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Longevity is one of the great achievements of the twentieth century. This paper will explore ways that elderly people can employ technology to enhance their independence, loosely termed “ElderTech.” ElderTech is designed to establish a sustained, long-term investment in research and development (R&D) for technologies that can provide the largest growing population, Americans over the age of 65, with the tools to ensure active aging (maintaining independence, self-reliance, and an enhanced quality of life). It will also promote aging in place (in the home); and will address and ease Medicare's financial burden on the federal government. ElderTech is aimed to establish a technology framework that will ensure that the United States (U.S.) is ready to meet the needs of its older Americans.

This paper presents a simple method of using Voronoi partitions for estimating vehicle fuel economy from a limited set of engine operating conditions. While one of the overarching goals of engine research is to continually improve vehicle fuel economy, evaluating the impact of a change in engine operating efficiency on the resulting fuel economy is a non-trivial task and typically requires drive cycle simulations with experimental data or engine model predictions and a full suite of engine controllers over a wide range of engine speeds and loads. To avoid the cost of collecting such extensive data, proprietary methods exist to estimate fuel economy from a limited set of engine operating conditions. This study demonstrates the use of Voronoi partitions to cluster and quantize the fuel consumed along a complex trajectory in speed and load to generate fuel consumption estimates based on limited simulation or experimental results.

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

There is a pressing need to develop accurate and robust approaches for predicting vehicle speed to enhance fuel economy/energy efficiency, drivability and safety of automotive vehicles. This paper details outcomes of research into various methods for the prediction of vehicle velocity. The focus is on short-term predictions over 1 to 10 second prediction horizon. Such short-term predictions can be integrated into a hybrid electric vehicle energy management strategy and have the potential to improve HEV energy efficiency. Several deterministic and stochastic models are considered in this paper for prediction of future vehicle velocity. Deterministic models include an Auto-Regressive Moving Average (ARMA) model, a Nonlinear Auto-Regressive with eXternal input (NARX) shallow neural network and a Long Short-Term Memory (LSTM) deep neural network. Stochastic models include a Markov Chain (MC) model and a Conditional Linear Gaussian (CLG) model.

Mcity at the University of Michigan in Ann Arbor provides a realistic off-roadway environment in which to test vehicles and drivers in complex traffic situations. It is intended for testing of various levels of vehicle automation, from advanced driver assistance systems (ADAS) to fully self-driving vehicles. In a recent human factors study of interfaces for teen drivers, we performed parallel experiments in a driving simulator and Mcity. We implemented driving scenarios of moderate complexity (e.g., passing a vehicle parked on the right side of the road just before a pedestrian crosswalk, with the parked vehicle partially blocking the view of the crosswalk) in both the simulator and at Mcity.

Traditional reliability assessment methods based on physical testing can require prohibitively large sample sizes in many applications. This has led manufacturers to employ virtual testing using CAE models in place of physical testing. However, when the CAE models are not valid, the resulting reliability assessment may be unreliable. In this paper we develop theory and methodology in which traditional physical testing can be used in conjunction with CAE models to create a new type of accelerated testing that requires smaller sample sizes than traditional test plans while exhibiting robustness with respect to inaccuracies in the CAE models. These test plans are implemented by physically testing a biased sample of products and employing a variance reduction technique such as importance sampling. The CAE model is used as a prior belief for failure probability from which one can derive the sampling plan which minimizes the variance.

The Human Motion Simulation Framework is a hierarchical set of algorithms for physical task simulation and analysis. The Framework is capable of simulating a wide range of tasks, including standing and seated reaches, walking and carrying objects, and vehicle ingress and egress. In this paper, model predictions for the terminal postures of standing object transfer tasks are compared to data from 20 subjects with a wide range of body dimensions. Whole body postures were recorded using optical motion capture for one-handed and two-handed object transfers to target destinations at three angles from straight ahead and three heights. The hand and foot locations from the data were input to the HUMOSIM Framework Reference Implementation (HFRI) in the Jack human modeling software. The whole-body postures predicted by the HFRI were compared to the measured postures using a set of measures selected for their importance to ergonomic analysis.

A research scheme and preliminary results of a pilot study concerning upper body coordination in reach movements is presented. Techniques for multi-joint arm movements were used to obtain the kinematics of each body segment in reach movements to targets spatially distributed in a horizontal plane. Further understanding of the control mechanisms associated with coordination is investigated by combining the information of gaze orientation and body segment movements during reach activities. The implicit sequence of body segments in reach movement can be derived from their kinematic characteristics. Moreover, an identification of phases composing a reach movement is attempted.

This SAE EDGE™ Research Report explores how the deployment of automated vehicles (AVs) will affect the insurance industry and the principles of liability that underly the structure of insurance in the US. As we trade human drivers for suites of sensors and computers, who (or what) is responsible when there is a crash? The owner of the vehicle? The automaker that built it? The programmer that wrote the code? Insurers have over 100 years of experience and data covering human drivers, but with only a few years’ worth of information on AVs – how can they properly predict the true risks associated with their deployment? Without an understanding of the nature and risks of AVs, how can the government agencies that regulate the insurance industry provide proper oversight? Do the challenges AVs present require a total reworking of our insurance and liability systems, or can our current structures be adapted to fit them with minor modifications?

The emission of excessive quantities of NOx when the automobile air conditioner is turned on has received a fair amount of attention in recent years. Since NOx is a smog precursor, it is important to understand the reasons for this jump in emissions especially on hot sunny days when air conditioner usage is at a maximum. A simple thermodynamic model is used to demonstrate how the torque from a typical air conditioner compressor is mainly related to the ambient temperature. The compressor's on-off cycling patterns are also characterized. Since the compressor significantly loads the engine, it affects fuel economy and emissions. The key independent variable that we employ to represent engine load is fuel rate. The correlations between engine-out NOx emissions and fuel rate are shown for a number of light duty vehicles and trucks. From these, a physical model for engine-out NOx emissions (with and without air conditioning) is presented.

In this paper the development of statistical metamodels and statistical fast running models is presented first. They are utilized for propagating uncertainties in a multi-discipline design optimization process. Two main types of uncertainty can be considered in this manner: uncertainty due to variability in design variables or in random parameters; uncertainty due to the utilization of metamodels instead of the actual simulation models during the optimization process. The value of the new developments and their engagement in multi-discipline design optimization is demonstrated through a case study. An underwater vehicle is designed under four different disciplines, namely, noise radiation, self-noise due to TBL excitation, dynamic response due to propulsion impact loads, and response to an underwater detonation.

Wet clutch packs are widely used in today’s automatic transmission systems for gear-ratio shifting. The frictional interfaces between the clutch plates are continuously lubricated with transmission fluid for both thermal and friction management. The open clutch packs shear transmission fluid across the rotating plates, contributing to measurable energy losses. A typical multi-speed transmission includes as many as 5 clutch packs. Of those, two to three clutches are open at any time during a typical drive cycle, presenting an opportunity for fuel economy gain. However, reducing open clutch drag is very challenging, while meeting cooling requirements and shift quality targets. In practice, clutch design adjustment is performed through trial-and-error evaluation of hardware on a test bench. The use of analytical methodologies is limited for optimizing clutch design features due to the complexity of fluid-structure interactions under rotating conditions.

This work investigates the potential improvements in vehicle fuel economy possible by optimizing gear shift strategies to leverage a novel boosting device, an electrically assisted variable speed supercharger (EAVS), also referred to as a power split supercharger (PSS). Realistic gear shift strategies, resembling those commercially available, have been implemented to control upshift and downshift points based on torque request and engine speed. Using a baseline strategy from a turbocharged application of a MY2015 Ford Escape, a vehicle gas mileage of 34.4 mpg was achieved for the FTP75 drive cycle before considering the best efficiency regions of the supercharged engine.

New technology emerges daily that enhances traffic and automotive systems. It challenges us to redefine safety in terms of achieving the system's overall goals at a minimal ‘cost’, i.e., loss of life and property. This redefinition requires that we develop the tools needed to understand driving as a decision making activity over a system consisting of driver, vehicle and infrastructure with an ever present and increasing flow of information between them. This even requires that we revisit the very notion of an accident. Loss of life and limb will increasingly be due dysfunctional decision-making, human and automated. This paper describes an approach to the study of driving as a system-based decision making and suggests an approach to the management of dysfunctional decisions. By making explicit, with the aid of game and systems theory, the role of information in driving we suggest a traffic-system based approach to behavioral constraints needed to ensure safety.

Simulations of humans performing seated reaches require accurate descriptions of the movements of the body segments that make up the torso. Data to generate such simulations were obtained in a laboratory study using industrial, auto, and truck seats. Twelve men and women reached to push-button targets located throughout their right-hand reach envelopes as their movements were recorded using an electromagnetic tracking system. The data illustrate complex patterns of motion that depend on target location and shoulder range of motion. Pelvis motion contributes substantially to seated reach capability. On padded seats, the effective center of rotation of the pelvis is often within the seat cushion below the pelvis rather than at the hips. Lumbar spine motions differ markedly depending on the location of the target. A categorization of reach targets into four zones differentiated by torso kinematics is proposed.

Simulation of reach movements is an essential component for proactive ergonomic analysis in digital human modeling and for numerous applications in vehicle design. Most studies on reach kinematics described human movements in static conditions. Earlier studies of reach performance in vibration environments focused mainly on fingertip deviation without considering multi-body dynamics. However, for the proper assessment of reach performance under whole-body vibration exposure, a multi-body biodynamic model needs to be developed. This study analyzes three dimensional reach kinematics of the upper extremity during in-vehicle operations, using a multi-segmental model of the upper body in the vibratory environment. The goals are to identify the characteristics of upper body reach movements and to investigate vibration-induced changes in joint kinematics. Thirteen subjects reached to four target directions in the right hemisphere.

This paper provides insight into the tradeoffs between exhaust energy recovery and increased pumping losses from the flow restriction of the electric turbo-generator (eTG) assessed using thermodynamic principles and with a detailed GT-Power engine model. The GT-Power engine model with a positive displacement expander model was used to predict the influence of back pressure on in-cylinder residuals and combustion. The eTG is assessed for two boosting arrangements: a conventional turbocharger (TC) and an electrically assisted variable speed (EAVS) supercharger (SC). Both a low pressure (post-turbine) and high pressure (pre-turbine) eTG are considered for the turbocharged configuration. The reduction in fuel consumption (FC) possible over various drive cycles is estimated based on the steady-state efficiency of frequently visited operating points assuming all recovered energy can be reused at an engine efficiency of 30% with 10% losses in the electrical path.

Liver injuries can be significant in vehicle crashes. In this study, the liver anatomy was quantified in both adult and pediatric populations as a function of gender and age. Five anatomical liver measurements were determined using CT scans of 260 normal livers. These measurements include the area and volume, and the length, width, and girth of the liver (IRB HUM00041441). To characterize geometrical shape, an inscribed sphere and circumscribed ellipsoid were fitted on the measurements. In the pediatric population the liver area and volume continuously increased with age. When normalized by patient weight, volume measurements show a decrease in volume with age, suggesting that the liver occupies a smaller proportion of the body with age. In the adult population, liver measurements varied with gender. The superior and inferior locations of the liver were also recorded with respect to the spine. The lower portion was at the L3 in small children and at L2 as children approached puberty.

A comprehensive quasi-dimensional computer simulation of the spark-ignition (SI) engine was used to explore part-load, fuel economy benefits of the Variable Stroke Engine (VSE) compared to the conventional throttled engine. First it was shown that varying stroke can replace conventional throttling to control engine load, without changing the engine characteristics. Subsequently, the effects of varying stroke on turbulence, burn rate, heat transfer, and pumping and friction losses were revealed. Finally these relationships were used to explain the behavior of the VSE as stroke is reduced. Under part load operation, it was shown that the VSE concept can improve brake specific fuel consumption by 18% to 21% for speeds ranging from 1500 to 3000 rpm. Further, at part load, NOx was reduced by up to 33%. Overall, this study provides insight into changes in processes within and outside the combustion chamber that cause the benefits and limitations of the VSE concept.