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The evolution of Intelligent Transportation Systems in recent timesnecessitates the development of self-awareness in agents. Before the intensiveuse of Machine Learning, the detection of abnormalities was manually programmedby checking every variable and creating huge nested conditions that are verydifficult to track. This paper aims to introduce a novel method to developself-awareness in autonomous vehicles that mainly focuses on detecting abnormalsituations around the considered agents. Multi-sensory time-series data fromthe vehicles are used to develop the data-driven Dynamic Bayesian Network (DBN)models used for future state prediction and the detection of dynamicabnormalities. Moreover, an initial level collective awareness model that canperform joint anomaly detection in co-operative tasks is proposed. The GNGalgorithm learns the DBN models' discrete node variables; probabilistictransition links connect the node variables. A Markov Jump Particle Filter(MJPF) is applied to predict future states and detect when the vehicle ispotentially misbehaving using learned DBNs as filter parameters. In this paper,datasets from real experiments of autonomous vehicles performing various tasksused to learn and test a set of switching DBN models.

Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection