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For at least 15 years it has been recognized that pre-crash data captured by event data recorders might help illuminate the actions of drivers prior to crashes. In left-turning crashes where pre-crash data are available from both vehicles it should be possible to estimate features such as the location and speed of the opposing vehicle at the time of turn initiation and the reaction time of the opposing driver. Difficulties arise however from measurement errors in pre-crash data and because the EDR data from the two vehicles are not synchronized so the resulting uncertainties should be accounted for. This paper describes a method for accomplishing this using Markov Chain Monte Carlo computation. First, planar impact methods are used to estimate the speeds at impact of the involved vehicles. Next, the impact speeds and pre-crash EDR data are used to reconstruct the vehicles’ trajectories during approximately 5 seconds preceding the crash.

A continuing topic of interest is how to best use information from Event Data Recorders (EDR) to reconstruct crashes. If one has a model which can predict EDR data from values of the target variables of interest, such as vehicle speeds at impact, then in principle one can invert this model to estimate the target values from EDR measurements. In practice though this can require solving a system of nonlinear equations and a reasonably flexible method for carrying this out involves replacing the inverse problem with nonlinear least-squares (NLS) minimization. NLS has been successfully applied to two-vehicle planar impact crashes in order to estimate impact speeds from different combinations of EDR, crush, and exit angle measurements, but an open question is how to assess the uncertainty associated with these estimates. This paper describes how Markov Chain Monte Carlo (MCMC) simulation can be used to quantify uncertainty in planar impact crashes.

Martinez and Schlueter [6] described a three-phase model for reconstructing tripped rollover crashes, where the vehicle's path is divided into pre-trip, trip, and post-trip phases. Brach and Brach [9] also described this model and noted that the trajectory segmentation method for the pre-trip phase needed further validation. When a vehicle leaves a measurable yaw mark at the start of its pre-trip phase it might be possible to compare estimates from the three-phase model to those obtained using the critical speed method, and this paper describes Bayesian reconstruction of two such cases. For the first, the 95 percent confidence interval for the case vehicle's initial speed, estimated using the critical speed method, was (64 mph, 81 mph) while the 95 percent confidence interval via the three-phase model was (66 mph, 79 mph).

The critical speed method uses measurements of the radii of yawmarks left by vehicles, together with values for centripetal acceleration, to estimate the speeds of the vehicles when the yawmarks were made. Several field studies have indicated that equating the centripetal force with braking friction produced biased estimates, but that the biases tended to be small (e.g. within 10%-15% on average) and led to underestimates, suggesting that the method can be useful for forensic purposes. Other studies, however, have challenged this conclusion. The critical speed method has also seen use in safety-related research, where it is important to have a reliable assessment of the uncertainty associated with a speed estimate. This paper describes a variant of the critical speed method, where data from field tests lead to an informative prior probability distribution for the centripetal acceleration.

In statistical modeling, cross-validation refers to the practice of fitting a model with part of the available data, and then using predictions of the unused data to test and improve the fitted model. In accident reconstruction, cross-validation is possible when two different measurements can be used to estimate the same accident feature, such as when measured skidmark length and pedestrian throw distance each provide an estimate of impact speed. In this case a Bayesian cross-validation can be carried out by (1) using one measurement and Bayes theorem to compute a posterior distribution for the impact speed, (2) using this posterior distribution to compute a predictive distribution for the second measurement, and then (3) comparing the actual second measurement to this predictive distribution. An actual measurement falling in an extreme tail of the predictive distribution suggests a weakness in the assumptions governing the reconstruction.

The Center for Clean Products has conducted a life-cycle assessment involving a comparison of exterior body closure panels made of different lightweight materials (aluminum, carbon fiber-reinforced polymer [CFRP] and glass fiber-reinforced polymer [GFRP]), to steel closure panels weighing 220 lbs as the baseline. In an additional, more forward-looking assessment, a monocoque body made of a carbon fiber-based composite was assumed to replace a conventional steel body, resulting in a substantial weight reduction (more than 60%). The primary results reveal that CFRP appears to be the least environmentally burdensome material in 9 of the 14 impact categories evaluated. This is mainly due to the fact that CFRP has the maximum weight reduction potential of all the materials evaluated (about 60% over steel), resulting in a much smaller quantity of material needed.

Fuel cells have attracted a great deal of attention in the last few years as potential replacements for conventional gasoline- or diesel-powered internal combustion engines. This study evaluated the potential life-cycle environmental impacts of a fuel cell vehicle (FCV) using a 50 kW proton exchange membrane (PEM) fuel cell system (both with and without a fuel reformer), and compared them with those of a gasoline-fueled internal combustion engine vehicle (ICEV). The fuels considered for the fuel cell systems were direct hydrogen (without reformer), and methanol and gasoline (with reformer). Exclusive of the propulsion systems, the rest of the vehicle was assumed to be the same across all the profiles.

This project team conducted a life-cycle-based environmental evaluation of new, lightweight materials (e.g., titanium, magnesium) used in two concept 3XVs -- i.e., automobiles that are three times more fuel efficient than today's automobiles -- that are being designed and developed in support of the Partnership for a New Generation of Vehicles (PNGV) program. The two concept vehicles studied were the DaimlerChrysler ESX2 and the Ford P2000. Data for this research were drawn from a wide range of sources, including: the two automobile manufacturers; automobile industry reports; government and proprietary databases; past life-cycle assessments; interviews with industry experts; and models.

The goal of this work is to calculate the lifetime emissions for a 1996 Saturn automobile over its 193,000-km useful life. To do this, the authors developed a vehicle-specific method for calculating nonmethane hydrocarbon (NMHC), carbon monoxide (CO), carbon dioxide (CO2), and nitrous oxide (NOx) emissions. Vehicle-specific emissions data were not available for methane (CH4) sulfur oxides (SOx), dinitrogen oxide (N2O), and particulate matter (PM). The authors selected most applicable emission factors for these compounds. The authors then compared the results of these emission calculations to several other published methods. All methods produced similar results for CO2 emissions. However, the various calculation methods produced significantly different results for NMHC, CO, NOx, CH4, SOx, N2O, and PM emissions. The vehicle-specific emissions tended to be lower than many of the other methods.

Automotive designers have the greatest influence on the environmental impacts of the production, use, and disposal of the automobile through their design choices. This paper surveys the use of tools for environmental design in the automotive industry and also presents preliminary results of the development and demonstration of a new Life-Cycle Design Toolkit for automotive designers developed by the University of Tennessee Center for Clean Products and Clean Technologies in partnership with the Saturn Corporation and the U.S. Environmental Protection Agency.

Painting a vehicle's exterior is an energy and emission-intensive process. Molding exterior panels in color has the potential to reduce both environmental burdens and cost. This study compares the material and energy flows for painting exterior panels (one front and one rear fascia) with those for molding the panels in color at the Saturn manufacturing facility. Molding fascias in color requires the addition of 62,700 kg of color pellets to the injection molding process, but eliminates several painting steps. These changes would result in reducing paint consumption by 500,000 kg/yr, energy consumption by 133 million MJ and volatile organic compound (VOC) emissions by 150,000 kg/yr.

Improving the methods and priorities for the collection of Life Cycle Management information is critical to the goal of reducing life cycle environmental impacts from automobile manufacturing. Currently, such information is collected under varying protocols, creating inefficiencies for OEM's and suppliers, and casting doubt on the information's quality. Recommendations from the EPA's CSI Automobile Manufacturing Sector provide important guidance to auto companies, suppliers and other stakeholders for commonizing life cycle data collection and developing data priorities. A variety of disciplines and areas of expertise, at OEM's and suppliers, will need to cooperate in the creation of new data collection tools.

A comprehensive total life cycle cost assessment should include estimates of liability costs to provide the best picture of the financial viability of investments, such as product or process changes and pollution prevention projects. Potential liability costs are by nature difficult to estimate and include a prediction (or probability estimate) of risk. Occupational health liabilities resulting from illnesses to workers due to chemical exposure in specific automotive industrial processes have been evaluated using an incidence rate approach. Potential health risks to workers at varying levels of risk from low to high were determined, and the estimated economic costs resulting from those risks have been developed. These occupational health liability cost estimates can be used for total cost accounting in life cycle management.