Modeling of low illuminance road lighting condition using road temporal profile
Pedestrian Automatic Emergency Braking (PAEB) system for avoiding/mitigating pedestrian crashes have been equipped on some passenger vehicles. At present, there are many efforts 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 addressing 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 facilities at night, in which 70% pedestrian crash social costs occurs. The test facility should include representative low-illuminance environment to enable the examination of sensing and control functions of different PAEB systems. This thesis work focuses on modeling low-illuminance driving environment and describes an approach to reconstruct 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 determine possible recreation of such environment for PAEB evaluation. This research is conducted in five steps. The first step is to identify lighting components that appear frequently on a low-illuminance environment that affect the performance of the PAEB. The identified lighting components include ambient light, same side/opposite side light poles, opposite side car headlight. Next step is to collect all potential pedestrian collision cases at night with GPS coordinate information from TASI 110 CAR naturalistic driving study video database. Thirdly, since ambient lighting is relatively xi random and lack of a certain pattern, ambient light intensity for each potential collision case is defined 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 profile method, which compressing region of interest in video data (x,y,t) to image data (x,y), is introduced to scan certain predefined 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 specific light sources can be recognized and classified. All light sources obtained are stamped with GPS coordinates and time information which are provided in corresponding data files along with the video. Lastly, by grouping all light source information of each representative 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.
Chien, Purdue University.
Automotive engineering|Electrical engineering
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