  The autonomous driving (AD) industry is exploring the use of knowledge graphs
(KGs) to manage the vast amount of heterogeneous data generated from vehicular
sensors. The various types of equipped sensors include video, LIDAR and RADAR.
Scene understanding is an important topic in AD which requires consideration of
various aspects of a scene, such as detected objects, events, time and
location. Recent work on knowledge graph embeddings (KGEs) - an approach that
facilitates neuro-symbolic fusion - has shown to improve the predictive
performance of machine learning models. With the expectation that
neuro-symbolic fusion through KGEs will improve scene understanding, this
research explores the generation and evaluation of KGEs for autonomous driving
data. We also present an investigation of the relationship between the level of
informational detail in a KG and the quality of its derivative embeddings. By
systematically evaluating KGEs along four dimensions -- i.e. quality metrics,
KG informational detail, algorithms, and datasets -- we show that (1) higher
levels of informational detail in KGs lead to higher quality embeddings, (2)
type and relation semantics are better captured by the semantic transitional
distance-based TransE algorithm, and (3) some metrics, such as coherence
measure, may not be suitable for intrinsically evaluating KGEs in this domain.
Additionally, we also present an (early) investigation of the usefulness of
KGEs for two use-cases in the AD domain.
