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The accident reconstruction community relies on photogrammetry for taking measurements from photographs. Camera matching, a close-range photogrammetry method, is a particularly useful tool for locating accident scene evidence after time has passed and the evidence is no longer physically visible. In this method, objects within the accident scene that have remained unchanged are used as a reference for locating evidence that is no longer physically available at the scene such as tire marks, gouge marks, and vehicle points of rest. Roadway lines, edges of pavement, sidewalks, signs, posts, buildings, and other structures are recognizable scene features that if unchanged between the time of accident and time of analysis are beneficial to the photogrammetric process. In instances where these scene features are limited or do not exist, achieving accurate photogrammetric solutions can be challenging.

All camera lenses contain optical aberrations as a result of the design and manufacturing processes. Lens aberrations cause distortion of the resulting image captured on film or a sensor. This distortion is inherent in all lenses because of the shape required to project the image onto film or a sensor, the materials that make up the lens, and the configuration of lenses to achieve varying focal lengths and other photographic effects. The distortion associated with lenses can cause errors to be introduced when photogrammetric techniques are used to analyze photographs of accidents scenes to determine position, scale, length and other characteristics of evidence in a photograph. This paper evaluates how lens distortion can affect images, and how photogrammetrically measuring a distorted image can result in measurement errors.

This paper presents a methodology for generating photo realistic computer simulation environments of nighttime driving scenarios by combining nighttime photography and videography with video tracking [1] and projection mapping [2] technologies. Nighttime driving environments contain complex lighting conditions such as forward and signal lighting systems of vehicles, street lighting, and retro reflective markers and signage. The high dynamic range of nighttime lighting conditions make modeling of these systems difficult to render realistically through computer generated techniques alone. Photography and video, especially when using high dynamic range imaging, can produce realistic representations of the lighting environments. But because the video is only two dimensional, and lacks the flexibility of a three dimensional computer generated environment, the scenarios that can be represented are limited to the specific scenario recorded with video.

This paper presents a methodology for generating video realistic computer simulated rain, and the effect rain has on driver visibility. Rain was considered under three different rain rates, light, moderate and heavy, and in nighttime and daytime conditions. The techniques and methodologies presented in this publication rely on techniques of video tracking and projection mapping that have been previous published. Neale et al. [2004, 2016], showed how processes of video tracking can convert two-dimensional image data from video images into three-dimensional scaled computer-generated environments. Further, Neale et al. [2013,2016] demonstrated that video projection mapping, when combined with video tracking, enables the production of video realistic simulated environments, where videographic and photographic baseline footage is combined with three-dimensional computer geometry.

The accident reconstruction community has previously relied upon photographs and site visits to recreate a scene. This method is difficult in instances where the site has changed or is not accessible. In 2017 the United States Geological Survey (USGS) released historical 3D point clouds (LiDAR) allowing for access to digital 3D data without visiting the site. This offers many unique benefits to the reconstruction community including: safety, budget, time, and historical preservation. This paper presents a methodology for collecting this data and using it in conjunction with aerial imagery, and camera matching photogrammetry to create 3D computer models of the scene without a site visit.

In 1980, research by Thebert introduced the use of photography equipment and transparencies for onsite reverse camera projection photogrammetry [1]. This method involved taking a film photograph through the development process and creating a reduced size transparency to insert into the cameras viewfinder. The photographer was then able to see both the image contained on the transparency, as well as the actual scene directly through the cameras viewfinder. By properly matching the physical orientation and positioning of the camera it was possible to visually align the image on the image on the transparency to the physical world as viewed through the camera. The result was a solution for where the original camera would have been located when the photograph was taken. With the original camera reverse-located, any evidence in the transparency that is no longer present at the site could then be replaced to match the evidences location in the transparency.

Tire tread separation events, a category of tire disablements, can be sub-categorized into two main types of separations. These include full tread separations, in which the tread around the entire circumference of the tire separates from the tire carcass, and partial tread separations, in which a portion of the tread separates and the flap remains attached to the tire for an extended period of time. In either case, the tire can remain inflated or lose air. Relatively, there have been few partial tire tread separation tests presented in the literature compared to full tread separation tests. In this study, the results of 25 full and partial tire tread separation tests, conducted with a variety of vehicles at highway speeds, are reported. Cases in which the tire remains inflated and loses air pressure are both considered. The testing was performed on a straight section of road and primarily focused on rear tire disablements.

This paper examines a method for generating a scaled three-dimensional computer model of an accident scene from video footage. This method, which combines the previously published methods of video tracking and camera projection, includes automated mapping of physical evidence through rectification of each frame. Video Tracking is a photogrammetric technique for obtaining three-dimensional data from a scene using video and was described in a 2004 publication titled, “A Video Tracking Photogrammetry Technique to Survey Roadways for Accident Reconstruction” (SAE 2004-01-1221).

When the visibility of an object or person in the roadway from a driver’s perspective is an issue, the potential effect of moonlight is sometimes questioned. To assess this potential effect, methods typically used to quantify visibility were performed during conditions with no moon and with a full moon. In the full moon condition, measurements were collected from initial moon rise until the moon reached peak azimuth. Baseline ambient light measurements of illumination at the test surface were measured in both no moon and full moon scenarios. Additionally, a vehicle with activated low beam headlamps was positioned in the testing area and the change in illumination at two locations forward of the vehicle was recorded at thirty-minute intervals as the moon rose to the highest position in the sky. Also, two separate luminance readings were recorded during the test intervals, one location 75 feet in front and to the left of the vehicle, and another 150 feet forward of the vehicle.

This paper presents a methodology for determining the position and speed of objects such as vehicles, pedestrians, or cyclists that are visible in video footage captured with only one camera. Objects are tracked in the video footage based on the change in pixels that represent the object moving. Commercially available programs such as PFTracktm and Adobe After Effectstm contain automated pixel tracking features that record the position of the pixel, over time, two dimensionally using the video’s resolution as a Cartesian coordinate system. The coordinate data of the pixel over time can then be transformed to three dimensional data by ray tracing the pixel coordinates onto three dimensional geometry of the same scene that is visible in the video footage background.

Small unmanned aerial systems have gained prominence in their use as tools for mapping the 3-dimensional characteristics of accident sites. Typically, the process of mapping an accident site involves taking a series of overlapping, high resolution photographs of the site, and using photogrammetric software to create a point cloud or mesh of the site. This process, known as image-based scanning, is explored and analyzed in this paper. A mock accident site was created that included a stopped vehicle, a bicycle, and a ladder. These objects represent items commonly found at accident sites. The accident site was then documented with several different unmanned aerial vehicles at differing altitudes, with differing flight patterns, and with different flight control software. The photographs taken with the unmanned aerial vehicles were then processed with photogrammetry software using different methods to scale and align the point clouds.

Photogrammetry and the accuracy of a photogrammetric solution is reliant on the quality of photographs and the accuracy of pixel location within the photographs. A photograph with lens distortion can create inaccuracies within a photogrammetric solution. Due to the curved nature of a camera’s lens(s), the light coming through the lens and onto the image sensor can have varying degrees of distortion. There are commercially available software titles that rely on a library of known cameras, lenses, and configurations for removing lens distortion. However, to use these software titles the camera manufacturer, model, lens and focal length must be known. This paper presents two methodologies for removing lens distortion when camera and lens specific information is not available. The first methodology uses linear objects within the photograph to determine the amount of lens distortion present. This method will be referred to as the straight-line method.

This paper presents a methodology for accurately representing dawn and dusk lighting conditions (twilight) through photographs and video recordings. Attempting to generate calibrated photographs and video during twilight conditions can be difficult, since the time available to capture the light changes rapidly over time. In contrast, during nighttime conditions, when the sun is no longer contributing light directly or indirectly through the sky dome, matching a specific time of night is not as relevant, as man-made lights are the dominate source of illumination. Thus, the initial setup, calibration and collection of calibrated video, when it is dark, is not under a time constraint, but during twilight conditions the time frame may be narrow. This paper applies existing methods for capturing calibrated footage at night but develops a method for adjusting the footage in the event matching an exact time during twilight is necessary.

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.

Video and photo based photogrammetry software has many applications in the accident reconstruction community including documentation of vehicles and scene evidence. Photogrammetry software has developed in its ease of use, cost, and effectiveness in determining three dimensional data points from two dimensional photographs. Contemporary photogrammetry software packages offer an automated solution capable of generating dense point clouds with millions of 3D data points from multiple images. While alternative modern documentation methods exist, including LiDAR technologies such as 3D scanning, which provide the ability to collect millions of highly accurate points in just a few minutes, the appeal of automated photogrammetry software as a tool for collecting dimensional data is the minimal equipment, equipment costs and ease of use.

Previous work demonstrated that the orientation of tire mark striations can be used to infer the braking actions of the driver [1]. An equation that related tire mark striation angle to longitudinal tire slip, the mathematical definition of braking, was presented. This equation can be used to quantify the driver’s braking input based on the physical evidence. Braking input levels will affect the speed of a yawing vehicle and quantifying the amount of braking can increase the accuracy of a speed analysis. When using this technique in practice, it is helpful to understand the sensitivity and uncertainties of the equation. The sensitivity and uncertainty of the equation are explored and presented in this study. The results help to formulate guidelines for the practical application of the method and expected accuracy under specified conditions. A case study is included that demonstrates the analysis of tire mark striations deposited during a real-world accident.

Forensic disciplines are called upon to locate evidence from a single camera or static video camera, and both the angle of incidence and resolution can limit the accuracy of single image photogrammetry. This research compares a baseline of known 3D data points representing evidence locations to evidence locations determined through single image photogrammetry and evaluates the effect that object resolution (measured in pixels), and angle of incidence has on accuracy. Solutions achieved using an automated process where a camera match alignment is calculated from common points in the 2D imagery and the 3D environment, were compared to solutions achieved in a more manual method by iteratively adjusting the camera’s position, orientation, and field-of-view until an alignment is achieved. This research independently utilizes both methods to achieve photogrammetry solutions and to locate objects within a 3D environment.

In 2016, Virtual Reality (VR) equipment entered the mainstream scientific, medical, and entertainment industries. It became both affordable and available to the public market in the form of some of the technologies earliest successful headset: the Oculus Rift™ and HTC Vive™. While new equipment continues to emerge, at the time these headsets came equipped with a 100° field of view screen that allows a viewer a seamless 360° environment to experience that is non-linear in the sense that the viewer can chose where they look and for how long. The fundamental differences, however, between the conventional form of visualizations like computer animations and graphics and VR are subtle. A VR environment can be understood as a series of two-dimensional images, stitched together to be a seamless single 360° image. In this respect, it is only the number of images the viewer sees at one time that separates a conventional visualization from a VR experience.

This paper introduces a method for calculating vehicle speed and uncertainty range in speed from video footage. The method considers uncertainty in two areas; the uncertainty in locating the vehicle’s position and the uncertainty in time interval between them. An abacus style timing light was built to determine the frame time and uncertainty of time between frames of three different cameras. The first camera had a constant frame rate, the second camera had minor frame rate variability and the third had more significant frame rate variability. Video of an instrumented vehicle traveling at different, but known, speeds was recorded by all three cameras. Photogrammetry was conducted to determine a best fit for the vehicle positions. Deviation from that best fit position that still produced an acceptable range was also explored. Video metadata reported by iNPUT-ACE and Mediainfo was incorporated into the study.

PC-Crash is a vehicular accident simulation software that is widely used by the accident reconstruction community. The goal of this article is to review the prior literature that has addressed the capabilities of PC-Crash and its accuracy and reliability for various applications (planar collisions, rollovers, and human motion). In addition, this article aims to add additional analysis of the capabilities of PC-Crash for simulating planar collisions and rollovers. Simulation analysis of five planar collisions originally reported and analyzed by Bailey [2000] are reexamined. For all five of these collisions, simulations were obtained with the actual impact speeds that exhibited excellent visual agreement with the physical evidence. These simulations demonstrate that, for each case, the PC-Crash software had the ability to generate a simulation that matched the actual impact speeds and the known physical evidence.