Search Results

In the 2016 SAE publication “Data Acquisition using Smart Phone Applications,” Neale et al., evaluated the accuracy of basic fitness applications in tracking position and elevation using the GPS and accelerometer technology contained within the smart phone itself [1]. This paper further develops the research by evaluating mid-level applications. Mid-level applications are defined as ones that use a phone’s internal accelerometer and record data at 1 Hz or greater. The application can also utilize add-on devices, such as a Bluetooth enabled GPS antenna, which reports at a higher sample rate (10 Hz) than the phone by itself. These mid-level applications are still relatively easy to use, lightweight and affordable [2], [3], [4], but have the potential for higher data sample rates for the accelerometer (due to the software) and GPS signal (due to the hardware). In this paper, Harry’s Lap Timer™ was evaluated as a smart phone mid-level application.

Accident reconstructionists routinely rely on computer software to perform analyses. While there are a variety of software packages available to accident reconstructionists, many rely on custom spreadsheet-based applications for their analyses. Purchased packages provide an improved interface and the ability to produce sophisticated animations of vehicle motion but can be cost prohibitive. Pycrash is a free, open-source Python-based software package that, in its current state, can perform basic accident reconstruction calculations, automate data analyses, simulate single vehicle motion and, perform impulse-momentum based analyses of vehicle collisions. In this paper, the current capabilities of Pycrash are illustrated and its accuracy is assessed using matching PC-Crash simulations performed using PC-Crash.

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).

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.

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.

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.