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Several sources report simple equations for calculating the lean angle required for a motorcycle and rider to traverse a curved path at a particular speed. These equations utilize several assumptions that reconstructionists using them should consider. First, they assume that the motorcycle is traveling a steady speed. Second, they assume that the motorcycle and its rider lean to the same lean angle. Finally, they assume that the motorcycle tires have no width, such that the portion of the tires contacting the roadway does not change or move as the motorcycle and rider lean. This study reports physical testing that the authors conducted with motorcycles traversing curved paths to examine the net effect of these assumptions on the accuracy of the basic formulas for motorcycle lean angle. We concluded that the basic lean angle formulas consistently underestimate the lean angle of the motorcycle as it traverses a particular curved path.

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.

Calculating the speed of a yawing and braked vehicle often requires an estimate of the vehicle deceleration. During a steering induced yaw, the rotational velocity of the vehicle will typically be small enough that it will not make up a significant portion of the vehicle’s energy. However, when a yaw is impact induced and the resulting yaw velocity is high, the rotational component of the vehicle’s kinetic energy can be significant relative to the translational component. In such cases, the rotational velocity can have a meaningful effect on the deceleration, since there is additional energy that needs dissipated and since the vehicle tires can travel a substantially different distance than the vehicle center of gravity. In addition to the effects of rotational energy on the deceleration, high yaw velocities can also cause steering angles to develop at the front tires. This too can affect the deceleration since it will influence the slip angles at the front tires.

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 reports a method for analyzing data from a DriveCam unit to determine impact speeds and velocity changes in vehicle-to-vehicle impacts. A DriveCam unit is an aftermarket, in-vehicle, event-triggered video and data recorder. When the unit senses accelerations over a preset threshold, an event is triggered and the unit records video from two camera views, accelerations along three directions, and the vehicle speed with a GPS sensor. In conducting the research reported in this paper, the authors ran four front-to-rear crash tests with two DriveCam equipped vehicles. For each test, the front of the bullet vehicle impacted the rear of the stationary target vehicle. Each of the test vehicles was impacted in the rear twice - once at a speed of around 10 mph and again at a speed around 25 mph. The accuracy of the DriveCam acceleration data was assessed by comparing it to the data from other in-vehicle instrumentation.

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.

Computer simulation of nighttime lighting in urban environments can be complex due to the myriad of light sources present (e.g., street lamps, building lights, signage, and vehicle headlamps). In these areas, vehicle headlamps can make a significant contribution to the lighting environment 1 , 2 . This contribution may need to be incorporated into a lighting simulation to accurately calculate overall light levels and to represent how the light affects the experience and quality of the environment. Within a lighting simulation, photometric files, such as the photometric standard light data file format, are often used to simulate light sources such as street lamps and exterior building lights in nighttime environments. This paper examines the validity of using these same photometric file types for the simulation of vehicle headlamps by comparing the light distribution from actual vehicle headlamps to photometric files of these same headlamps.

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 a 2012 paper, Brach, Brach, and Louderback (BBL) investigated the uncertainty that arises in calculating the change in velocity and crush energy with the use of the CRASH3 equations (2012-01-0608). They concluded that the uncertainty in these values caused by variations in the stiffness coefficients significantly outweighed the uncertainty caused by variations in the crush measurements. This paper presents a revised analysis of the data that BBL analyzed and further assesses the level of uncertainty that arises in CRASH3 calculations. While the findings of this study do not invalidate BBL's ultimate conclusion, the methodology utilized in this paper incorporated two changes to BBL's methodology. First, in analyzing the crash test data for several vehicles, a systematic error that is sometimes present in the reported crush measurements was accounted for and corrected.

PC-Crash™, a widely used crash analysis software package, incorporates the capability for modeling non-constant vehicle acceleration, where the acceleration rate varies with speed, weight, engine power, the degree of throttle application, and the roadway slope. The research reported here offers a validation of this capability, demonstrating that PC-Crash can be used to realistically model the build-up of a vehicle's speed under maximal acceleration. In the research reported here, PC-Crash 9.0 was used to model the full-throttle acceleration capabilities of three vehicles with automatic transmissions - a 2006 Ford Crown Victoria Police Interceptor (CVPI), a 2000 Cadillac DeVille DTS, and a 2003 Ford F150. For each vehicle, geometric dimensions, inertial properties, and engine/drivetrain parameters were obtained from a combination of manufacturer specifications, calculations, inspections of exemplar vehicles and full-scale vehicle testing.

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 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.

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.

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.

This paper presents a comprehensive literature review of original equipment event data recorders (EDR) installed in passenger vehicles, as well as a summary of results from the instrumented validation studies. The authors compiled 187 peer-reviewed studies, textbooks, legal opinions, governmental rulemaking policies, industry publications and presentations pertaining to event data recorders. Of the 187 total references, there were 64 that contained testing data. The authors conducted a validation analysis using data from 27 papers that presented both the EDR and corresponding independent instrumentation values for: Vehicle velocity change (ΔV) Pre-Crash vehicle speed The combined results from these studies highlight unique observations of EDR system testing and demonstrate the observed performance of original equipment event data recorders in passenger vehicles.

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.

Improvements in computer image processing and identification capability have led to programs that can rapidly perform calculations and model the three-dimensional spatial characteristics of objects simply from photographs or video frames. This process, known as structure-from-motion or image based scanning, is a photogrammetric technique that analyzes features of photographs or video frames from multiple angles to create dense surface models or point clouds. Concurrently, unmanned aircraft systems have gained widespread popularity due to their reliability, low-cost, and relative ease of use. These aircraft systems allow for the capture of video or still photographic footage of subjects from unique perspectives. This paper explores the efficacy of using a point cloud created from unmanned aerial vehicle video footage with traditional single-image photogrammetry methods to recreate physical evidence at a crash scene.