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

The evolution of Intelligent Transportation Systems in recent times necessitates the development of self-awareness in agents. Before the intensive use of Machine Learning, the detection of abnormalities was manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. This paper aims to introduce a novel method to develop self-awareness in autonomous vehicles that mainly focuses on detecting abnormal situations around the considered agents. Multi-sensory time-series data from the vehicles are used to develop the data-driven Dynamic Bayesian Network (DBN) models used for future state prediction and the detection of dynamic abnormalities. Moreover, an initial level collective awareness model that can perform joint anomaly detection in co-operative tasks is proposed. The GNG algorithm learns the DBN models' discrete node variables; probabilistic transition links connect the node variables. A Markov Jump Particle Filter (MJPF) is applied to predict future states and detect when the vehicle is potentially misbehaving using learned DBNs as filter parameters. In this paper, datasets from real experiments of autonomous vehicles performing various tasks used to learn and test a set of switching DBN models. • Self-awareness models considering pair-based features of the vehicles. • Initial level collective awareness model for joint anomaly detection. • Data-driven Dynamic Bayesian Network models for anomaly detection in driving tasks. • The Markov Jump Particle Filter for anomaly detection of vehicles. • Anomaly detection without exhaust coding for self-state variables for large projects. [ABSTRACT FROM AUTHOR]