Thesis (M.A.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 103-105). Also available in print.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 57-59). Also available on the Internet.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 268-278). Also available via the Internet.

The goal of this thesis is to implement the control and design of an intelligent vehicle based on an embedded DSP platform (eZdspTM F2812). The overall system including steering wheel AC serve motor, brake actuator, throttle driving circuit and the sensors is equipped in a golf car as a platform. Otherwise, digital image processing technology is used to realize the autonomous driving system which can achieve multi-mode of lane-keeping, lane-change and obstacle-avoidance. In the lane-keeping control, the road information can be provided by the vision system. According to the offset and displacement of angle as input signal, a fuzzy controller is used to compute the desired steering wheel angle and let the golf car can cross road safely. In lane-change, a smooth trajectory can be generated by IMU, IMU is used to collect the information data of yaw rate and yaw angle when human-driving. That makes autonomous driving system become more humanlike and to achieve an open-loop lane-change maneuver. In obstacle-avoidance, we use a laser range scanner to detect the distance of a front obstacle. When the distance is lower than safety distance, double lane-change will be activated to avoid the front obstacle. The overall system has been examined on NSYSU campus roads.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 377-389). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 328-349). Available also in a digital version from Dissertation Abstracts.

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Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.

Thesis (PhDTransportSystemsEngineering)--University of South Australia, 2003.