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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 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 Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. However, modeling driver’s lane-changing decision and implementation process is challenging due to the complexity and uncertainty of driving behaviors. To address this issue, this article presents a personalized Lane-Changing Model (LCM) for Advanced Driver Assistance System (ADAS) based on deep learning method. The LCM contains three major computational components. Firstly, with abundant inputs of Root Residual Network (Root-ResNet), LCM is able to exploit more local information from the front view video data. Secondly, the LCM has an ability of learning the global spatial-temporal information via Temporal Modeling Blocks (TMBs). Finally, a two-layer Long Short-Term Memory (LSTM) network is used to learn video contextual features combined with lane boundary based distance features in lane change events.

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 In today’s competitive automobile market, driver comfort is at utmost importance and the bar is being raised continuously. Gear Shifting is a crucial customer touch point. Any issue or inconvenience caused while shifting gear can result into customer dissatisfaction and will impact the brand image. While there are continual efforts being taken by most of the car manufactures, “Double Bump” in gearshift has remained as a pain area and impact severely on the shift feel. This is more prominent in North-South (N-S) transmissions. In this paper ‘Double Bump’ is a focus area and a mathematical / analytical approach is demonstrated by analyzing ‘impacting parameters’ and establishing their co-relation with double bump. Additionally, the results are also verified with a simulation model.

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 The main focus of this article is the online estimation of the terminal airspace sector capacity from the Air Traffic Controller 0ATC) dynamical neural model using Neural Partial Differentiation (NPD) with permissible safe separation and affordable workload. For this purpose, a primarily neural model of a multi-input-single-output (MISO) ATC dynamical system is established, and the NPD method is used to estimate the model parameters from the experimental data. These estimated parameters have a less relative standard deviation, and hence the model validation results show that the predicted neural model response is well matched with the intervention of the ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters, which are unknown in practice.

Abstract Commercial vehicles contribute to the majority of freight transportation in the United States. They are also significant fuel consumers, with over 23% of fuel used in transportation in the United States. The gas price volatility and increasingly stringent regulation on greenhouse-gas emissions have driven manufacturers to adopt new fuel-efficient technologies. Among others, an advanced transmission control strategy, which can provide tangible improvement with low incremental cost. In the commercial sector, individual drivers have little or no interest in vehicle fuel economy, contrary to fleet owners. Aggressive driving behavior can greatly increase the real-world vehicle fuel consumption. However, the effectiveness of transmission calibration to match the shift strategy to the driving characteristics is still a challenge.

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 When commercial vehicles drive in a mountainous area, the complex road condition and long slopes cause frequent acceleration and braking, which will use 25% more fuel. And the brake temperature rises rapidly due to continuous braking on the long-distance downslopes, which will make the brake drum fail with the brake temperature exceeding 308°C [1]. Meanwhile, the kinetic energy is wasted during the driving progress on the slopes when the vehicle rolls up and down. Our laboratory built a model that could calculate the distance from the top of the slope, where the driver could release the accelerator pedal. Thus, on the slope, the vehicle uses less fuel when it rolls up and less brakes when down. What we do in this article is use this model in a real vehicle and measure how well it works.

Abstract Automated guided vehicle systems (AGVS) are the prominent one in modern material handling systems used in small manufacturing enterprises (SMEs) due to their exciting features and benefits. This article pinpoints the need of AGVS in SMEs by describing the material handling selection in SMEs and enlightening recent technological developments and approaches of the AGVS. Additionally, it summarizes the analytical and simulation-based tools utilized in design problems of AGVS along with the influence of material handling management and key hurdles of AGVS. The current study provides a limelight towards making smart automated guided vehicles (AGVs) with the simplified and proper routing system and favorable materials and more importantly reducing the cost and increasing the flexibility.

Abstract In this study, active-passive filling friction stir repairing (A-PFFSR) process was employed to repair the volume defects in friction stir welding (FSW) joints of Al-Cu alloy. The volume defects with varied geometries were first machined into taper holes, which are similar to keyhole defect by a rotational tool with a threaded pin. Then, the keyhole defect was effectively filled with the materials around the keyhole and an additional filler using a number of nonconsumable pinless tools with the shoulders having six spiral flutes. The macro/microstructures, microhardness, and tensile properties of the repaired joints were investigated. The influences of plunge speed on macro/microstructures and mechanical properties of the repaired joints have been analyzed too. It was noticed that decreasing plunge speed was effective to improve the frictional heat and material flow, which increased joint surface integrity avoiding interfacial drawbacks.

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 The proposed approach present the development of a computer tool that allows, in the first phase, the modeling of the electric vehicle power chain. This phase is based on a library developed under the Matlab-Simulink simulation environment. This library contains all the components of the power chain; it offers the selection of the desired configuration of each component. In the second phase, the tool solves the autonomy optimization problem. This problem is resolved by a program based on genetic algorithms. This program permits to optimize the configuration parameters maximizing the vehicle autonomy of the chosen chain. This tool is based on a graphical interface developed under the Matlab simulation environment.

Abstract A key element in an ergonomically designed driver’s seat in a car is the correct identification of driver seating position and posture accommodation. Current practice by the automotive Original Equipment Manufacturer (OEM) is to utilize the Society of Automotive Engineering (SAE) J1517 standard practice as a reference. However, it was found that utilizing such guidelines, which were developed based on the American population, did not fit well with the anthropometry and stature of the Malaysian population. This research seeks to address this issue by comparing the SAE J1517 Model against Malaysian preferred driving position. A total of 62 respondents were involved for the driver seating position and accommodation study in the vehicle driver’s seat buck mockup survey and measurements. The results have shown that the Malaysian drivers prefer to sit forward as compared to the SAE J1517 Model and have shorter posture joint angle.

Abstract The advancement towards development of autonomy follows either the bottom-up approach of gradually improving and expanding existing Advanced Driver Assist Systems (ADAS) technology where the driver is present in the control loop or the top-down approach of directly developing autonomous vehicle hardware and software using alternative approaches without the driver present in the control loop. Most ADAS systems today fall under the classification of SAE Level 1 which is also referred to as the driver assistance level. The progression from SAE Level 1 to SAE Level 2 or partial automation involves the critical task of merging automated lateral control and automated longitudinal control such that the tasks of steering and acceleration/deceleration are not required to be handled by the driver under certain conditions [1].

Abstract To evaluate that automated vehicle is as safe as a human driver, a following question is studied: how does an automated vehicle react under extreme conditions close to collision? In order to understand the collision avoidance capability of an automated vehicle, we should analyze not only such post-extreme condition behavior but also pre-extreme condition behavior. We present a theory to analyze the collision avoidance capability of automated driving technologies. We also formulate a collision avoidance equation on the theory. The equation has two types of solutions: response driving plans and preparation driving plans. The response driving plans are supported by response strategy on which the vehicle reacts after detection of a hazard and they are highly efficient in terms of travel time.

Abstract Automotive vehicle includes various systems like engine, transmission, exhaust, air intake, cooling and many more systems. No doubt the performance of individual system depends upon their core design. But for performance, the system needs to be fastened properly. In automotive, most of the joints used fasteners which helps in serviceability of the components. There are more than thousands of fasteners used in the vehicle. At various locations, we found issue of bolt loosening and because of this design intent performance has not met by the system. During product development of ECS (Engine cooling system), various issues reported to loosening the bolt. The pre-mature failure of bolt loosening, increases the interest in young engineers for understanding the behavior of fastener in vehicle running conditions. This paper focuses on the design of wedge shape of washer to avoid bolt loosening.

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.