Thesis (Ph.D.) - Carleton University, 2005. / Includes bibliographical references (p. 154-166). Also available in electronic format on the Internet.

Thesis (M.S.) in Civil Engineering--University of Maine, 2008. / Includes vita. Includes bibliographical references (leaves 86-87).

Thesis (M. Sc.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 131-133).

Thesis (M.Eng.Sc.) - University of Queensland, 2003. / Includes bibliography.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 128-130). Also available on the Internet.

Thesis (M.S.)--University of Florida, 2001. / Title from title page of source document. Document formatted into pages; contains x, 85 p.; also contains graphics. Includes vita. Includes bibliographical references.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 128-130). Also available on the Internet.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 107-112). Also available via the Internet.

Indiana University-Purdue University Indianapolis (IUPUI) / Research on robust Pre-Collision Systems (PCS) requires new techniques that will allow a better understanding of the vehicle-pedestrian dynamic relationship, and which can predict pedestrian future movements. Our research analyzed videos from the Transportation Active Safety Institute (TASI) 110-Car naturalistic driving dataset to extract two dynamic pedestrian semantic features. The dataset consists of videos recorded with forward facing cameras from 110 cars over a year in all weather and illumination conditions. This research focuses on the potential-conflict situations where a collision may happen if no avoidance action is taken from driver or pedestrian. We have used 1000 such 15 seconds videos to find vehicle-pedestrian relative dynamic trajectories and pose of pedestrians. Adaptive structural local appearance model and particle filter methods have been implemented and modified to track the pedestrians more accurately. We have developed new algorithm to compute Focus of Expansion (FoE) automatically. Automatically detected FoE height data have a correlation of 0.98 with the carefully clicked human data. We have obtained correct tractography results for over 82% of the videos. For pose estimation, we have used flexible mixture model for capturing co-occurrence between pedestrian body segments. Based on existing single-frame human pose estimation model, we have introduced Kalman filtering and temporal movement reduction techniques to make stable stick-figure videos of the pedestrian dynamic motion. We were able to reduce frame to frame pixel offset by 86% compared to the single frame method. These tractographs and pose estimation data were used as features to train a neural network for classifying ‘potential conflict’ and ‘no potential conflict’ situations. The training of the network achieved 91.2% true label accuracy, and 8.8% false level accuracy. Finally, the trained network was used to assess the probability of collision over time for the 15 seconds videos which generates a spike when there is a ‘potential conflict’ situation. We have also tested our method with TASI mannequin crash data. With the crash data we were able to get a danger spike for 70% of the videos. The research enables new analysis on potential-conflict pedestrian cases with 2D tractography data and stick-figure pose representation of pedestrians, which provides significant insight on the vehicle-pedestrian dynamics that are critical for safe autonomous driving and transportation safety innovations.

One of today's most serious social, economical and environmental problems is traffic congestion. In addition to the financial cost of the problem, the number of traffic related injuries and casualties is very high. A recently considered approach to increase safety while reducing congestion and improving driving conditions is Automated Highway Systems (AHS). The AHS will evolve from the present highway system to an intelligent vehicle/highway system that will incorporate communication, vehicle control and traffic management techniques to provide safe, fast and more efficient surface transportation. A key factor in AHS deployment is intelligent vehicle control. While the technology to safely maneuver the vehicles exists, the problem of making intelligent decisions to improve a single vehicle's travel time and safety while optimizing the overall traffic flow is still a stumbling block. We propose an artificial intelligence technique called stochastic learning automata to design an intelligent vehicle path controller. Using the information obtained by on-board sensors and local communication modules, two automata are capable of learning the best possible (lateral and longitudinal) actions to avoid collisions. This learning method is capable of adapting to the automata environment resulting from unmodeled physical environment. Simulations for simultaneous lateral and longitudinal control of an autonomous vehicle provide encouraging results. Although the learning approach taken is capable of providing a safe decision, optimization of the overall traffic flow is also possible by studying the interaction of the vehicles. The design of the adaptive vehicle path planner based on local information is then carried onto the interaction of multiple intelligent vehicles. By analyzing the situations consisting of conflicting desired vehicle paths, we extend our design by additional decision structures. The analysis of the situations and the design of the additional structures are made possible by the study of the interacting reward-penalty mechanisms in individual vehicles. The definition of the physical environment of a vehicle as a series of discrete state transitions associated with a "stationary automata environment" is the key to this analysis and to the design of the intelligent vehicle path controller. This work was supported in part by the Center for Transportation Research and Virginia DOT under Smart Road project, by General Motors ITS Fellowship program, and by Naval Research Laboratory under grant no. N000114-93-1-G022. / Ph. D.