Thesis (M.A.) (Honours) -- University of Western Sydney, 2001.

Thesis (M. App. Sc.)--Carleton University, 2004. / Includes bibliographical references (p. 144-151). Also available in electronic format on the Internet.

Thesis (M.A.) - Carleton University, 2005. / Includes bibliographical references (p. 42-46). Also available in electronic format on the Internet.

Thesis(M.S.)--Auburn University, 2006. / Abstract. Vita. Includes bibliographic references (p.84-85).

Bibliography: p. 82-83. / Issued also as a M.S. thesis in the Dept. of Electrical Engineering, 1968. / DSR Project no. 79723.

This study was undertaken to determine which variables and combination of variables could be used for the prediction of on-the-road drowsiness. Numerous driver-vehicle performance measures and secondary task performance measures were collected so that the predictability of several definitional measures of drowsiness could be tested. Twelve volunteer subjects were employed in the algorithm development phase of this study. All subjects were from the driver population in the Blacksburg, Virginia area. The participants were sleep deprived and drove a moving base simulator late at night in order to increase the likelihood that they would experience drowsiness while driving. After completion of data collection, numerous algorithms were developed using multiple regression and discriminant analysis methods. Another twelve volunteer subjects were subsequently employed in the algorithm validation phase of this study. Similar physiological and driving performance measures were collected during both phases of the study. All subjects were from the same driver population. All subjects were run under similar conditions as those in the algorithm development phase. Algorithms that appeared promising which were developed in the first phase of study were validated by applying them to the new data in an attempt to predict drowsiness on a new subject pool. It was found that drowsiness could be detected on a new subject pool and that the rate of correct predictions was quite high. There was no general decrease in predictive power of the drowsiness detection algorithms when applied to new data. Results showed that an accuracy rate of over 90 percent could be accomplished when output from the detection algorithms were classified into categories of "Awake," "Questionable," and "Drowsy." / Ph. D.

This study examined the parameters under which risk compensation in driving can occur due to the use of safety belts. Risk compensation theories hypothesize that if individuals wear safety belts, they will . drive in a more risky manner than if they do not wear safety belts due to the increased perception of safety they provide. Although much of the current literature has debated the existence of risk compensation in driving for many years, until the current study an experimental analysis of the effect has not yet been conducted that permits a controlled examination of both between-subject and within-subject effects. Risk compensation was not found in the between-subject analyses of the present research, however the within-subject analyses demonstrated the risk compensation effect. Subjects drove significantly faster when they switched from not wearing a safety belt to wearing a safety belt than subjects who either did not switch belt use or drivers who switched from safety belt use to safety belt non-use. The study also suggested that the mechanism by which risk compensation occurs is that safety belt use makes drivers feel safer when they can compare the sensations wearing a safety belt vs. those when not wearing a safety belt. The risk compensation effect probably did not manifest itself in the between-subject studies because this comparison did not (and could not) take place. The implications of this study to driving real automobiles on multi-user roadways is discussed. Suggestions and examples of possible research to further expand the knowledge about how and when risk compensation occurs are also provided. / Ph. D. / incomplete_metadata

The Type-II dilemma zone refers to the segment of roadway approaching an intersection where drivers have difficulty deciding to stop or proceed through at the onset of the circular yellow (CY) indication. Signalized intersection safety can be improved when the dilemma zone is correctly identified and steps are taken to reduce the likelihood that vehicles are caught in it. This research employs driving simulation as a means to collect driver response data at the onset of the CY indication to better understand and describe the dilemma zone. The data obtained was compared against that from previous experiments documented in the literature and the evidence suggests that driving simulator data is valid for describing driver behavior under the given conditions. Fuzzy logic was proposed as a tool to model driver behavior in the dilemma zone, and three such models were developed to describe driver behavior as it relates to the speed and position of the vehicle. These models were shown to be consistent with previous research on this subject and were able to predict driver behavior with up to 90% accuracy. / Graduation date: 2013

Diss. Tampereen teknillinen korkeakoulu, 2006. / Myös verkkojulkaisuna.

Modern vehicles are designed to protect occupants in the event of a crash with some vehicles better at this than others. However, passenger protection during an accident has shown to be not enough in many high impact crashes. Statistics have shown that the human error is the number one contributor to road accidents. This research study explores how driver error can be reduced through technology which observes driver behaviour and reacts when certain unwanted patterns in behaviour have been detected. Finally a system that detects driver fatigue and driver distraction has been developed using non-invasive machine vision concepts to monitor observable driver behaviour. / Electrical Engineering / M. Tech. (Electrical Engineering)