Indiana University-Purdue University Indianapolis (IUPUI) / Road accident reconstruction and simulation investigates the accident causes, suggests improvements in vehicle design and investigates failures in vehicle control and safety systems such as the anti-lock brake system (ABS) and air-bag deployment. This thesis focuses on analysis of crash data from vehicles not equipped with collision warning systems. Vehicle parameters before and during an accident can be recorded using an Event Data Recorder (EDR) which helps in reconstructing an accident. This tool, installed in the vehicle, records different crash parameters like vehicle speed, lateral and longitudinal acceleration, seat-belt status, and air-bag deployment over a period that spens the accident. This thesis focuses on accident reconstruction with and without EDR data. A simulation software tool called HVE is used to visually recreate the reconstructed accidents. HVE is a platform to execute different accident simulation methods which are used for specific types of simulations. Two such simulation methods, EDSMAC4 and EDHIS, are discussed in this thesis. The former is an important method for vehicle-to-vehicle collisions and the latter is used for analysis of human behavior involved in the accident. Three real-life accidents were chosen for reconstruction and simulation. They were Bus and Car accident, Three Vehicle accident and Intersection accident. These particular accidents were chosen to represent a diverse selection of accidents based on the following parameters: the locations of the accidents, the vehicles involved in each accident, and the data available. A qualitative analysis of vehicle occupant's behavior is also presented for one of the three accidents. The thesis discusses in detail the reconstruction of these three accidents. Throughout these simulations, the thesis illustrates the advantages and limitations of the EDR and HVE simulation software for accident reconstruction and simulation.

Two of the leading causes of automobile accidents are driver impairment due to alcohol and drowsiness. Apparently, a relatively large percentage of these accidents occur because drivers are unaware of the degree to which they are impaired due to these sources. The purpose of this research was to develop models which could detect driver impairment due to alcohol, drowsiness, or the combination of alcohol and drowsiness, and which could be practically implemented in an automobile. Such detection models, if successfully implemented in conjunction with a system to warn an impaired driver of his or her condition, could potentially save hundreds of lives each year. Six driver-subjects operated a computer controlled driving simulator during each of four conditions. The four conditions consisted of a control condition, an alcohol condition, a sleep-deprived condition, and a combination alcohol and sleep-deprived condition. Moderate levels of alcohol and sleep deprivation were used for this study. Nineteen performance and behavioral measures were collected during this study. Each measure was evaluated singly and in combination with other measures to determine potential value for detection of driver impairment. Detection models were then formulated using the most promising detection measures. The results indicated that a useful on-board drowsiness impairment detection device is possible and practical for highway driving. This device would also, in all likelihood, provide useful detection information regardless of whether low to moderate amounts of alcohol were present in a drowsy driver. The results also showed that on-board alcohol impairment detection may be possible at moderate to high BAC. / Master of Science