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Understanding the specific illumination pattern of a vehicle headlamp beam is useful for the evaluation of nighttime visibility issues in accident reconstruction. There are several challenges associated with traditional headlamp mapping techniques, including that mapping must be conducted in darkness, the mapping location topography may not be level or flat, maintaining a measurement grid is difficult to accomplish in the field, data acquisition requires multiple individuals, and it is generally very time consuming. Traditional techniques require the surrounding surface to be permanently marked in the evening in order to allow for future data collection of the mapped points in daylight conditions. The methodology presented here alleviates some of these challenges through real-time data collection and by accounting for topographical differences, which eliminates the requirement that mapping be conducted on a level or flat surface, and it reduces the number of required participants.

Emergency personnel and first responders have the opportunity to document crash scenes while evidence is still recent. The growth of the drone market and the efficiency of documentation with drones has led to an increasing prevalence of aerial photography for incident sites. These photographs are generally of high resolution and contain valuable information including roadway evidence such as tire marks, gouge marks, debris fields, and vehicle rest positions. Being able to accurately map the captured evidence visible in the photographs is a key process in creating a scaled crash-scene diagram. Image rectification serves as a quick and straightforward method for producing a scaled diagram. This study evaluates the precision of the photo rectification process under diverse roadway geometry conditions and varying camera incidence angles.