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Prior research has tested the validity of Cycles Engine render in Blender for the creation of physically correct lighting models; however, a research gap still exists in examining the use of Arnold render engine in 3DS Max for accident reconstruction and other forensic settings [1]. Specifically, the process presented in this paper utilizes the Arnold render engine within 3DS-Max to analyze the lighting models. Arnold is a physically-based render (PBR) engine and can be used to recreate an accident scene geometry and lighting conditions. The goal is to create light sources within Arnold that represent the real-world light sources. The light sources in Arnold are quantified by several variables, including intensity, color, and size. The intensity and size variables determine the self-emitted radiance of the light source and require further explanation to determine the relationship between these variables in Arnold and real-world lighting quantities.

Numerous studies have validated SIMON and DyMESH with respect to vehicle dynamics and crash analysis for accident reconstruction. The impetus for this paper is to develop an accessible methodology for calculating three-dimensional stiffness coefficients for HVE-SIMON and DyMESH. This method uses acceleration-time data (crash pulse) from a vehicle crash test, data that is widely available through the National Highway Traffic Safety Administration (NHTSA). The crash pulse, along with vehicle mass and impact speed, are used to calculate the force acting on the vehicle and the associated vehicle deflection time history. A technique for determining the area-deflection function is created from a computer model of the vehicle, HVE-SIMON, and basic photo-editing software. The calculated force divided by the associated area function (F/A) is plotted versus deflection and a third-order polynomial is then fit to the curve.

Vision is the primary sense used to navigate through this world when driving, walking, biking, or performing most tasks. and thus visibility is a critical concern in the design of roadways, pathways, vehicles, buildings, etc. and the investigation of accidents. In order to assess visibility, the accident scene can be documented under similar conditions. Geometric and photometric measurements can be taken for later analysis. Calibrated photographs or video of a recreated scene can be captured to illustrate the visibility at a later time. This process can often require significant coordination of the physical features at the scene. It can be difficult to precisely control the motion and timing of moving features such as pedestrians and vehicles. The result is fixed in that you capture specific scenarios with specific conditions with the selected field of view and perspective of the cameras used.

Mapping the luminance values of a visual scene is of broad interest to accident reconstructionists, human factors professionals, and lighting experts. Such mappings are useful for a variety of purposes, including determining the effectiveness and appropriateness of lighting installations, and performing visibility analyses for accident case studies. Previous work has shown that pixel intensity captured by consumer-grade digital still cameras can be calibrated to estimate luminance [1-7]. Taking a digital still image and converting this image into a luminance map even further reduces the time required for luminance measurement. Suway and Suway previously presented a methodology for estimating luminance from digital images and video of a scene [1]. In this paper, the authors update this methodology for calculating luminance from a digital camera.

Laser scanners are typically used in vehicle accident reconstruction and forensic applications to measure roadway and vehicle details. However, laser scanners used near congested roadways can digitize unwanted passing vehicles, which produces a scan with noisy and poor image quality point clouds. On the other hand, small Unmanned Aircraft System (sUAS) images of reflective objects may result in a less accurate mesh, and capturing vertical surfaces such as telephone poles, traffic lights, and building faces is more difficult. Prior research has tested the accuracy of sUAS-captured images processed with commercially available software, such as AgiSoft or Pix4D, as well as in comparison to the accuracy of laser scan data. Research still has yet to be conducted on combining the laser scans and sUAS images for use in accident reconstruction and other forensic settings.

Accident reconstruction specialists have long relied on post-crash deformation and energy equivalence calculations to determine impact severity and the experienced change in velocity during the impact event. In order to utilize post-crash deformation, information must be known about the vehicle's structure and its ability to absorb crash energy. The Federal Motor Vehicle Safety Standards (FMVSS), the New Car Assessment Program (NCAP), and the Insurance Institute of Highway Safety (IIHS), have created databases with crash testing data for a wide range of vehicles. These crash tests allow reconstruction specialists to determine a specific vehicle's ability to absorb energy as well as to generalize the energy absorption characteristics across vehicle classes. These methods are very well publicized.

Mapping a light source, any light source, is of broad interest to accident reconstructionists, human factors professionals and lighting experts. Such mappings are useful for a variety of purposes, including determining the effectiveness and appropriateness of lighting installations, and performing visibility analyses for accident case studies. Currently, mapping a light source can be achieved with several different methods. One such method is to use an illuminance meter and physically measure each point of interest on the roadway. Another method utilizes a goniometer to measure the luminous intensity distribution, this is a near-field measurement. Both methods require significant time and the goniometric method requires extensive equipment in a lab. A third method measures illumination distribution in the far-field using a colorimeter or photometer.

Video from dash or surveillance cameras is sometimes used in vehicle accident reconstruction to analyze the speeds of vehicles. However, video captured during nighttime, during poor visibility conditions, or of events out of frame may not always visually capture details needed to determine the speed of the vehicle in question. Prior research has determined speed from vehicle acoustic signals, but little research has analyzed the audio portion of dash camera video for use in accident reconstruction and other forensic settings. The purpose of this study was to outline and test the validity of a method for using the audio portion of dash camera video to determine vehicle speed. Extracting the audio portion from the video recording and further processing it with commercially available software can allow the calculation of vehicle speed and acceleration when traveling over roadway surfaces and detection of turn signal activations while driving.