Abnormality Detection using Graph Matching for Multi-Task Dynamics of Autonomous Systems

Abstract

Self-learning abilities in autonomous systems are essential to improve their situational awareness and detection of normal/abnormal situations. In this work, we propose a graph matching technique for activity detection in autonomous agents by using the Gromov-Wasserstein framework. A clustering approach is used to discretise continuous agents' states related to a specific task into a set of nodes with similar objectives. Additionally, a probabilistic transition matrix between nodes is used as edges weights to build a graph. In this paper, we extract an abnormal area based on a sub-graph that encodes the differences between coupled of activities. Such sub-graph is obtained by applying a threshold on the optimal transport matrix, which is obtained through the graph matching procedure. The obtained results are evaluated through experiments performed by a robot in a simulated environment and by a real autonomous vehicle moving within a University Campus.

Supporting Safe Decision Making Through Holistic System-Level Representations & Monitoring -- A Summary and Taxonomy of Self-Representation Concepts for Automated Vehicles

“…Some of those publications rather use the term self-awareness, while focusing on smaller monitoring aspects, e.g. for anomaly detection in single signals [30] or specific functions, such as in localization algorithms [31]. In contrast to these contained approaches, we have introduced the concept of self-aware automated vehicles [10] and present a general argument for cross-layer models, i.e.…”