  Annotated driving scenario trajectories are crucial for verification and
validation of autonomous vehicles. However, annotation of such trajectories
based only on explicit rules (i.e. knowledge-based methods) may be prone to
errors, such as false positive/negative classification of scenarios that lie on
the border of two scenario classes, missing unknown scenario classes, or even
failing to detect anomalies. On the other hand, verification of labels by
annotators is not cost-efficient. For this purpose, active learning (AL) could
potentially improve the annotation procedure by including an annotator/expert
in an efficient way. In this study, we develop a generic active learning
framework to annotate driving trajectory time series data. We first compute an
embedding of the trajectories into a latent space in order to extract the
temporal nature of the data. Given such an embedding, the framework becomes
task agnostic since active learning can be performed using any classification
method and any query strategy, regardless of the structure of the original time
series data. Furthermore, we utilize our active learning framework to discover
unknown driving scenario trajectories. This will ensure that previously unknown
trajectory types can be effectively detected and included in the labeled
dataset. We evaluate our proposed framework in different settings on novel
real-world datasets consisting of driving trajectories collected by Volvo Cars
Corporation. We observe that active learning constitutes an effective tool for
labelling driving trajectories as well as for detecting unknown classes.
Expectedly, the quality of the embedding plays an important role in the success
of the proposed framework.
