  This study identifies a gap in data-driven approaches to robot-centric
pedestrian interactions and proposes a corresponding pipeline. The pipeline
utilizes unsupervised learning techniques to identify patterns in interaction
data of urban environments, specifically focusing on conflict scenarios.
Analyzed features include the robot's and pedestrian's speed and contextual
parameters such as proximity to intersections. They are extracted and reduced
in dimensionality using Principal Component Analysis (PCA). Finally, K-means
clustering is employed to uncover underlying patterns in the interaction data.
A use case application of the pipeline is presented, utilizing real-world robot
mission data from a mid-sized German city. The results indicate the need for
enriching interaction representations with contextual information to enable
fine-grained analysis and reasoning. Nevertheless, they also highlight the need
for expanding the data set and incorporating additional contextual factors to
enhance the robots situational awareness and interaction quality.
