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Photogrammetry is a commonly used and accepted technique within the field of accident reconstruction for taking measurements from photographs. Previous work has shown the accuracy of optimized close-range photogrammetry techniques to be within 2 mm compared to other high accuracy measurement techniques when using a known calibrated camera. This research focuses on the use of inverse camera close-range photogrammetry, where photographs from an unknown camera are used to model a vehicle. Photogrammetry is a measurement technique that utilizes triangulation to take measurements from photographs. The measurements are dependent on the geometry of the camera, such as the sensor size, focal length, lens type, etc. Three types of cameras were tested for accuracy; a high-end commercial camera, a point and shoot camera, and a cell phone camera.

Several GoPro camera models contain Global Positioning System (GPS), accelerometer, and gyroscope instrumentation and are capable of measuring and recording position, velocity, acceleration, and inertial data. This study evaluates the accuracy of data obtained from GoPro cameras through a series of controlled tests. A test vehicle was instrumented with a Racelogic VBOX data acquisition unit as well as various generations of GoPro camera units equipped with GPS capability and driven on a road course. The raw data collected with the GoPro cameras and the translations of this data provided by the GoPro Quik desktop software application were compared to data collected with the validated VBOX data acquisition unit. The results demonstrated that position, velocity, and acceleration data recorded with GoPro cameras is consistent with VBOX data and is useful for applications related to accident reconstruction.

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.