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Abstract Truck platooning comprises a number of trucks equipped with automated lateral and longitudinal vehicle control technology, which allows them to move in tight formation with short following distances. This study is an initial step toward developing an understanding of the occupant injury risks associated with the multiple sequential impacts between truck platoons and roadside safety barriers, regardless of whether the crash is associated with a malfunction of automated control or human operation. Full-scale crash impacts of a tractor-trailer platoon into a concrete bridge guardrail were simulated for a specific Test Level condition according to the Manual for Assessing Safety Hardware (MASH) standards. The model of the bridge barrier was developed based on its drawings, and material properties were assigned according to literature data.

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 Potential collisions with oncoming traffic while turning left belong to the most safety-critical situations accounting for ~25% of all intersection crossing path crashes. A Left Turn Assist (LTA) was developed to reduce the number of crashes. Crucial for the effectiveness of the system is the design of the human-machine interface (HMI), i.e. defining how the system uses the calculated crash probability in the communication with the driver. A driving simulator study was conducted evaluating a warning strategy for two use cases: firstly, the driver comes to a stop before turning (STOP), and secondly, the driver moves on without stopping (MOVE). Forty drivers drove through three STOP and two MOVE scenarios. For the STOP scenarios, the study compared the effectiveness of an audio-visual warning with an additional brake intervention and a baseline. For the MOVE scenarios, the study analyzed the effectiveness of the audio-visual warning against a baseline.

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 Sleep quality and maintenance of the optimal cognitive functioning is of crucial importance for aviation safety. Fatigue Risk Management (FRM) enables the operator to achieve the objectives set in their safety and FRM policies. As in any other risk management cycle, the FRM value can be realized by deploying suitable tools that aid robust decision-making. For the purposes of our article, we focus on fatigue hazard identification to explore the possible developments forward through the enhancement of objective tools in air transport operators. To this end we compare subjective and objective tools that could be employed by an FRM system. Specifically, we focus on an exploratory survey on 120 pilots and the analysis of 250 fatigue reports that are compared with objective fatigue assessment based on the polysomnographic (PSG) and neurocognitive assessment of three experimental cases.

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 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 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 Purpose: Obesity can be a contributing factor to decrease head injuries in motor vehicle collisions (MVCs). We sought to assess whether obesity is associated with decreased head injury from MVCs by sex. Methods: This study was a retrospective observational study and evaluated crash data from 1997 to 2015 obtained from the International Center for Automotive Medicine. Patients were categorized into normal, overweight, and obesity subgroups. The primary endpoint was serious head injury. Multivariate logistic regression analysis was performed, and adjusted odds ratios (AORs) of subgroups were calculated for study outcomes adjusted for any potential confounders. Results: Among enrolled 588 patients, 262 were male with 30.9% normal, 40.1% overweight, and 29.0% obesity subgroups, and 326 were female with 44.8% normal, 24.8% overweight, and 30.4% obesity subgroups.

Abstract Modern driver compartment restraint systems have at least three key components that work together: safety belt system, airbags, and collapsible steering column. During a crash, a steering column will collapse at a predetermined force called breakaway force. Once the force of a crash has reached the breakaway force threshold, the column will move towards the motor area. When the column moves, the drivers’ peak forces and acceleration are decreased because the time and distance that are given to decelerate are increased. The usage of a breakaway force element inside the steering column allows car manufacturers to control the movement of the steering column at a certain point during a crash. Any load below the breakaway force, such as airbag deployment and normal or misuse forces applied by the driver, is absorbed by the system. Today’s force-based systems are optimized (design/configure) using various crash configurations, leading to one specific behavior of the column.

Abstract Brake systems are one of the essential components of vehicles ensuring the safety of roads and passengers as well as accident prevention. Faulty brake systems, however, can cause inevitable accidents. Fatality analysis reporting system of NHTSA (National Highway Transport Safety Association) has reported that heavy and light trucks, which are obliged to be equipped with dual-circuit air brake system, were, respectively, involved in 8.8% and 38.0% of fatal crashes in the United States, during 2017. Number of heavy vehicle accidents due to complete failure of brake system is far less than accidents due to deficiencies such as worn out brake linings, out-of-adjustment push rod strokes, and leak in the circuits. Severe leakages due to ruptured air hoses or punctured reservoir are highly unlikely to be replenished by compressor and would be distinguished through pressure indicator.

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 The frequency of video-capturing collision events from surveillance systems are increasing in reconstruction analyses. The video that has been provided to the investigator may not always include a clear perspective of the relevant area of interest. For example, surveillance video of an incident may have captured a pre- or post-incident perspective that, while failing to capture the precise moment when the pedestrian was struck by a vehicle, still contains valuable information that can be used to assist in reconstructing the incident. When surveillance video is received, a quick and efficient technique to place the subject object or objects into a three-dimensional environment with a known rate of error would add value to the investigation.

Abstract In recent years, to avoid the failure of the onboard perception system, intelligent vehicle infrastructure cooperative systems have been attracting attention in the field of autonomous vehicles. Using the perception technology of roadside sensors to provide supplementary traffic information for autonomous vehicles has become an increasing trend. Several roadside perception solutions select deep learning for three-dimensional (3D) object detection. However, deep learning methods have several issues and lack reliability in practical engineering applications. To tackle this challenge, this study proposes a pedestrian detection algorithm based on roadside Light Detection And Ranging (LiDAR) by combining traditional and deep learning algorithms. To meet real-time demand, Octree with region-of-interest (ROI) selection is introduced and improved to filter the background in each frame, which improves the clustering speed.

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 Due to their large volume structure, when a heavy vehicle encounters sudden road conditions, emergency turns, or lane changes, it is very easy for vehicle rollover accidents to occur; however, well-designed suspension systems can greatly reduce vehicle rollover occurrence. In this article, a novel semi-active suspension adaptive control based on AdaBoost algorithm is proposed to effectively improve the vehicle rollover stability under dangerous working conditions. This research first established a vehicle rollover warning model based on the AdaBoost algorithm. Meanwhile, the approximate skyhook damping suspension model is established as the reference model of the semi-active suspension. Furthermore, the model reference adaptive control (MRAC) system is established based on Lyapunov stability theory, and the adaptive controller is designed.

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.