Thesis (M.A.) -- Central Connecticut State University, 2006. / Thesis advisor: Marc B. Goldstein. "... in partial fulfillment of the requirements for the degree of Master of Arts in Psychology." Includes bibliographical references (leaves 22-25). Also available via the World Wide Web.

Thesis (M. S.)--Psychology, Georgia Institute of Technology, 2007. / Dr. Gregory M. Corso, Committee Chair ; Dr. Arthur D. Fisk, Committee Member ; Dr. Lawrence James, Committee Member.

Thesis (M.S.)--University of Iowa, 2006. / Supervisor: Lea-Der Chen. Includes bibliographical references (leaves 114-116).

Thesis (Ph. D.)--Georgia Institute of Technology, 2006. / Includes bibliographical references (p. 177-183). Also available online via the Georgia Tech ETD Database website (http://etd.gatech.edu/).

Thesis (M.S.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 41-42). Also available on the World Wide Web.

Thesis (Ph. D.)--Wichita State University, 2005. / Includes bibliographical references (leaves 81-90). Also available online via the Wichita State University Library website (http://library.wichita.edu/).

No description available.

Thesis (M.S.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 92-95)

This investigation assessed the utility of a selection-based instructional program in teaching relations between driving behavior and driving stimuli in addition to the emergence of topography-based responding. A selection-based instructional program was delivered to three individuals with intellectual disabilities and/or learning disabilities in order to teach participants relations of sameness between automobile operation stimuli and driving behaviors. Participants were directly taught relations between video models of vehicle operation, road sign outlines, and textual stimuli of road signs using a selection-based instructional protocol delivered via a computer program. Following mastery of the selection-based instruction the emergence of selection-based responding on symmetrical and transitive posttest probes at the mastery level was observed for all 3 participants. Furthermore, movement on posttest generalization vehicle simulator probe was observed for one participant.

Road safety has become a major concern due to the increased rate of deaths caused by road accidents. For this purpose, intelligent transportation systems are being developed to reduce the number of fatalities on the road. A plethora of work has been undertaken on the detection of different styles of behaviour such as fatigue and drunken behaviour of the drivers; however, owing to complexity of human behaviour, a lot has yet to be explored in this field to assess different styles of the abnormal behaviour to make roads safer for travelling. This research focuses on detection of a very complex driver’s behaviours: ‘tafheet’, reckless and aggressive by proposing and building a driver’s behaviour detection model in the context-aware system in the VANET environment. Tafheet behaviour is very complex behaviour shown by young drivers in the Middle East, Japan and the USA. It is characterised by driving at dangerously high speeds (beyond those commonly known in aggressive behaviour) coupled with the drifting and angular movements of the wheels of the vehicle, which is similarly aggressive and reckless driving behaviour. Thus, the dynamic Bayesian Network (DBN) framework was applied to perform reasoning relating to the uncertainty associated with driver’s behaviour and to deduce the possible combinations of the driver’s behaviour based on the information gathered by the system about the foregoing factors. Based on the concept of context-awareness, a novel Tafheet driver’s behaviour detection architecture had been built in this thesis, which had been separated into three phases: sensing phase, processing and thinking phase and the acting phase. The proposed system elaborated the interactions of various components of the architecture with each other in order to detect the required outcomes from it. The implementation of this proposed system was executed using GeNIe 2.0 software, resulting in the construction of DBN model. The DBN model was evaluated by using experimental set of data in order to substantiate its functionality and accuracy in terms of detection of tafheet, reckless and aggressive behaviours in the real time manner. It was shown that the proposed system was able to detect the selected abnormal behaviours of the driver based on the contextual data collected. The novelty of this system was that it could detect the reckless, aggressive and tafheet behaviour in sequential manner, based on the intensity of the driver’s behaviour itself. In contrast to previous detection model, this research work suggested the On Board Unit architecture for the arrangement of sensors and data processing and decision making of the proposed system, which can be used to pre-infer the complex behaviour like tafheet. Thus it has the potential to prevent the road accidents from happening due to tafheet behaviour.