A Health Monitoring System with Hybrid Bayesian Network for Autonomous Vehicle

Autonomous Vehicles should transform the urban transport scenario. However, to be able to navigate completely autonomously, they also need to deal with faults that its components are subject to. Therefore, Health Monitoring System, is a component of the autonomous system which constantly monitor the...

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Bibliographic Details
Published in2019 19th International Conference on Advanced Robotics (ICAR) pp. 260 - 265
Main Authors Gomes, Iago Pacheco, Wolf, Denis Fernando
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
Autonomous vehicles
Bayes methods
Fault detection
Global Positioning System
Knowledge based systems
Monitoring
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DOI10.1109/ICAR46387.2019.8981565

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Summary:Autonomous Vehicles should transform the urban transport scenario. However, to be able to navigate completely autonomously, they also need to deal with faults that its components are subject to. Therefore, Health Monitoring System, is a component of the autonomous system which constantly monitor the integrity of those components, so that safety measures are taken as soon as an abnormal condition is detected. This paper presents a Health Monitoring System using Component-based Hierarchical approach and Hybrid Bayesian Networks with Residue Evidence for Fault Detection and Diagnosis in lateral and longitudinal controllers, and also in the GPS sensor. Finally, the results demonstrated the reliability of the proposed methods for Fault Detection and Diagnosis, and also highlighted the importance of safety protocols for Autonomous Vehicles.
DOI:10.1109/ICAR46387.2019.8981565