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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.

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.

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.

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 investigates the dynamics of four motorcycle crashes that occurred on or near a curve (Edwards Corner) on a section of the Mulholland Highway called “The Snake.” This section of highway is located in the Santa Monica Mountains of California. All four accidents were captured on video and they each involved a high-side fall of the motorcycle and rider. This article reports a technical description and analysis of these videos in which the motion of the motorcycles and riders is quantified. To aid in the analysis, the authors mapped Edwards Corner using both a Sokkia total station and a Faro laser scanner. This mapping data enabled analysis of the videos to determine the initial speed of the motorcycles, to identify where in the curve particular rider actions occurred, to quantify the motion of the motorcycles and riders, and to characterize the roadway radius and superelevation throughout the curve.