  The development of autonomous vehicles requires having access to a large
amount of data in the concerning driving scenarios. However, manual annotation
of such driving scenarios is costly and subject to the errors in the rule-based
trajectory labeling systems. To address this issue, we propose an effective
non-parametric trajectory clustering framework consisting of five stages: (1)
aligning trajectories and quantifying their pairwise temporal dissimilarities,
(2) embedding the trajectory-based dissimilarities into a vector space, (3)
extracting transitive relations, (4) embedding the transitive relations into a
new vector space, and (5) clustering the trajectories with an optimal number of
clusters. We investigate and evaluate the proposed framework on a challenging
real-world dataset consisting of annotated trajectories. We observe that the
proposed framework achieves promising results, despite the complexity caused by
having trajectories of varying length. Furthermore, we extend the framework to
validate the augmentation of the real dataset with synthetic data generated by
a Generative Adversarial Network (GAN) where we examine whether the generated
trajectories are consistent with the true underlying clusters.
