On Integral Metrics and Trajectory Classification

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Lilly Vernor, Class of 2020


In this project, we explore distance in the context of metrics. More specifically, we take a look at an integral metric that is used to determine the distance between sets. The motivation behind this project is to determine that the integral metric we use is meaningful in the context of our data set. The data set we useconsists of trajectories of cars along a portion of the I5 highway. Through training, testing, and evaluating this model on the data set, we can reach conclusions on the structure of the data and the success of this integral metric in terms of a classifier. In the end, we can both determine whether or not this integral metric fits with the data set chosen and explore other areas where the metric could possibly succeed or where it might fail.

Comments from Mentors

From my experiences having Lilly in class, I already knew her to be a capable and hard-working student and an excellent writer.  She rises to the challenge and has always committed herself to mastering the material presented to her.  So when another advisor working with Lilly on this research project described her as a "dream student" - someone who could be given a task during a meeting, and then would come back the next week with the task completed and several new ideas stemming from it - I was not surprised, and gladly joined her thesis committee.  Through her writing and presentations Lilly demonstrated a strong mastery of the background material and ownership over her research results and future goals.  It was my great pleasure to work with Lilly and I commend her for this accomplishment!

Dr. Emily Herzig

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College of Science and Engineering