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When a vehicle with protruding wheel studs makes contact with another vehicle or object in a sideswipe configuration, the tire sidewall, rim and wheel studs of that vehicle can deposit distinct geometrical damage patterns onto the surfaces it contacts. Prior research has demonstrated how relative speeds between the two vehicles or surfaces can be calculated through analysis of the distinct contact patterns. This paper presents a methodology for performing this analysis by visually modeling the interaction between wheel studs and various surfaces, and presents a method for automating the calculations of relative speed between vehicles. This methodology also augments prior research by demonstrating how the visual modeling and simulation of the wheel stud contact can extend to almost any surface interaction that may not have any previous prior published tests, or test methods that would be difficult to setup in real life.

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.

Accurately reconstructing the speed of a yawing and braking vehicle requires an estimate of the varying rates at which the vehicle decelerated. This paper explores the accuracy of several approaches to making this calculation. The first approach uses the Bakker-Nyborg-Pacejka (BNP) tire force model in conjunction with the Nicolas-Comstock-Brach (NCB) combined tire force equations to calculate a yawing and braking vehicle's deceleration rate. Application of this model in a crash reconstruction context will typically require the use of generic tire model parameters, and so, the research in this paper explored the accuracy of using such generic parameters. The paper then examines a simpler equation for calculating a yawing and braking vehicle's deceleration rate which was proposed by Martinez and Schlueter in a 1996 paper. It is demonstrated that this equation exhibits physically unrealistic behavior that precludes it from being used to accurately determine a vehicle's deceleration rate.

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.

Previous studies have reported and validated equations for calculating the lean angle required for a motorcycle and rider to traverse a curved path at a particular speed. In 2015, Carter, Rose, and Pentecost reported physical testing with motorcycles traversing curved paths on an oval track on a pre-marked range in a relatively level parking lot. Several trends emerged in this study. First, while theoretical lean angle equations prescribe a single lean angle for a given lateral acceleration, there was considerable scatter in the real-world lean angles employed by motorcyclists for any given lateral acceleration level. Second, the actual lean angle was nearly always greater than the theoretical lean angle. This prior study was limited in that it only examined the motorcycle lean angle at the apex of the curves. The research reported here extends the previous study by examining the accuracy of the lean angle formulas throughout the curves.

This paper presents yaw testing of vehicles with tread removed from tires at various locations. A 2004 Chevrolet Malibu and a 2003 Ford Expedition were included in the test series. The vehicles were accelerated up to speed and a large steering input was made to induce yaw. Speed at the beginning of the tire mark evidence varied between 33 mph and 73 mph. Both vehicles were instrumented to record over the ground speed, steering angle, yaw angle and in some tests, wheel speeds. The tire marks on the roadway were surveyed and photographed. The Critical Speed Formula has long been used by accident reconstructionists for estimating a vehicle’s speed at the beginning of yaw tire marks. The method has been validated by previous researchers to calculate the speed of a vehicle with four intact tires. This research extends the Critical Speed Formula to include yawing vehicles following a tread detachment event.

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.

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.

This paper presents analyses of 21real-world pedestrian versus vehicle collisions that were video recorded from vehicle dash mounted cameras or surveillance cameras. These pedestrian collisions have in common an impact configuration where the pedestrian was at the side of the vehicle, or with a minimal overlap at the front corner of the vehicle (less than one foot overlap). These impacts would not be considered frontal impacts [1], and as a result determining the speed of the vehicle by existing methods that incorporate the pedestrian travel distance post impact, or by assessing vehicle damage, would not be applicable. This research examined the specific interaction of non-frontal, side-impact, and minimal overlap pedestrian impact configurations to assess the relationship between the speed of the vehicle at impact, the motion of the pedestrian before and after impact, and the associated post impact travel distances.