Modeling of Low Illuminance Road Lighting Condition Using Road Temporal Profile

Indiana University-Purdue University Indianapolis (IUPUI)%%%%Pedestrian Automatic Emergency Braking (PAEB) system for avoiding/mitigatingpedestrian crashes have been equipped on some passenger vehicles. At present,there are many e orts for the development of common standard for the performanceevaluation of PAEB. The Transportation Active Safety Institute (TASI) at IndianaUniversity-Purdue University-Indianapolis has been studying the problems and ad-dressing the concerns related to the establishment of such a standard with supportfrom Toyota Collaborative Safety Research Center (CSRC). One of the importantcomponents 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 facilityshould include representative low-illuminance environment to enable the examinationof sensing and control functions of di erent PAEB systems. This thesis work focuseson modeling low-illuminance driving environment and describes an approach to recon-struct the lighting conditions. The goal of this research is to characterize and modellight sources at a potential collision case at low-illuminance environment and deter-mine possible recreation of such environment for PAEB evaluation. This research isconducted 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 thePAEB. The identi ed lighting components include ambient light, same side/oppositeside 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 CARnaturalistic 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 allvideo frames in this case. Fourth step is to classify interested light sources from theselected videos. The temporal pro le method, which compressing region of interestin video data (x,y,t) to image data (x,y), is introduced to scan certain prede nedregion on the video. Due to the fact that light sources (except ambient light) imposedistinct light patterns on the road, image patterns corresponding to speci c lightsources can be recognized and classi ed. All light sources obtained are stamped withGPS coordinates and time information which are provided in corresponding data lesalong with the video. Lastly, by grouping all light source information of each repre-sentative street category, representative light description of each street category canbe generated. Such light description can be used for lighting construction of PAEBtest facility.