Call number: LD2668 .T4 1986 G35 / Master of Science / Industrial and Manufacturing Systems Engineering

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Chan, Kai Chi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 91-98). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Motivation --- p.4 / Chapter 1.3 --- Literature Review --- p.7 / Chapter 1.3.1 --- Headlamp Preference Investigation --- p.7 / Chapter 1.3.2 --- Vehicle Dynamic System --- p.10 / Chapter 1.3.3 --- Inertial Navigation Systems --- p.11 / Chapter 1.4 --- Objective --- p.12 / Chapter 1.5 --- A Sensor Based Method --- p.14 / Chapter 1.5.1 --- Accelerometer --- p.14 / Chapter 1.5.2 --- Lighting --- p.16 / Chapter 1.5.3 --- System Design --- p.19 / Chapter 1.6 --- Thesis Organization --- p.20 / Chapter 1.7 --- Achievement and Contributions --- p.21 / Chapter 2 --- Methodology --- p.23 / Chapter 2.1 --- Kinematics of a Turning Car --- p.24 / Chapter 2.2 --- Headlamp Direction Prediction --- p.27 / Chapter 2.2.1 --- Steering Wheel Angle Measurement --- p.28 / Chapter 2.2.2 --- Steering Wheel Angle Stabilization --- p.31 / Chapter 2.2.3 --- Auxiliary Headlamps Control --- p.36 / Chapter 3 --- Implementation --- p.48 / Chapter 3.1 --- Hardware Configuration --- p.49 / Chapter 3.2 --- Design Framework --- p.51 / Chapter 3.3 --- Night Drive Simulator --- p.55 / Chapter 3.3.1 --- Simulator Configuration --- p.56 / Chapter 3.3.2 --- Turning Path Prediction --- p.61 / Chapter 3.3.3 --- Auxiliary Headlamps Control --- p.63 / Chapter 4 --- Experiments --- p.65 / Chapter 4.1 --- Steering Wheel Angle Measurement --- p.66 / Chapter 4.1.1 --- Experiment Setup --- p.66 / Chapter 4.1.2 --- Evaluation Results --- p.68 / Chapter 4.2 --- Auxiliary Headlamps Prediction --- p.71 / Chapter 4.2.1 --- Simulation --- p.72 / Chapter 4.2.2 --- Test Drive --- p.76 / Chapter 5 --- Conclusions --- p.86 / Chapter 5.1 --- Summary --- p.86 / Chapter 5.2 --- Limitations and Future Works --- p.87 / Chapter 5.2.1 --- Headlamp of LEDs --- p.87 / Chapter 5.2.2 --- Simple Car Model --- p.88 / Chapter 5.2.3 --- Response Time of Filtering --- p.88 / Chapter 5.2.4 --- Test Drive --- p.89 / Chapter 6 --- Publications --- p.90 / Bibliography --- p.91

Indiana University-Purdue University Indianapolis (IUPUI) / Pedestrian Automatic Emergency Braking (PAEB) system for avoiding/mitigating pedestrian crashes have been equipped on some passenger vehicles. At present, there are many e orts for the development of common standard for the performance evaluation of PAEB. The Transportation Active Safety Institute (TASI) at Indiana University-Purdue University-Indianapolis has been studying the problems and ad- dressing the concerns related to the establishment of such a standard with support from Toyota Collaborative Safety Research Center (CSRC). One of the important components in the PAEB evaluation is the development of standard testing facili- ties at night, in which 70% pedestrian crash social costs occurs [1]. The test facility should include representative low-illuminance environment to enable the examination of sensing and control functions of di erent PAEB systems. This thesis work focuses on modeling low-illuminance driving environment and describes an approach to recon- struct the lighting conditions. The goal of this research is to characterize and model light sources at a potential collision case at low-illuminance environment and deter- mine possible recreation of such environment for PAEB evaluation. This research is conducted in ve steps. The rst step is to identify lighting components that ap- pear frequently on a low-illuminance environment that a ect the performance of the PAEB. The identi ed lighting components include ambient light, same side/opposite side light poles, opposite side car headlight. Next step is to collect all potential pedes- trian collision cases at night with GPS coordinate information from TASI 110 CAR naturalistic driving study video database. Thirdly, since ambient lighting is relatively random and lack of a certain pattern, ambient light intensity for each potential col- lision case is de ned and processed as the average value of a region of interest on all video frames in this case. Fourth step is to classify interested light sources from the selected videos. The temporal pro le method, which compressing region of interest in video data (x,y,t) to image data (x,y), is introduced to scan certain prede ned region on the video. Due to the fact that light sources (except ambient light) impose distinct light patterns on the road, image patterns corresponding to speci c light sources can be recognized and classi ed. All light sources obtained are stamped with GPS coordinates and time information which are provided in corresponding data les along with the video. Lastly, by grouping all light source information of each repre- sentative street category, representative light description of each street category can be generated. Such light description can be used for lighting construction of PAEB test facility.