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A special spot weld element (SWE) is presented for simplified representation of spot joints in complex structures for structural durability evaluation using the mesh-insensitive structural stress method. The SWE is formulated using rigorous linear four-node Mindlin shell elements with consideration of weld region kinematic constraints and force/moments equilibrium conditions. The SWEs are capable of capturing all major deformation modes around weld region such that rather coarse finite element mesh can be used in durability modeling of complex vehicle structures without losing any accuracy. With the SWEs, all relevant traction structural stress components around a spot weld nugget can be fully captured in a mesh-insensitive manner for evaluation of multiaxial fatigue failure.

Often, when assessing the distraction or ease of use of an in-vehicle task (such as entering a destination using the street address method), the first question is “How long does the task take on average?” Engineers routinely resolve this question using computational models. For in-vehicle tasks, “how long” is estimated by summing times for the included task elements (e.g., decide what to do, press a button) from SAE Recommended Practice J2365 or now using new static (while parked) data presented here. Times for the occlusion conditions in J2365 and the NHTSA Distraction Guidelines can be determined using static data and Pettitt’s Method or Purucker’s Method. These first approximations are reasonable and can be determined quickly. The next question usually is “How likely is it that the task will exceed some limit?”

The use of lightweight materials is one of the important means to reduce the quality of the vehicle, which involves the connection of dissimilar materials, such as the combination of lightweight materials and traditional steel materials. The riveting quality of self-piercing riveting (SPR) technology will directly affect the safety and durability of automobiles. Therefore, in the initial joint development process, the quality of self-piercing riveting should be inspected and classified to meet safety standards. Based on this, this paper divides the self-piercing riveting quality into riveting appearance quality and riveting section quality. Aiming at the appearance quality of riveting, the generation of cracks on the lower surface of riveting will seriously affect the riveting strength. The existing method of identifying cracks on the lower surface of riveting based on artificial vision has strong subjectivity, low efficiency and cannot be applied on a large scale.

The Lane Change Task (LCT) provides a simple, scorable simulation of driving, and serves as a primary task in studies of driver distraction. It is widely accepted, but somewhat limited in functionality, a problem this project partially overcomes. In the Lane Change Task, subjects drive along a road with 3 lanes in the same direction. Periodically, signs appear, indicating in which of the 3 lanes the subject should drive, which changes from sign to sign. The software is plug-and-play for a current Windows computer with a Logitech steering/pedal assembly, even though the software was written 18 years ago. For each timestamp in a trial, the software records the steering wheel angle, speed, and x and y coordinates of the subject. A limitation of the LCT is that few characteristics of this useful software can be readily modified as only the executable code is available (on the ISO 26022 website), not the source code.

Making manned and remotely-controlled wheeled and tracked vehicles easier to drive, especially off-road, is of great interest to the U.S. Army. If vehicles are easier to drive (especially closed hatch) or if they are driven autonomously, then drivers could perform additional tasks (e.g., operating weapons or communication systems), leading to reduced crew sizes. Further, poorly driven vehicles are more likely to get stuck, roll over, or encounter mines or improvised explosive devices, whereby the vehicle can no longer perform its mission and crew member safety is jeopardized. HMI technology and systems to support human drivers (e.g., autonomous driving systems, in-vehicle monitors or head-mounted displays, various control devices (including game controllers), navigation and route-planning systems) need to be evaluated, which traditionally occurs in mission-specific (and incomparable) evaluations.

Engaging in visual-manual tasks such as selecting a radio station, adjusting the interior temperature, or setting an automation function can be distracting to drivers. Additionally, if setting the automation fails, driver takeover can be delayed. Traditionally, assessing the usability of driver interfaces and determining if they are unacceptably distracting (per the NHTSA driver distraction guidelines and SAE J2364) involves human subject testing, which is expensive and time-consuming. However, most vehicle engineering decisions are based on computational analyses, such as the task time predictions in SAE J2365. Unfortunately, J2365 was developed before touch screens were common in motor vehicles.

Automated driving is considered a key technology for reducing traffic accidents, improving road utilization, and enhancing transportation economy and thus has received extensive attention from academia and industry in recent years. Although recent improvements in artificial intelligence are beginning to be integrated into vehicles, current AD technology is still far from matching or exceeding the level of human driving ability. The key technologies that need to be developed include achieving a deep understanding and cognition of traffic scenarios and highly intelligent decision-making. Automated Vehicles, the Driving Brain, and Artificial Intelligenceaddresses brain-inspired driving and learning from the human brain's cognitive, thinking, reasoning, and memory abilities. This report presents a few unaddressed issues related to brain-inspired driving, including the cognitive mechanism, architecture implementation, scenario cognition, policy learning, testing, and validation.

Automated vehicles (AVs)—and the automated driving systems (ADSs) that enable them—are increasing in prevalence but remain far from ubiquitous. Progress has occurred in spurts, followed by lulls, while the motor transportation system learns to design, deploy, and regulate AVs. Automated Vehicles: A Human/Machine Co-learning Experience focuses on how engineers, regulators, and road users are all learning about a technology that has the potential to transform society. Those engaged in the design of ADSs and AVs may find it useful to consider that the spurts and lulls and stakeholder tussles are a normal part of technology transformations; however, this report will provide suggestions for effective stakeholder engagement. Click here to access the full SAE EDGETM Research Report portfolio.

In partly autonomous driving such as level 2 or level 3 automatic driving from SAE international classification, the switching of the driving right between the human driver and the machine plays an important role in the driving process of vehicle [1]. In this paper, the data collected from vehicle states and the driving behavior of drivers is completed via a simulator and self-report questionnaires. A fuzzy analytic network process (Fuzzy-ANP) model is developed to evaluate the driving capability of the drivers in real time from vehicle states due to its direct inherent link to the change of the driving state of drivers Moreover, in this model, the idea of group decision and multi-index fusion is adopted. The questionnaire is required to identify the experimental results from the simulator. The results show that the proposed Fuzzy-ANP model can evaluate the driving capability of the participants in real time accurately.

Tanker trucks are commonly used for transporting liquid material including chemical and petroleum products. On the one hand, tanker trucks are susceptible to rollover accidents due to the high center of gravity when they are loaded and due to the liquid sloshing effects when the tank is partially filled. On the other hand, tanker truck rollover accidents are among the most dangerous vehicle crashes, frequently resulting in serious to fatal driver injuries and significant property damage, because the liquid cargo is often hazardous and flammable. Therefore, effective schemes for tanker truck rollover avoidance are highly desirable and can bring a considerable amount of societal benefit. Yet, the development of such schemes is challenging, as tanker trucks can operate in various environments and be affected by manufacturing variability, aging, degradation, etc. This paper considers the use of Learning Reference Governor (LRG) for tanker truck rollover avoidance.

Strain-rate sensitivity has been neglected in the simulation of the traditional stamping process because the strain rate typically does not significantly impact the forming behavior of sheet metals in such a quasi-static process, and traditional crank or link mechanical presses lack the flexibility of slide motion. However, the recent application of servo drive presses in stamping manifests improvement in formability and reduction of springback, besides increased productivity and energy savings. An accurate simulation of servo stamping entails constitutive models with strain-rate sensitivity. This study evaluated a few strain rate-sensitive models including the power-law model, the linear power-law model, the Johnson-Cook model, and the Cowper-Symonds model through the exercise of fitting these models to the experimental data of a deep draw quality (DDQ) steel.

With ongoing concerns about the elevated levels of ambient air pollution in urban areas and the contribution from heavy-duty diesel vehicles, hybrid electric vehicles are considered as a potential solution as they are perceived to be more fuel efficient and less polluting than their conventional engine counterparts. However, recent studies have shown that real-world emissions may be substantially higher than those measured in the laboratory, mainly due to operating conditions that are not fully accounted for in dynamometer test cycles. At the U.S. EPA National Fuel and Vehicle Emissions Laboratory (NVFEL) the in-use criteria emissions and energy efficiency of heavy-duty class 8 vehicles (up to 36280 kg) can be evaluated under controlled conditions in the heavy-duty chassis dynamometer test.

Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs (including those of the engine and aftertreatment systems) has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the short-range prediction based on vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications-based information, is difficult and error-prone.

Self-piercing rivets (SPR) are efficient and economical joining methods used in the manufacturing of lightweight automotive bodies. The finite element method (FEM) is a potentially effective way to assess the joining process of SPRs. However, uncertain parameters could lead to significant mismatches between the FEM predictions and physical tests. Thus, a sensitivity study on critical model parameters is important to guide the high-fidelity modeling of the SPR insertion process. In this paper, an axisymmetric FEM model is constructed to simulate the insertion process of the SPR using LS-DYNA/explicit. Then, several surrogate models are evaluated and trained using machine learning methods to represent the relations between selected inputs (e.g., material properties, interfacial frictions, and clamping force) and outputs (cross-section dimensions).

Aircraft components are commonly produced with 7000 series aluminum alloys (AA) due to its weight, strength, and fatigue properties. Auto Industry is also choosing more and more aluminum component for weight reduction. Current additive manufacturing (AM) methods fall short of successfully producing 7000 series AA due to the reflective nature of the material along with elements with low vaporization temperature. Moreover, lacking in ideal thermal control, print inherently defective products with such issues as poor surface finish alloying element loss and porosity. All these defects contribute to reduction of mechanical strength. By monitoring plasma with spectroscopic sensors, multiple information such as line intensity, standard deviation, plasma temperature or electron density, and by using different signal processing algorithm, AM defects have been detected and classified.

Self-piercing rivet (SPR) joints are a key joining technology for lightweight materials, and they have been widely used in automobile manufacturing. Manual visual crack inspection of SPR joints could be time-consuming and relies on high-level training for engineers to distinguish features subjectively. This paper presents a novel machine learning-based crack detection method for SPR joint button images. Firstly, sub-images are cropped from the button images and preprocessed into three categories (i.e., cracks, edges and smooth regions) as training samples. Then, the Artificial Neural Network (ANN) is chosen as the classification algorithm for sub-images. In the training of ANN, three pattern descriptors are proposed as feature extractors of sub-images, and compared with validation samples. Lastly, a search algorithm is developed to extend the application of the learned model from sub-images into the original button images.

As European and Chinese tailpipe emission regulations for gasoline light-duty vehicles impose particulate number limits, automotive manufacturers have begun equipping some vehicles with a gasoline particulate filter (GPF). Increased understanding of how soot morphology, reactivity, and GPF loading affect GPF filtration and regeneration characteristics is necessary for advancing GPF performance. This study investigates the impacts of morphology, reactivity, and filter soot loading on GPF filtration and regeneration. Soot morphology and reactivity are varied through changes in fuel injection parameters, known to affect soot formation conditions. Changes in morphology and reactivity are confirmed through analysis using a transmission electron microscope (TEM) and a thermogravimetric analyzer (TGA) respectively.

Early exhaust valve opening (eEVO) increases the exhaust gas temperature by faster termination of the power stroke and is considered as a potential warm up strategy for diesel engines aftertreatment thermal management. In this study, first, it is shown that when eEVO is applied, the engine main variables such as the boost pressure, exhaust gas recirculation (EGR) and injection (timing and quantity) must be re-calibrated to develop the required torque, avoid exceeding the exhaust temperature limits and keep the air fuel ratio sufficiently high. Then, a two-step procedure is presented to optimize the engine operation after the eEVO system is introduced, using a validated diesel engine model. In the first step, the engine variables are optimized at a constant eEVO shift. In the second step, optimal eEVO trajectories are calculated using Dynamic Programming (DP) for a transient test cycle.

A new closed-form structural stress solution for a spot weld in a square thin plate under central bending conditions is derived based on the thin plate theory. The spot weld is treated as a rigid inclusion and the plate is treated as a thin plate. The boundary conditions follow those of the published solution for a rigid inclusion in a square plate under counter bending conditions. The new closed-form solution indicates that structural stress solution near the rigid inclusion on the surface of the plate along the symmetry plane is larger than those for a rigid inclusion in an infinite plate and a finite circular plate with pinned and clamped outer boundaries under central bending conditions. When the radius distance becomes large and approaches to the outer boundary, the new analytical stress solution approaches to the reference stress whereas the other analytical solutions do not.

This study uses full drive cycle simulation to compare the fuel consumption of a vehicle with a turbocharged (TC) engine to the same vehicle with an alternative boosting technology, namely, a hybrid supercharger, in which a planetary gear mechanism governs the power split to the supercharger between the crankshaft and a 48 V 5 kW electric motor. Conventional mechanically driven superchargers or electric superchargers have been proposed to improve the dynamic response of boosted engines, but their projected fuel efficiency benefit depends heavily on the engine transient response and driver/cycle aggressiveness. The fuel consumption benefits depend on the closed-loop engine responsiveness, the control tuning, and the torque reserve needed for each technology. To perform drive cycle analyses, a control strategy is designed that minimizes the boost reserve and employs high rates of combustion dilution via exhaust gas recirculation (EGR).