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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.

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.

Rising electric scooter popularity has seen a surge in electric scooter crashes. Crash reconstructionists increasingly have access to global positioning system (GPS) data for micromobile vehicle trips, and GPS devices can produce a wealth of data about cyclists’, scooterists’, and other riders’ road paths and route usage. However, prior research has demonstrated that GPS positional accuracy is less reliable for more nuanced roadway positioning, such as which lane a vehicle occupies, as well as within-lane movements, such as acceleration and deceleration⁠. This limitation presents a challenge for crash reconstructionists that may have access to GPS data and require second-by-second positional accuracy to determine such nuanced maneuvers and vehicle positioning in their analysis. The purpose of this study was to explore the positional accuracy of five GPS units for a micromobile vehicle during three different ride conditions: acceleration, deceleration, and constant speed.

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.

Bicycle riders increasingly utilize bicycle computers or cell phones equipped with GPS positioning to track and share their rides. These bicycle computers and online platforms typically contain a wealth of ride-specific information, such as speed, location, heart rate, pedaling cadence, and more. This information can be essential to forensic experts attempting to analyze bicycle movements in order to reconstruct bicycle crashes. However, there has been little research specifically discussing the positional accuracy of GPS systems for bicycles to date. The purpose of this study is to examine the positional accuracy of several GPS units in real-world environments, including rural landscape and dense urban settings. The location data from portable GPS devices were compared to reference data recorded by a Racelogic VBox 3i with Real Time Kinematic (RTK).

It is important for large trailers to be outlined with retroreflective tape to make them more conspicuous in roadway environments with diminished ambient lighting. Retroreflective material is also utilized on signs as well as clothing to improve their conspicuity. As used conspicuity tape does not perform at the same level as clean and new tape. Hence, there is a need for visibility testing of retroreflective materials with degraded or reduced effectiveness. In an effort to control the coefficient of retroreflection (RA). A methodology that uniformly obscured parts of the retroreflective materials was developed. Validation testing of this procedure was conducted using glass bead sheeting, as well as microscopic prismatic sheeting. The results from the study showed that, by uniformly obscuring parts of the tape, RA is approximately a linear function of the area exposed to the viewer. Thus, the overall perceived brightness and coefficient of retroreflection readings were reduced.

Modeling retroreflective material in a three-dimensional computer modeling and Physically Based Rendering (PBR) engine is extremely difficult without fully understanding the physics behind how the light rays interact and behave with the retroreflective materials. Without the proper engineering and physics understanding, incorrect, inaccurate and unfair animations can be created that attempt to show the visibility of retroreflective materials. In this paper we will discuss the engineering and physics of retroreflective materials and how light interacts with these materials. We will also describe how to incorporate the engineering and science of retroreflective materials into a PBR engine to create a fair and accurate light simulation displaying the visibility of the retroreflective materials.

Retroreflection occurs when a light ray incident on a surface is reflected back towards the light source. The performance of a retroreflective material is of interest to accident reconstructionist, human factors professionals, lighting professionals, and roadway design professionals. The retroreflective effect of a material can be defined by the coefficient of retroreflection, which is a function of the light’s entrance angle and the viewer’s observation angle. The coefficient of retroreflection of a material is typically measured in a laboratory environment or in the field with a retroreflectometer. Often the material in question cannot be taken to a laboratory for testing and commercially available portable retroreflectometers are limited to entrance angles of 45 degrees or less and may be cost prohibitive in some cases.

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.

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.

Accident reconstructionists will typically document scenes, evidence, vehicles or objects of interest by using 3-dimensional laser scanners. These techniques are well documented, utilized and can be extremely accurate. However, when the subject of documentation involves surfaces that include intricate, highly reflective, and/or complex geometry (motorcycles, wheelchairs, stairs, etc.) the commercially available laser scanners can produce obscuring dense stray and scattered points which results in point clouds that could require tedious manual registration and/or optimization. This paper compares a FARO Focus laser scanner, Pix4DMapper, and Agisoft’s Photoscan point cloud data to FARO ARM measurements of vehicles, other transportation devices and architectural features. It was shown that the Pix4DMapper and Agisoft’s Photoscan point cloud data resulted in detailed and accurate point cloud data compared to the FARO ARM measurements.

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. One of the most common methods for mapping luminance is to use a spot type luminance meter. This requires individual measurements of all objects of interest and can be extremely time consuming. Luminance cameras can also be used to create a luminance map. While luminance cameras will map a scene’s luminance values more quickly than a spot luminance meter, commercially available luminance cameras typically require long capture times during low illuminance (up to 30 seconds). Previous work has shown that pixel intensity captured by consumer-grade digital still cameras can be calibrated to measure luminance.

Collision statistics show that more than half of all pedestrian fatalities caused by vehicles occur at night. The recognition of objects at night is a crucial component in driver responses and in preventing nighttime pedestrian accidents. To investigate the root cause of this fact pattern, Richard Blackwell conducted a series of experiments in the 1950s through 1970s to evaluate whether restricted viewing time can be used as a surrogate for the imperfect information available to drivers at night. The authors build on these findings and incorporate the responses of drivers to objects in the road at night found in the SHRP-2 naturalistic database. A closed road outdoor study and an indoor study were conducted using an automatic shutter system to limit observation time to approximately ¼ of a second. Results from these limited exposure time studies showed a positive correlation to naturalistic responses, providing a validation of the time-limited exposure technique.

It is extremely important to accurately depict photographs or video taken of a scene at night, when attempting to show how the subject scene appeared. It is widely understood that digital image sensors cannot capture the large dynamic range that can be seen by the human eye. Furthermore, todays commercially available printers, computer monitors, TV’s or other displays cannot reproduce the dynamic range that is captured by the digital cameras. Therefore, care must be taken when presenting a photograph or video while attempting to accurately depict a subject scene. However, there are many parameters that can be altered, while taking a photograph or video, to make a subject scene either too bright or too dark. Similarly, adjustments can be made to a printer or display to make the image appear either too bright or too dark. There have been several published papers and studies dealing with how to properly capture and calibrate photographs and video of a subject scene at night.

Federal Motor Vehicle Safety Standard (FMVSS) No. 108 has minimum performance requirements for retroreflective tape at different entrance and observation angles. In the author's preliminary research, all DOT-C2 retroreflective tape on the market is advertised as meeting and exceeding FMVSS No. 108 requirements. The authors' literature review revealed that there have been no publications quantifying the performance of commercially available DOT-C2 retroreflective tape across a wide range of entrance and observation angles. Therefore, without additional study, an accident reconstruction expert cannot know exactly how a specific type of compliant tape may perform, beyond the minimum federal requirements. In an attempt to solve this issue, the authors have quantified the performance of different types of retroreflective tape with a retroreflectometer.

Accident reconstruction experts are often asked to evaluate the visibility and conspicuity of objects in the roadway. It is common for objects placed in or along the roadway, vehicles, and required by Federal Motor Vehicle Safety Standard (FMVSS) No. 108 for certain vehicles and trailers, to have red and white DOT-C2 retroreflective tape installed on several locations. Retroreflective tape is designed to reflect light back towards the light source at the same entrance angle. The authors' literature review revealed that there have been no publications quantifying the performance of commercially available DOT-C2 retroreflective tape with real world vehicles. Therefore, without additional study, an accident reconstruction expert cannot know exactly how a specific type of compliant tape would perform beyond the minimum federal requirements. In the current research, the performance of white and red DOT-C2 retroreflective tape is quantified.

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.

Bobtail testing data published in the literature are limited and the difference in deceleration of a bobtail configuration compared to a tractor-trailer has not been fully evaluated in the past. The authors seek to increment and update previous research on the topic. This paper presents detailed braking characteristic information obtained from full scale instrumented testing of a bobtail vehicle at various speeds. Brake timing is analyzed for the tested condition to determine the overall braking characteristics. The findings of this study are compared to 1) other testing performed with the same tractor configured with a trailer at different loading conditions and 2) to results published in literature for both bobtail vehicles and other loading conditions for both 6×4 and 4×2 tractor axle configurations.

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.