Using Unsupervised Learning to Explore Robot-Pedestrian Interactions in Urban Environments

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. An...

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Bibliographic Details
Published inarXiv.org
Main Authors Zug, Sebastian, Jäger, Georg, Seyffer, Norman, Plank, Martin, Licht, Gero, Felix Wilhelm Siebert
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 20.05.2024
Subjects
Cluster analysis
Clustering
Principal components analysis
Robots
Situational awareness
Unsupervised learning
Urban environments
Vector quantization
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Summary: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.
ISSN:2331-8422