Kineticorp LLC

Greenwood Village, CO, United States

Kineticorp LLC

Greenwood Village, CO, United States
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Neale W.T.,Kineticorp LLC | Hessel D.,Kineticorp LLC | Terpstra T.,Kineticorp LLC
SAE Technical Papers | Year: 2011

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. Lens distortion from a variety of cameras is analyzed, and the ultimate effect that this distortion has on the image is evaluated, with a discussion on the overall difference this distortion would cause to measuring evidence in an image, such as tire mark distances and curvature. Ways of correcting this distortion are also addressed. Copyright © 2011 SAE International.


Rose N.A.,Kineticorp LLC | Carter N.,Kineticorp LLC
SAE Technical Papers | Year: 2014

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. This systematic error arises when a vehicle's plastic bumper fascia rebounds more than the underlying structure, creating an air gap and causing the reported crush measurements both to underestimate the actual deformation and to exhibit more scatter than they otherwise would. This scatter translates into uncertainty in the stiffness coefficients. Second, linear regression was used to obtain the stiffness coefficients and to quantify their uncertainty. Instead of using linear regression, BBL assumed the same damage onset speed (b0) for each crash test. In essence, this means that BBL assumed a value for one of the stiffness coefficients, with the only variability in that coefficient coming from the differences in vehicle weights from test to test. The methodology employed in this paper eliminated the need to assume a damage onset speed. Copyright © 2014 SAE International.


Rose N.A.,Kineticorp LLC | Carter N.,Kineticorp LLC | Pentecost D.,Kineticorp LLC
SAE Technical Papers | Year: 2014

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. In each case, the full-throttle acceleration of the vehicles modeled in PC-Crash showed good agreement with the acceleration of the real vehicles in our road tests. Copyright © 2014 SAE International.


Beauchamp G.,Kineticorp LLC | Thornton D.,Kineticorp LLC | Bortles W.,Kineticorp LLC | Rose N.,Kineticorp LLC
SAE International Journal of Transportation Safety | Year: 2016

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. Copyright © 2016 SAE International.


Beauchamp G.,Kineticorp LLC | Pentecost D.,Kineticorp LLC | Koch D.,Kineticorp LLC | Rose N.,Kineticorp LLC
SAE International Journal of Transportation Safety | Year: 2016

Tire mark striations are discussed often in the literature pertaining to accident reconstruction. The discussions in the literature contain many consistencies, but also contain disagreements. In this article, the literature is first summarized, and then the differences in the mechanism in which striations are deposited and interpretation of this evidence are explored. In previous work, it was demonstrated that the specific characteristics of tire mark striations offer a glimpse into the steering and driving actions of the driver. An equation was developed that relates longitudinal tire slip (braking) to the angle of tire mark striations [1]. The longitudinal slip equation was derived from the classic equation for tire slip and also geometrically. In this study, the equation for longitudinal slip is re-derived from equations that model tire forces. Human Vehicle Environment (HVE), a common accident reconstruction and vehicle dynamics simulation software package, was then used to compare striation direction as predicted by the striation slip equation to tire force direction in the simulation. This paper focuses on discussions about striations in the literature and the relationship between tire mark striations and tire forces. A companion paper, "Tire Mark Striations: Sensitivity and Uncertainty Analysis", focuses on the practical use of striation evidence for accident reconstruction purposes [2]. Copyright © 2016 SAE International.


Neale W.T.,Kineticorp LLC | Marr J.,Kineticorp LLC | Hessel D.,Kineticorp LLC
SAE Technical Papers | Year: 2016

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. However, by combining the realistic imagery from video and photographs with the flexibility of a computer generated environment, it is possible to vary any number of factors such as the speed of vehicles and the driver lane position, and to vary the types of vehicles and lighting conditions involved in the scenario. The combination of projection mapping, video tracking, and nighttime video and photography methodologies allow this flexibility. In addition to presenting the methodology and the resulting computer simulation environment, the final simulation is compared to actual video recordings of the same driving scenario to evaluate how similar they are in value, tone, color and visibility. © 2016 SAE International.


Rose N.A.,Kineticorp LLC | Carter N.,Kineticorp LLC | Beauchamp G.,Kineticorp LLC
SAE Technical Papers | Year: 2016

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. This paper explores the influence of high rotational energies and impact induced steering on the deceleration experienced by a vehicle following an impact. © 2016 SAE International.


Neale W.T.,Kineticorp LLC | Danaher D.,Kineticorp LLC | McDonough S.,Kineticorp LLC | Owens T.,Kineticorp LLC
SAE Technical Papers | Year: 2016

There are numerous publically available smart phone applications designed to track the speed and position of the user. By accessing the phones built in GPS receivers, these applications record the position over time of the phone and report the record on the phone itself, and typically on the application's website. These applications range in cost from free to a few dollars, with some, that advertise greater functionality, costing significantly higher. This paper examines the reliability of the data reported through these applications, and the potential for these applications to be useful in certain conditions where monitoring and recording vehicle or pedestrian movement is needed. To analyze the reliability of the applications, three of the more popular and widely used tracking programs were downloaded to three different smart phones to represent a good spectrum of operating platforms. Several tests were conducted to evaluate the applications ability to measure speed, elevation change, and positioning on aerial imagery. The data reported by the applications in each test was compared to a Race Logic VBOX VB20SL3 Data Acquisition Unit that was also used in the same tests. The VBOX unit was used as a standard against which to measure the applications efficacy since this unit is specifically designed to monitor and record vehicle movement1. The results show that under certain conditions, speed, positioning on aerial imagery, and elevation change as recorded by applications were relatively accurate for conditions where the recorded period occurred over a long duration of time. The results from this testing shows that recording the motion of a vehicle or pedestrian over a long duration of time, greater than 10 seconds, with minimal changes in velocity can be properly documented by the use of a smart phone running a commonly available applications. © Copyright 2016 SAE International.


Terpstra T.,Kineticorp LLC | Voitel T.,Kineticorp LLC | Hashemian A.,Kineticorp LLC
SAE Technical Papers | Year: 2016

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. This paper evaluates the accuracy and capabilities of four automated photogrammetry based software programs to accurately create 3D point clouds, by comparing the results to 3D scanning. Both a damaged and undamaged vehicle were documented with video and photographs and on average the damaged vehicle set returned more data points with higher accuracy than the undamaged vehicle set. Four cameras types were evaluated and more accurate results were achieved when using either a DSLR or a point-and-shoot camera than when using a GoPro, or a cell phone camera. Photogrammetry data from video footage was analyzed and found to be both less accurate and to return less data than photographs. By limiting the number of photographs used, it was found that a photogrammetry solution could be achieved with as few as 16 photographs encircling a vehicle, but better results were reached with a larger number of photographs. Copyright © 2016 SAE International.


Neale W.T.,Kineticorp LLC | Hessel D.,Kineticorp LLC | Koch D.,Kineticorp LLC
SAE Technical Papers | Year: 2016

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. This paper explains the automated process of first tracking pixels in the video footage, and then remapping the 2D coordinates onto three dimensional geometry using previously published projection mapping and photogrammetry techniques. The results of this process are then compared to VBOX recordings of the objects seen in the video to evaluate the accuracy of the method. Some beneficial aspects of this process include the time reduced in tracking the object, since it is automated, and also that the shape and size of the object being tracked does not need to be known since it is a pixel being tracked, rather than the geometry of the object itself. Copyright © 2016 SAE International.

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