  The clustering of autonomous driving scenario data can substantially benefit
the autonomous driving validation and simulation systems by improving the
simulation tests' completeness and fidelity. This article proposes a
comprehensive data clustering framework for a large set of vehicle driving
data. Existing algorithms utilize handcrafted features whose quality relies on
the judgments of human experts. Additionally, the related feature compression
methods are not scalable for a large data-set. Our approach thoroughly
considers the traffic elements, including both in-traffic agent objects and map
information. Meanwhile, we proposed a self-supervised deep learning approach
for spatial and temporal feature extraction to avoid biased data
representation. With the newly designed driving data clustering evaluation
metrics based on data-augmentation, the accuracy assessment does not require a
human-labeled data-set, which is subject to human bias. Via such unprejudiced
evaluation metrics, we have shown our approach surpasses the existing methods
that rely on handcrafted feature extractions.
