to improve interpolation and allow extrapolation for three data-based algorithms, by modifying the model input (feature) space. Neural network, regression, and k-nearest neighbor predictions of engine emissions and volumetric efficiency were greatly improved by generating 736,281 artificial feature spaces and then performing feature selection to choose feature spaces (feature selection) so that extrapolations in the original feature space were interpolations in the new feature space. A novel feature selection method was developed that used a two-stage search process to uniquely select the best feature spaces for every prediction.

Abstract Identifying and eliminating failure scenarios is critical in the development of autonomous vehicle (AV) systems. However, finding such failures through real-world vehicle-level testing is a difficult task as system disengagements and accidents are rare occurrences. Simulation approaches have been proposed to supplement vehicle-level testing and reduce the costs associated with operating large fleets of autonomous test vehicles. While one can run more vehicles in simulation than in the real world, applying traditional Monte Carlo sampling techniques to find failures still yields an unguided search and a large waste of computing resources. A more directed method than random sampling is needed to identify failure scenarios in a computationally efficient manner. Adaptive Stress Testing (AST) is a method that uses reinforcement learning (RL) paradigms to efficiently find failure scenarios in stochastic sequential decision-making systems.

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 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 Contemporary research has found differences between demographic groups in their stated instrument cluster component design preferences. For instance, elderly drivers prefer large icons and textual displays of information, while younger drivers preferred gauges to display information. The purpose of this study was to evaluate whether instrument clusters, designed for specific demographic groups, would facilitate safe driving behavior and solicit higher evaluation scores in their targeted demographics. Fifty participants, consisting of 30 elderly and 20 younger drivers (gender-balanced), completed a series of tasks to retrieve information from the instrument cluster while driving a high-fidelity simulator. Participants’ driving behavior, response time, subjective ratings, and a semi-structured post-experimental interview on different cluster designs were collected to evaluate each instrument cluster design.

Abstract This article presents data mining methodologies designed to support data-driven, long-term, and large-scale research in the areas of in-vehicle monitoring, learning, and assessment of older adults’ driving behavior and physiological signatures under a set of well-defined driving scenarios. The major components presented in the article include the instrumentation of an easily transportable vehicle data acquisition system (VDAS) designed to collect multimodal sensor data during naturalistic driving, an ontology that enables the study of driver behaviors at different levels of integration of semantic heterogeneity into the driving context, and a driving trip segmentation algorithm for automatically partitioning a recorded real-world driving trip into segments representing different types of roadways and traffic conditions.

Abstract Numerical prediction of a confined, co-flowing, laminar jet diffusion flame has been investigated under sinusoidal “g-jitter” to describe the flame structure; this type of flame-body force interaction is typical of a microgravity environment such as in the spacecraft. We introduced g-jitter in the direction orthogonal to the fuel and air inflow. We show that the lower frequencies (0.1-0.5 Hz) of sinusoidal g-jitter significantly affected the flame geometry and behavior. The majority of the flame structure was found to oscillate directly in response to the imposed g-jitter. It has also been observed that nonlinearity in the response behaviors is more prominent in the reaction zone of the flame.

Abstract A classical antiskid brake system (ABS) is typically used to control the brake fluid pressure by creating repeated cycles of decreasing and increasing brake force to avoid wheel locking, causing the fluctuation of the brake hydraulic pressure and resulting in vibration during wheel rotation. This article proposes a new approach of skid control for ABS by controlling the disk pad position. This new approach involves using a modest control method to determine the optimal skid that allows the wheel to exert maximum friction force for decelerating the vehicle by shifting the brake pad position instead of modulating the brake fluid pressure. This pad position control (PPC) method works in a continuous manner. Therefore, no rapid changes are required in the brake pressure and wheel rotation speed. To identify the PPC braking performance, braking test simulations and experiments have been carried out.

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 The dynamics and comfort of a vehicle closely depends on the stiffness of its suspension system. The suspension system of a vehicle always had to trade-off between comfort and performance of a vehicle; since for comfort a softer suspension is preferred which in turn decreases the aerodynamics and cornering performance and increases the ride height of the vehicle; whereas in stiffer suspension the ride height can be lowered, but forces due to bumps are transferred all the way up to the drivers cabin. This article aims to design a vehicle suspension model with variable stiffness using quasi-zero stiffness (QZS) mechanism and study its force-displacement characteristics and minimize the fundamental stiffness of the suspension system. The model developed uses the principle of negative stiffness to achieve low stiffness for the softer suspension system.

Abstract In times when automated driving is becoming increasingly relevant, dynamic simulators present an appropriate simulation environment to faithfully reproduce driving scenarios. A realistic replication of driving dynamics is an important criterion to immerse persons in the virtual environments provided by the simulator. Motion Cueing Algorithms (MCAs) compute the simulator’s control input, based on the motions of the simulated vehicle. The technical restrictions of the simulator’s actuators form the main limitation in the execution of these input commands. Typical dynamic simulators consist of a hexapod with six degrees of freedom (DoF) to reproduce the vehicle motion in all dimensions. Since its workspace dimensions are limited, significant improvements in motion capabilities can be achieved by expanding the simulator with redundant DoF by means of additional actuators.

Abstract Agent-based modeling and simulation (ABMS) has been a popular approach for modeling autonomous and interacting agents in a multi-agent system. Specifically, ABMS can be applied to connected and automated vehicles (CAVs) since CAVs can operate autonomously with the help of onboard sensors, and cooperate with each other through vehicle-to-everything (V2X) communications. In order to improve energy efficiency and mobility of traffic, we have developed an online feedforward/feedback longitudinal controller for CAVs to cooperatively merge at ramps. Agent-based CAV models were built in the Unity3D environment, where vehicles are given connectivity and autonomy through C#-based scripting API. Agent-based infrastructure model is also built as a Unity3D simulation network based on the city of Mountain View, California.

Abstract Autonomous Vehicle (AV) technology has the potential to fundamentally transform the automotive industry, reorient transportation infrastructure, and significantly impact the energy sector. Rapid progress is being made in the core artificial intelligence engines that form the basis of AV technology. However, without a quantum leap in testing and verification, the full capabilities of AV technology will not be realized. Critical issues include finding and testing complex functional scenarios, verifying that sensor and object recognition systems accurately detect the external environment independent of weather conditions, and building a regulatory regime that enables accumulative learning. The significant contribution of this article is to outline a novel methodology for solving these issues by using the Florida Poly AV Verification Framework (FLPolyVF).