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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.

A follow-up case study on rollover testing with a single full-size sport utility vehicle (SUV) was conducted under controlled real-world conditions. The purpose of this study was to conduct a well-documented rollover event that could be utilized in evaluating various methods and techniques over the phases associated with rollover accidents. The phases documented and discussed, inherent to rollovers, are: pre-trip, trip, and rolling phases. With recent advances in technology, new devices and techniques have been designed which improve the ability to capture and document the unpredictable dynamic events surrounding vehicle rollovers. One such device is an inertial measurement unit (IMU), which utilizes GPS technology along with integrated sensors to report and record measured dynamic parameters real-time. The data obtained from a RT-4003 IMU device are presented and compared along with previous test data and methodology.

Photogrammetry, camera matching, and model-based image matching are commonly used techniques to analyze photographs and video for accident reconstruction and other forensic applications. Investigators are often tasked with taking measurements from photographs or determining speed from a video. All such calculations are based on fundamental geometric principles governing image projection inside a camera. Most treatments in the literature express the image projection equations in matrix notation rather than closed-form solutions. The purpose of this paper is to present a geometric derivation of the image projection equations in closed form that can be readily applied by a qualified investigator without the need for specialized software. In addition, a simple brute force optimization procedure is described to perform camera matching and model-based image matching. Examples are provided to demonstrate the method.