Clustering Optimization for Abnormality Detection in Semi-Autonomous Systems

Abstract

The use of machine learning techniques is fundamental for developing autonomous systems that can assist humans in everyday tasks. This paper focus on selecting an appropriate network size for detecting abnormalities in multisensory data coming from a semi-autonomous vehicle. We use an extension of Growing Neural Gas with the utility measurement (GNG-U) for segmenting multisensory data into an optimal set of clusters that facilitate a semantic interpretation of data and define local linear models used for prediction purposes. A functional that favors precise linear dynamical models in large state space regions is considered for optimization purposes. The proposed method is tested with synchronized multi-sensor dynamic data related to different maneuvering tasks performed by a semi-autonomous vehicle that interacts with pedestrians in a closed environment. Comparisons with a previous work of abnormality detection are provided.