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The addition of synthetic traffic to a driving simulation greatly enhances the realism of the virtual world. Giving this traffic human-like behavior is likewise desirable, and has been the focus of some research over the past few years. This paper presents a modular architecture for including autonomous traffic in a driving simulation, and describes the first steps taken toward the application of this architecture to the DaimlerChrysler Auburn Hills Simulator. By separating the planning part of the agent from the low-level control and vehicle dynamics systems, the described architecture permits the inclusion of powerful, previously developed components in a straightforward way; in the present application, agents use Soar to reason about their actions. This paper gives an overview of the structures of the agents, and of the entire system, describes the components and their implementations, and discusses the current state of the project and plans for the future.

When unexpected integrated lubricant-related problems occur, for example, high-speed operating conditions, lubricants can be degraded and even fail to reach certain automatic transmission parts. Dryout of oil films means a serious lack of lubrication, which may damage the power transmission line and key parts. Dryout of ATF is analogous to that in forced convective boiling and condensation. It thus requires special efforts to determine the mechanics that induce such fluid transport phenomena. This paper presents an experimental investigation of lubricant activities in the main shaft. Dimensional analysis is applied, and flow maps of the air-oil flow and dryout regimes are constructed. Correlations closely agree with the data and reveal the possibility of dryout. Heat transfer effect is briefly discussed.

A series of inflator tank tests was carried out to determine the amount of transient heat losses to the tank wall during these tests. The time history data of tank wall temperature, and tank interior gas temperature and pressure, were measured. The tank wall temperature data were analyzed using an inverse heat conduction method to generate the transient heat loss fluxes from the tank gas to the tank wall. The validity of the results are discussed along with the physical reasoning and experimental observations. This is the first part of an effort in a research project to develop a comprehensive heat transfer model to predict the transient heat losses to the tank wall during the inflator tank test.

A new approach to driver seat-position modeling is presented. The equations of the Seating Accommodation Model (SAM) separately predict parameters of the distributions of male and female fore/aft seat position in a given vehicle. These distributions are used together to predict specific percentiles of the combined male-and-female seat-position distribution. The effects of vehicle parameters-seat height, steering-wheel-to-accelerator pedal distance, seat-cushion angle, and transmission type-are reflected in the prediction of mean seat position. The mean and standard deviation of driver population stature are included in the prediction for the mean and standard deviation of the seat-position distribution, respectively. SAM represents a new, more flexible approach to predicting fore/aft seat-position distributions for any driver population in passenger vehicles. Model performance is good, even at percentiles in the tails of the distribution.

This paper investigates the effects of manufacturing variations in fuel injectors on the engine performance with emphasis on emissions. The variations are taken into consideration within a Reliability-Based Design Optimization (RBDO) framework. A reduced version of Multi-Zone Diesel engine Simulation (MZDS), MZDS-lite, is used to enable the optimization study. The numerical noise of MZDS-lite prohibits the use of gradient-based optimization methods. Therefore, surrogate models are developed to filter out the noise and to reduce computational cost. Three multi-objective optimization problems are formulated, solved and compared: deterministic optimization using MZDS-lite, deterministic optimization using surrogate models and RBDO using surrogate models. The obtained results confirm that manufacturing variation effects must be taken into account in the early product development stages.

Connected autonomy brings with it the means of significantly increasing vehicle Energy Economy (EE) through optimal Eco-Driving control. Much research has been conducted in the area of autonomous Eco-Driving control via various methods. Generally, proposed algorithms fall into the broad categories of rules-based controls, optimal controls, and meta-heuristics. Proposed algorithms also vary in cost function type with the 2-norm of acceleration being common. In a previous study the authors classified and implemented commonly represented methods from the literature using real-world data. Results from the study showed a tradeoff between EE improvement and run-time and that the best overall performers were meta-heuristics. Results also showed that cost functions sensitive to the 1-norm of acceleration led to better performance than those which directly minimize the 2-norm.

In FOA (First Order Analysis) any vehicle body structure might be interpreted as a collective simple structure that can be decomposed into 3 fundamental structure types. The first structure is the “BEAM”, whose cross sectional properties as well as its material dominates the mechanical behavior, the second is the “PANEL (shear panel, plate, and shell)”, whose mechanical behavior can be varied by changing its geometrical properties in the thickness direction, i.e. adding beads or flanges. The third structure is the “JOINT”, which connects the proceeding structures, and transfer complex three-dimensional loads with three-dimensional deformation. In the present work, we shall propose a methodology to identify a portion of an arbitrary FE model of an automotive body structure, with a “BEAM” structure in the FOA approach. In the latter chapter of this paper, cross section loads will be related with cross sectional properties in the aspect of the element strain energy concept.

We have developed a methodology for analysis of Premixed Charge Compression Ignition (PCCI) engines that applies to conditions in which there is some stratification in the air-fuel distribution inside the cylinder at the time of combustion. The analysis methodology consists of two stages: first, a fluid mechanics code is used to determine temperature and equivalence ratio distributions as a function of crank angle, assuming motored conditions. The distribution information is then used for grouping the mass in the cylinder into a two-dimensional (temperature-equivalence ratio) array of zones. The zone information is then handed on to a detailed chemical kinetics model that calculates combustion, emissions and engine efficiency information. The methodology applies to situations where chemistry and fluid mechanics are weakly linked.

Thermal distortions of friction disks caused by frictional heating modify pressure distribution on friction surfaces. Pressure distribution, in turn, determines distribution of generated frictional heat. These interdependencies create a complex thermoelastic system that, under some conditions, may become unstable and may lead to severe pressure concentrations with very high local temperature and stress. The phenomenon is responsible for many common thermal failure modes of friction elements and is known as frictionally excited thermoelastic instability (TEI). In the paper, one of the cases of TEI is investigated theoretically and experimentally. The study involves a two-disk structure with one fiction disk and one matching steel disk that have one friction interface. An unsteady heat conduction problem and an elastic contact problem are modeled as axisymmetric ones and are solved using the finite element method.

The rapid development of connected and automated vehicle technologies together with cloud-based mobility services are revolutionizing the transportation industry. As a result, huge amounts of data are being generated, collected, and utilized, hence providing tremendous business opportunities. However, this big data poses serious challenges mainly in terms of data privacy. The risks of privacy leakage are amplified by the information sharing nature of emerging mobility services and the recent advances in data analytics. In this paper, we provide an overview of the connected vehicle landscape and point out potential privacy threats. We demonstrate two of the risks, namely additional individual information inference and user de-anonymization, through concrete attack designs. We also propose corresponding countermeasures to defend against such privacy attacks. We evaluate the feasibility of such attacks and our defense strategies using real world vehicular data.

Wet clutches are complex hydrodynamic devices used in both conventional and electrified drivetrain systems. They couple or de-couple powertrain components for applications such as automatic shifting, engine disconnect and torque vectoring. Clutch engagement behaviors vary greatly, depending on design parameters and operating conditions. Because of their direct impact on vehicle drivability and fuel economy, a predictive CAE model is desired for enabling analytical design verification processes. During engagement, a wet clutch transmits torque through viscous shear and asperity contact. A conventional Coulomb’s model, which is routinely utilized in shift simulations, is inadequate to capture non-linear hydrodynamic effects for higher fidelity analysis. Extensive research has been conducted over the years to derive hydrodynamic torque transfer models based on 1D squeeze film or 3D CFD. They are typically coupled with an elastic asperity contact model for mechanical torque transfer.

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.

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).

Assessments of reach capability using human figure models are commonly performed by exercising each joint of a kinematic chain, terminating in the hand, through the associated ranges of motion. The result is a reach envelope determined entirely by the segment lengths, joint degrees of freedom, and joint ranges of motion. In this paper, the validity of this approach is assessed by comparing the reach envelopes obtained by this method to those obtained in a laboratory study of men and women. Figures were created in the Jack human modeling software to represent the kinematic linkages of participants in the laboratory study. Maximum reach was predicted using the software's kinematic reach-envelope generation methods and by interactive manipulation. Predictions were compared to maximum reach envelopes obtained experimentally. The findings indicate that several changes to the normal procedures for obtaining maximum reach envelopes for seated tasks are needed.

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.

Cost reduction, quality improvement, and regulatory compliance are well-recognized competitive issues. Companies must excel along each of these fronts while operating in an environment of rapid and multi-faceted change, limited financial and human capital, and increasing product development time pressure. In addition, consumers are demanding automobiles that provide greater performance, function, and comfort while emitting lower emissions, consuming fewer gallons of gasoline, injuring fewer humans, and requiring fewer dollars to build and purchase. A solution to these seemingly conflicting objectives is to take a systems view of the product and industry. This paper explores the material decision process so that manufacturers, component suppliers, and material providers may better understand the interlocking web of compromises that shape the pursuit of value-added alternatives and avoidance of unprofitable compromises.

The paper presents a brief summary of results from a Delphi forecast focused on North American Auto industry philosophies, practices, and tools for various phases of the product- development process, and their impact on cost, quality, and design lead time. The forecasting technique is a systematic, iterative method of forecasting based upon the judgement of a panel composed of knowledgeable experts. The study provides a snapshot of current expectations for the product development process, including the use of computer aided design tools, design methodologies, strategies, tools, and design education/training. The paper highlights issues pertaining to product cycle time, organizational barriers, supplier's role and globalization challenges.

New high-temperature Mg alloys are being considered to replace 380 Al in transmission cases, wherein bolt-load retention, and creep, is of prime concern. One of these alloys is die cast AE42, which has much better creep properties than does AZ91D but is still not as creep resistant as 380 Al. It is thus important to investigate bolt-load retention and creep of AE42 as an initial step in assessing its suitability as a material for transmission housings. To that end, the bolt-load retention behavior of die-cast AE42, AZ91D and 380 Al have been examined using standard M10 bolts specially instrumented with stable high-temperature strain gages. The bolt-load retention test pieces were die cast in geometries approximating the flange and boss regions in typical bolted joints. Bolt-load retention properties were examined as a function of time (at least 100 hours), temperature (150 and 175 °C) and initial bolt preload (14 to 34 kN).

Innovations in mobility are built upon a management of complex interactions between sub-systems and components. A need for CAE tools that are capable of system simulations is well recognized, as evidenced by a growing number of commercial packages. However impressive they are, the predictability of such simulations still rests on the representation of the base components. Among them, a wet clutch actuator continues to play a critical role in the next generation propulsion systems. It converts hydraulic pressure to mechanical force to control torque transmitted through a clutch pack. The actuator is typically modeled as a hydraulic piston opposed by a mechanical spring. Because the piston slides over a seal, some models have a framework to account for seal friction. However, there are few contributions to the literature that describe the effects of seals on clutch actuator behaviors.

The U.S. Environmental Protection Agency’s (EPA’s) Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) tool was created to estimate greenhouse gas (GHG) emissions from light-duty vehicles. ALPHA is a physics-based, forward-looking, full vehicle computer simulation capable of analyzing various vehicle types with different powertrain technologies, showing realistic vehicle behavior, and auditing of all energy flows in the model. In preparation for the midterm evaluation (MTE) of the 2017-2025 light-duty GHG emissions rule, ALPHA has been refined and revalidated using newly acquired data from model year 2013-2016 engines and vehicles. The robustness of EPA’s vehicle and engine testing for the MTE coupled with further validation of the ALPHA model has highlighted some areas where additional data can be used to add fidelity to the engine model within ALPHA.