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A new realistic motion control algorithm for digital human models is presented in this paper based on the principle of effort minimization. The proposed algorithm is developed through an innovative mathematical model to make the applications more flexible and more global, especially for the visualization of human motions in automotive assembly operations. The central idea of this unique model is to interpret the solution of the homogeneous Lagrange equation for a mannequin as the origin of dynamic motion. Furthermore, a digital human possesses about 42 joints over the main body except the head, fingers and toes, and offers a large room of kinematic redundancy. We have found 14 new 3-D independent motion markers assigned over the human body to constitute a Cartesian coordinate system, under which a minimum-effort based dynamic control scheme is developed using a state-feedback linearization procedure.

With the increasing emphasis on Systems Engineering, there is a need to ensure that Electrical/Electronic (E/E) Systems Endurance Testing of vehicles, in a real world environment, prior to Production Launch, is performed in a manner and at a technological level that is commensurate with the high level of electronics and computers in contemporary vehicles. Additionally, validating the design and performance of individual standalone electronic systems and modules “on the bench” does not guarantee that all the permutations and combinations of real-world hardware, software, and driving conditions are taken into account. Traditional Proving Ground (PG) vehicle testing focuses mainly on powertrain durability testing, with only a simple checklist being used by the PG drivers as a reminder to cycle some of the electrical components such as the power window switches, turn signals, etc.

The Hybrid III family of dummies is used to estimate the response of an occupant during a crash. One recent area of interest is the response of the neck during air bag loading. The biomechanical response of the Hybrid III dummy's neck was based on inertial loading during crash events, when the dummy is restrained by a seat belt and/or seat back. Contact loading resulting from an air bag was not considered when the Hybrid III dummy was designed. This paper considers the effect of air bag loading on the 5th percentile female Hybrid III dummies. The response of the neck is presented in comparison to currently accepted biomechanical corridors. The Hybrid III dummy neck was designed with primary emphasis on appropriate flexion and extension responses using the corridors proposed by Mertz and Patrick. They formulated the mechanical performance requirements of the neck as the relationship between the moment at the occipital condyles and the rotation of the head relative to the torso.

This paper utilizes GPS tracked multiday travel activities to estimate the temporal distribution of electricity loads and assess battery electric vehicle (BEV) grid impacts at a significant market penetration level. The BEV load and non-PEV load vary by time of the day and day of the week. We consider two charging preferences: home priority assumes BEV drivers prefer charging at home and would not charge at public charging stations unless the state of charge (SOC) of the battery is not sufficient to cover the way back to home; and charging priority does not require drivers to defer charging to home and assumes drivers will utilize the first available charging opportunity. Both home and charging priority scenarios show an evening peak demand. Charging priority scenario also shows a morning peak on weekdays, possibly due to workplace charging.

With increasingly stringent regulations mandating the improvement of vehicle fuel economy, automotive manufacturers face growing pressure to develop and implement technologies that improve overall system efficiency. One such technology is an automatic (auto) stop-start feature. Auto stop-start reduces idle time and reduces fuel use by temporarily shutting the engine off when the vehicle comes to a stop and automatically re-starting it when the brake is released, or the accelerator is pressed. As mandated by the U.S. Congress, the U.S. Environmental Protection Agency (EPA) is required to keep the public informed about fuel saving practices. This is done, in partnership with the U.S. Department of Energy (DOE), through the fueleconomy.gov website. The “Fuel-Saving Technologies” and “Gas Mileage Tips” sections of the website are focused on helping the public make informed purchasing decisions and encouraging fuel-saving driving habits.

We report a chemical sensing technology composed of engineered bioluminescent bacteria placed on an integrated microluminometer. The bacteria have been engineered to luminesce when the targeted compound is metabolized, while the microluminometer detects, processes, and then reports the magnitude of this optical signal. In this work we report our progress in the development of these biosensors and present data from our prototypes.

We report a chemical sensing technology composed of engineered bioluminescent bacteria placed on an integrated microluminometer. The bacteria have been engineered to luminesce when the targeted compound is metabolized, while the microluminometer detects, processes, and then reports the magnitude of this optical signal. In this work we report our progress in the development of these biosensors and present data from our early prototypes.

This paper contains a review of methods for deriving risk curves from biomechanical data obtained from impact experiments on human surrogates. It covers many of the problems and pitfalls of obtaining realistic human risk curves from impact experiments. The strength and weakness of both parametric and non-parametric methods are evaluated. The limitations of standard analysis of censored impact test data are presented. Methods are given for determining risk curves from both doubly censored data and data obtained from impacts to body regions in which there are more than one mechanism of injury. A detailed set of examples is presented in which different experimental data are analyzed using the Consistent Threshold method and the logistic approach. Finally risk curves for published data are presented for the femur, head, thorax, and neck.

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

It is rare for an attempt towards optimization at the fleet-level when consideration is given to the sheer number of seemingly unpredictable interactions among vehicles and infrastructure in congested urban areas. To close the gap, we introduce a simulation based framework to explore the impact of speed synchronization on fuel economy improvement for fleets in traffic. The framework consists of traffic and vehicle modules. The traffic module is used to simulate driver behavior in urban traffic; and the vehicle module is employed to estimate fuel economy. Driving schedule is the linkage between these two modules. To explore the impact, a connected vehicle technology sharing vehicle speed information is used for better fuel economy of a fleet including six vehicles. In all scenarios analyzed, the leading vehicle operates under the EPA Urban Dynamometer Driving Schedule (UDDS), while the other five vehicles follow the leader consecutively.

The pulse severities from various vehicle impact tests need to be assessed during the impact structure development and targeting stage to assure that the occupants can meet the injury criteria as required. The conventional method using TTZV (time to zero velocity), TDC (total dynamic crush), and G1/G2 (two stage averaged pulse) is often unable to give a quick and clear answer to the question being raised. A simple numerical tool is developed here to assess the pulse severity with a single parameter in which the severity is expressed as the amount of chest travel under a certain target restraint curve or chest A-D curve. The tool is applied to several front impact vehicle pulses to show the effectiveness. The new method developed here can be used to assess the pulse severity in an easy and objective way along with conventional parameters.

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving.

Automobiles of the future will be forced to travel further on a tank of fuel while discharging lower levels of pollutants. Currently, the United States uses in excess of 18 million barrels of petroleum per day. Sixty-six percent is used in the transportation of people and goods. Highway vehicles currently account for just under two-thirds of the nation's gasoline consumption, and about one-third of the total United States energy usage [1] while contributing a significant amount to the annual U.S. air pollutant burden. In 1997, 57.5% of the carbon monoxide, 29.8% of the nitrogen oxides, 27.2% of the volatile organic compounds, and 23.8% of the carbon dioxide came from highway vehicles [2] The U.S. government has supported R&D pertinent to highway vehicles since the early 1960's, to mitigate these problems.

Driver is a key component in vehicle simulation. An ideal driver model simulates driving patterns a human driver may perform to negotiate road profiles. There are simulation packages having the capability to simulate driver behavior. However, it is rarely documented how they work with road profiles. This paper proposes a new truck driver model for vehicle simulation to imitate actual driving behavior in negotiating road grade and curvature. The proposed model is developed based upon Gipps' car-following model. Road grade and curvature were not considered in the original Gipps' model although it is based directly on driver behavior and expectancy for vehicles in a stream of traffic. New parameters are introduced to capture drivers' choice of desired speeds that they intend to use in order to negotiating road grade and curvature simultaneously. With the new parameters, the proposed model can emulate behaviors like uphill preparation for different truck drivers.

To mitigate head impact injuries of vehicle occupants in impact accidents, the FMVSS 201 requires padding of vehicle interior so that under the free-moving-head-form impact, the head injury criterion (HIC) is below the limit. More recently, pedestrian head impact on the vehicle bonnet has been a subject being studied and regulated as requirements to the automobile manufacturers. Over the years, the square wave has been considered as the best waveform for head impacts, although it is impractical to achieve. This paper revisits the head impact topic and challenges the optimality of aiming at the square waveform. It studies several different simple waveforms, with the objective to achieve minimal HIC or minimal crush space required in head-form impacts. With that it is found that many other waveforms can be more efficient and more practical than the square wave, especially for the pedestrian impact.

To provide useful information for automakers to design successful plug-in hybrid electric vehicle (PHEV) products and for energy and environmental analysts to understand the social impact of PHEVs, this paper addresses the question of how many of the U.S. consumers, if buying a PHEV, would prefer what electric ranges. The Market-oriented Optimal Range for PHEV (MOR-PHEV) model is developed to optimize the PHEV electric range for each of 36,664 sampled individuals representing U.S. new vehicle drivers. The optimization objective is the minimization of the sum of costs on battery, gasoline, electricity and refueling hassle.

To make informed decisions about travel and vehicle purchase, consumers need unbiased and accurate information of the fuel economy they will actually obtain. In the past, the EPA fuel economy estimates based on its 1984 rules have been widely criticized for overestimating on-road fuel economy. In 2008, EPA adopted a new estimation rule. This study compares the usefulness of the EPA's 1984 and 2008 estimates based on their prediction bias and accuracy and attempts to improve the prediction of on-road fuel economies based on consumer and vehicle attributes. We examine the usefulness of the EPA fuel economy estimates using a large sample of self-reported on-road fuel economy data and develop an Individualized Model for more accurately predicting an individual driver's on-road fuel economy based on easily determined vehicle and driver attributes. Accuracy rather than bias appears to have limited the usefulness of the EPA 1984 estimates in predicting on-road MPG.

In this paper, we study the sensitivity of a vehicle impact harshness (IH) performance to the suspension bushing rates. A mid-sized uni-body SUV is selected for this study, with the acceleration responses at the driver seat track and the steering wheel as objective functions. A sensitivity study is conducted using an ADAMS full vehicle model including a tire model and flexible body structure representation over an IH event. The study resulted in the identification of key bushings that affect the IH performance and its sensitivity to the bushing rates. Based on the results, we came-up with an “optimal” bushing set that minimizes impact harshness, which was subjectively verified to result in significant improvement in IH.

In September 2003, the Mercedes Car Group set another milestone by introducing the fifth generation of automatic transmissions developed and manufactured in-house since 1960. The world's first 7-speed automatic transmission 7G-TRONIC is featured in the Mercedes-Benz S, SL, CL and E-Classes with V8 gasoline engines. Deduced from the demands of the requirement specifications, the 5-speed automatic transmission was decisively improved; the result is a clear increase in spontaneity, agility, fuel economy, and driving comfort for the customer. And because of the harmony between the vehicle and its powertrain, excellent results in the areas of performance, reduced emissions, comfort, and acoustics are obtained.

NVH targets for future vehicles are often defined by utilizing a competitive benchmarking vehicle in conjunction with an existing production and/or reference vehicle. Mode management of full vehicle modes is one of the most effective and significant NVH strategies to achieve such targets. NVH dynamic characteristics of a full vehicle can be assessed and quantified through experimental modal testing for determination of global body mode resonance frequency, damping property, and mode shape. Major body modes identified from full vehicle modal testing are primarily dominated by the vehicle's body-in-white structure. Therefore, an estimate of BIW modes from full vehicle modes becomes essential, when only full vehicle modes from experimental modal testing exist. Establishing BIW targets for future vehicles confines the fundamental NVH behavior of the full vehicle.