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Abstract The ASTM D130 was first issued in 1922 as a tentative standard for the detection of corrosive sulfur in gasoline. A clean copper strip was immersed in a sample of gasoline for three hours at 50°C with any corrosion or discoloration taken to indicate the presence of corrosive sulfur. Since that time, the method has undergone many revisions and has been applied to many petroleum products. Today, the ASTM D130 standard is the leading method used to determine the corrosiveness of various fuels, lubricants, and other hydrocarbon-based solutions to copper. The end-of-test strips are ranked using the ASTM Copper Strip Corrosion Standard Adjunct, a colored reproduction of copper strips characteristic of various degrees of sulfur-induced tarnish and corrosion, first introduced in 1954. This pragmatic approach to assessing potential corrosion concerns with copper hardware has served various industries well for a century.

Abstract Modern Automated Driving (AD) systems rely on safety measures to handle faults and to bring the vehicle to a safe state. To eradicate lethal road accidents, car manufacturers are constantly introducing new perception as well as control systems. Contemporary automotive design and safety engineering best practices are suitable for analyzing system components in isolation, whereas today’s highly complex and interdependent AD systems require a novel approach to ensure resilience to multiple-point failures. We present a holistic and cost-effective safety concept unifying advanced safety measures for handling multiple-point faults. Our proposed approach enables designers to focus on more pressing issues such as handling fault-free hazardous behavior associated with system performance limitations. To verify our approach, we developed an executable model of the safety concept in the formal specification language mCRL2.

Abstract The United Nation Economic Commission for Europe (UNECE) Regulation 155—Cybersecurity and Cybersecurity Management System (UN R155) mandates the development of cybersecurity management systems (CSMS) as part of a vehicle’s lifecycle. An inherent component of the CSMS is cybersecurity risk management and assessment. Validation and verification testing is a key activity for measuring the effectiveness of risk management, and it is mandated by UN R155 for type approval. Due to the focus of R155 and its suggested implementation guideline, ISO/SAE 21434:2021—Road Vehicle Cybersecurity Engineering, mainly centering on the alignment of cybersecurity risk management to the vehicle development lifecycle, there is a gap in knowledge of proscribed activities for validation and verification testing.

Abstract This article proposes a novel approach of a hybrid system of physics and data-driven modeling for accurately estimating the state of charge (SOC) of a battery. State of Charge (SOC) is a measure of the remaining battery capacity and plays a significant role in various vehicle applications like charger control and driving range predictions. Hence the accuracy of the SOC is a major area of interest in the automotive sector. The method proposed in this work takes the state-of-the-art practice of Kalman filter (KF) and merges it with intelligent capabilities of machine learning using neural networks (NNs). The proposed hybrid system comprises a physics-based battery model and a plurality of NNs eliminating the need for the conventional KF while retaining its features of the predictor-corrector mechanism of the variables to reduce the errors in estimation.

Abstract With the introduction of advanced lightweight materials with complex microstructures and behaviors, more focus is put on the accurate determination of their forming limits, and that can only be possible through experiments as the conventional theoretical models for the forming limit curve (FLC) prediction fail to perform. Despite that, CAE engineers, designers, and toolmakers still rely heavily on theoretical models due to the steep costs associated with formability testing, including mechanical setup, a large number of tests, and the cost of a stereo digital image correlation (DIC) system. The international standard ISO 12004-2:2021 recommends using a stereo DIC system for formability testing since two-dimensional (2D) DIC systems are considered incapable of producing reliable strains due to errors associated with out-of-plane motion and deformation.

Abstract The 2010 Environmental Protection Agency (EPA) Emission Standard for heavy-duty engines required 0.2 g/bhp-hr over certification cycles (cold and hot Federal Test Procedure [FTP]), and the California Air Resources Board (CARB) standards require 0.02 g/bhp-hr for the same cycles leading to a 90% reduction of overall oxides of nitrogen (NOx) emissions. Similar reductions may be considered by the EPA through its Cleaner Trucks Initiative program. In this article, aftertreatment system components consisting of a diesel oxidation catalyst (DOC); a selective catalytic reduction (SCR) catalyst on a diesel particulate filter (DPF), or SCR-F; a second DOC (DOC2); and a SCR along with two urea injectors have been analyzed, which could be part of an aftertreatment system that can achieve the 0.02 g/bhp-hr standard.

Abstract Heavy-duty (HD) diesel engines are the primary propulsion systems in use within the transportation sector and are subjected to stringent oxides of nitrogen (NOx) and particulate matter (PM) emission regulations. The objective of this study is to develop a robust calibration technique to optimize HD diesel engine for performance and emissions to meet current and future emissions standards during certification and real-world operations. In recent years, California - Air Resources Board (C-ARB) has initiated many studies to assess the technology road maps to achieve Ultra-Low NOx emissions for HD diesel applications [1]. Subsequently, there is also a major push for the complex real-world driving emissions as the confirmatory and certification testing procedure in Europe and Asia through the UN-ECE and ISO standards.

Abstract In this study, a parametric thoracic spine (T-spine) model was developed to account for morphological variations among the adult population. A total of 84 CT scans were collected, and the subjects were evenly distributed among age groups and both sexes. CT segmentation, landmarking, and mesh morphing were performed to map a template mesh onto the T-spine vertebrae for each sampled subject. Generalized procrustes analysis (GPA), principal component analysis (PCA), and linear regression analysis were then performed to investigate the morphological variations and develop prediction models. A total of 13 statistical models, including 12 T-spine vertebrae and a spinal curvature model, were combined to predict a full T-spine 3D geometry with any combination of age, sex, stature, and body mass index (BMI). A leave-one-out root mean square error (RMSE) analysis was conducted for each node of the mesh predicted by the statistical model for every T-spine vertebra.

Abstract A major contributor to fatal vehicle crashes is hydroplaning, which has traditionally been reported at a specific vehicle speed for a given operating condition. However, hydroplaning is a complex phenomenon requiring a holistic, probabilistic, and multidisciplinary approach. The objective of this article is to develop a probabilistic approach to predict Hydroplaning Potential and Risk that integrates fundamental understanding of the interdependent factors: hydrology, fluid-solid interactions, tire mechanics, and vehicle dynamics. A novel theoretical treatment of Hydroplaning Potential and Risk is developed, and simulation results for the prediction of water film thickness and Hydroplaning Potential are presented. The results show the advantages of the current approach which could enable the improvement of road, vehicle, and tire design, resulting in greater safety of the traveling public.

Abstract Safety is always a crucial aspect of developing autonomous systems, and the motivation behind this project comes from the need to address the traffic crashes occurring globally on a daily basis. The present work studies the coexistence of the novel rule-based behavioral planning framework with the five key advanced driver assistance system (ADAS) features as proposed in this article to fulfill the safety requirements and enhance the comfort of the driver/passengers to achieve a receding-horizon autopilot. This architecture utilizes data from the sensor fusion and the prediction module for the prediction time horizon of 2 s iteratively, which is continuously moving forward (hence, the receding horizon), and helps the behavior planner understand the intent of other vehicles on the road in advance.

Abstract Tailored blanks offer great lightweighting opportunities for automotive industry and were applied on the front rails of a sedan in this research. To achieve the most efficient material usage, all the front rail parts were tailored into multiple sheets with the gauge of each sheet defined as a design variable for optimization. The equivalent static loads (ESL) method was adopted for linear optimization and the Insurance Institute for Highway Safety (IIHS) moderate overlap frontal crash as the nonlinear analysis load case. The torsion and bending stiffness of the sedan body in white (BIW) were set as design constraints. The occupant compartment intrusion in IIHS moderate overlap front crash was set as design objective to be minimized. The optimal thickness configuration for the tailored front rail designs was obtained through ESL optimization for multiple mass saving targets.

Abstract In this work, an in situ multispecies portable emission measurement system (PEMS) is presented. The system is based on tunable diode laser absorption spectroscopy (TDLAS) and is capable of measuring tailpipe emissions without the necessity of online calibration. It is intended for application on passenger cars within the real drive emission (RDE) procedure of the Worldwide Harmonized Light Duty Test Procedure (WLTP). In contrast to the extractive measurement principles of commercially available PEMS, the introduced measurement system does not require gas sampling or preconditioning and thus does not suffer from the same low-pass filter effects on the measurements. These differences are suspected to have an impact on certification-relevant measurement data. Measurements have been conducted on a 3-cylinder 1 liter EURO 6 b gasoline engine test bench to investigate the differences between the presented measurement system and a commercially available PEMS.

Abstract The advancement in vision sensors and embedded technology created the opportunity in autonomous vehicles to look ahead in the future to avoid potential obstacles and steep regions to reach the target location as soon as possible and yet maintain vehicle safety from rollover. The present work focuses on developing a nonlinear model predictive controller (NMPC) for a high-speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles, and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in the MATLAB environment. The CasADi tool provides a platform to formulate the NMPC problem using symbolic expressions, which is an easy and efficient way of solving the optimization problem. In the present work, the vehicle lateral dynamics are modeled using the Pacejka nonlinear tire model.

Abstract An improved control method of automatic emergency braking (AEB) for rear-end collision avoidance is proposed, which combines the advantages of a time-to-collision (TTC) control algorithm and professional driver emergency braking behavior. The TTC control algorithm mostly adopts phased braking, and although it can avoid collision effectively, the braking process is radical and brake comfort is poor. The emergency braking system with professional driver fitting (PDF) has good comfort and can also avoid collision successfully. However, its brake trigger time is too early, which leads to the stopping distance being too large under high-speed conditions and affects the road utilization. By combining the advantages of the two control methods, an improved control algorithm for AEB is proposed. When the TTC value is not greater than a predetermined limit, the PDF control switch will be closed to avoid collision.

Abstract Advanced Driver Assistance Systems (ADAS) are playing a significant role in enhancing driver safety and occupant comfort in modern vehicles. The primary research focus in this domain includes the precise perception of the current state and the prediction of the future states of dynamic agents. To perform these tasks an intelligent agent capable of operating in the stochastic environment is implemented in the form of various ADAS features. A trajectory prediction problem can be defined using either a model-based or data-driven approach. The current article addresses the problem of trajectory prediction in the stochastic environment using a model-based approach with a quintic polynomial as a function approximator to ensure smooth acceleration trajectory for the left and right lane-change maneuvers. The task of trajectory prediction also considers the information about the vehicle dynamics, the concept of Receding Time Horizon (RTH), and the variable curvature model of the road.

Abstract The variation of inflation pressure has an important effect on the longitudinal slip characteristics of tires that can affect the braking performance of the vehicle, so the influence of inflation pressure should be taken into account in high-precision tire models. However, the effects of inflation pressure and vertical load on tire force and moment characteristics are usually coupled. When the inflation pressure is changing while keeping the load constant, the tire contact patch and carcass stiffness will change at the same time, so the contribution of tread and carcass to tire traction properties cannot be decoupled so that the tire design cannot be well guided. On the contrary, if the vertical loading method is changed, the vertical deflection control is used instead of load control.

Abstract We model neck loading as a function of impact severity in aligned rear impacts. Neck loading is understood and expected to vary as a function of factors including crash severity, occupant compartment design, and occupant metrics. Within occupant compartment design, seat and restraint characteristics are expected to influence the biomechanical response and occupant kinematics. We investigated the relationship between biomechanical neck-loading metrics and impact severity expressed as speed change (delta-V) by examining 47 low to moderate speed rear-impact crash and sled tests utilizing the Hybrid III (HIII) 50th male Anthropomorphic Test Device (ATD). Our hypothesis was that the relationship between severity expressed as delta-V and the neck metrics examined could be modeled as linear consistent with an understanding that neck loading in a rear impact results from the acceleration of the vehicle.

Abstract Analysis of Kinetic Metrics in Submarining vs Non-Submarining Conditions for a 6YO Pediatric Human Body Model in Frontal Impacts

Abstract The Japanese population is aging rapidly, raising the number of traffic accidents involving elderly drivers. In Japan, single-vehicle accidents are a serious problem because they often result in fatalities. We analyzed these accidents by vehicle type, age group, and driving area. To examine the risk of accidents of the elderly drivers, their driving frequency needs to be considered, which is less. Moreover, it is difficult to know the actual distance driven by them. Therefore, in this paper, based on the assumption that the number of rear-end collisions is a proxy for the traffic volume, we used the number of such collisions as a control for the driving frequency. It was found that in single-vehicle accidents, elderly drivers were at higher risk than other age groups, especially when driving light motor vehicles (K-type vehicles) in non-urban areas.

Abstract The increased scope of active and passive safety in motor vehicles and the enforcement of approval requirements for individual parts and assemblies affect the design and parameters of a car’s motion. The tire, which transmits forces and torques onto the road’s surface is a particularly crucial element in the vehicle. Its structure, type of mixture, and operating conditions determine the safety of vehicle motion. The three-axial force system loads the tires of the car and affects both the tread and sidewall, as well as the suspension and steering system. Taking into account the controllability and stability of movement, the tire is subjected to dynamic and thermal loads, as well as to wear and random damage. This negatively impacts on the joints of composite layers. The sudden loss of pressure in the tire can lead to serious accidents, especially when moving at high speeds, due to changes in the rolling radius.