  Clustering traffic scenarios and detecting novel scenario types are required
for scenario-based testing of autonomous vehicles. These tasks benefit from
either good similarity measures or good representations for the traffic
scenarios. In this work, an expert-knowledge aided representation learning for
traffic scenarios is presented. The latent space so formed is used for
successful clustering and novel scenario type detection. Expert-knowledge is
used to define objectives that the latent representations of traffic scenarios
shall fulfill. It is presented, how the network architecture and loss is
designed from these objectives, thereby incorporating expert-knowledge. An
automatic mining strategy for traffic scenarios is presented, such that no
manual labeling is required. Results show the performance advantage compared to
baseline methods. Additionally, extensive analysis of the latent space is
performed.
