  Nowadays, the ubiquity of various sensors enables the collection of
voluminous datasets of car trajectories. Such datasets enable analysts to make
sense of driving patterns and behaviors: in order to understand the behavior of
drivers, one approach is to break a trajectory into its underlying patterns and
then analyze that trajectory in terms of derived patterns. The process of
trajectory segmentation is a function of various resources including a set of
ground truth trajectories with their driving patterns. To the best of our
knowledge, no such ground-truth dataset exists in the literature. In this
paper, we describe a trajectory annotation framework and report our results to
annotate a dataset of personal car trajectories. Our annotation methodology
consists of a crowd-sourcing task followed by a precise process of aggregation.
Our annotation process consists of two granularity levels, one to specify the
annotation (segment border) and the other one to describe the type of the
segment (e.g. speed-up, turn, merge, etc.). The output of our project, Dataset
of Annotated Car Trajectories (DACT), is available online at
https://figshare.com/articles/dact_dataset_of_annotated_car_trajectories/5005289 .

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