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Accurately measuring the kinematics of a vehicle is necessary to understand vehicle dynamics. As such, a new technique for measuring planar motion of a vehicle using downward-facing high-speed or high-definition camera is presented in this paper. Forward, lateral, and angular velocities can be obtained simultaneously from a calibrated image sequence by using concepts from digital image correlation (DIC). The technique requires the use of a camera, mounting device (e.g. tripod) and computer for post processing the image sequence. The technique is shown to agree with Radar, GPS, and Accelerometer based techniques for measuring velocity. The camera based system may be well suited to measure lower velocities compared to other common instrumentation systems. Digital image correlation is a technique used to study displacement, deformation, and strain by examining a sequence of digital images of a random pattern on the surface of a material.

Total station equipment, triangulation, or some other mapping technique can generate x-y coordinates describing curved tire marks on the pavement. These marks may result from a critical speed maneuver. Traditionally, these marks are assumed to follow a circular arc and a radius can be determined for use in the critical speed yaw formula. However, critical speed yaw marks typically have a decreasing radius in the direction of travel and a spiral is a more precise fit to the data. In this paper, a total least squares fitting approach is presented to fit the parameters of three types of spiral curves to coordinate data. These are a clothoid spiral, a logarithmic spiral, and an Archimedean spiral which are evaluated and compared for usability in a critical speed yaw analysis. A spreadsheet implementation is presented that makes use of the Microsoft Excel Solver Add-in to perform the minimization of the total least squares fit for the spirals.

There are numerous activities in the automotive industry in which a vehicle drives a pre-defined route multiple times such as portable emissions measurement systems testing or real-world electric vehicle range testing. The speed profile is not the same for each drive cycle due to uncontrollable real-world variables such as traffic, stoplights, stalled vehicles, or weather conditions. It can be difficult to compare each run accurately. To this end, this paper presents a method to compare and quantify the repeatability of real-world on-road vehicle driving schedules using dynamic time warping (DTW). DTW is a well-developed computational algorithm which compares two different time-series signals describing the same underlying phenomenon but occurring at different time scales. DTW is applied to real-world, on-road drive cycles, and metrics are developed to quantify similarities between these drive cycles.