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Health Monitoring System for Autonomous Vehicles using Dynamic Bayesian Networks for Diagnosis and Prognosis
Journal of Intelligent & Robotic Systems ( IF 3.3 ) Pub Date : 2020-12-14 , DOI: 10.1007/s10846-020-01293-y
Iago Pachêco Gomes , Denis Fernando Wolf

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.



中文翻译:

基于动态贝叶斯网络的无人驾驶车辆健康监测系统

自动驾驶汽车有可能改变城市交通状况。但是,为了能够安全地自主导航,他们需要处理其组件遭受的故障。因此,健康监控系统是自治系统的重要组成部分,因为它可以进行故障检测和诊断。此外,预后系统也很重要,因为它可以在车辆导航期间进行预测性维护和更安全的决策。本文提出了一种基于层次化组件的健康监控系统,该系统具有使用残留物生成的动态贝叶斯网络(DBN)进行故障检测,诊断和预后的能力,结合了基于知识和基于模型的检测,诊断和预后方法。我们使用不同的机器学习指标以及使用CaRINA II自动驾驶汽车平台和CARLA模拟器收集的带有传感器读数的数据集来评估拟议的动态贝叶斯网络。模拟和实验结果均表明,即使某些模型变量的数据丢失率很高,DBN仍具有良好的性能。

更新日期:2020-12-14
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