Health Monitoring System for Autonomous Vehicles using Dynamic Bayesian Networks for Diagnosis and Prognosis

Abstract

Autonomous Vehicles have the potential to change the urban transport scenario. However, to be able to safely navigate autonomously they need to deal with faults that its components are subject to. Therefore, Health Monitoring System is a essential component of the autonomous system, since allows Fault Detection and Diagnosis. In addition, Prognosis System is also important, since it allows predictive maintenance and safer decisions during vehicle navigation. This paper presents a Hierarchical Component-based Health Monitoring System with Fault Detection, Diagnosis and Prognosis using Dynamic Bayesian Network (DBN) with residue generation, a combination of knowledge-based and model-based detection, diagnosis and prognosis approaches. We evaluate the proposed Dynamic Bayesian Network using different machine learning metrics and a dataset with sensor readings gathered using the CaRINA II autonomous vehicle platform, and the CARLA simulator. Both simulated and experimental results demonstrated a positive performance of the DBNs even with high rate of missing data for some of the model’s variables.