Intelligent Diagnosis Based On Validated And Fused Data For Relilability And Safety Enhancement Of Automated Vehicles In An IVHS

Alice Agogino, Susan Chao, Kai Goebel, Satnam Alag, Bradly Cammon and Jiangxin Wang

Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley

Abstract: Vehicles in an IVHS system rely heavily on information obtained from sensors. So far, most control systems make the implicit assumption that sensor information is always correct. However, in reality, sensor information is always corrupted to some degree by noise which varies with operating conditions, environmental conditions, and other factors. In addition, sensors can fail due to a variety of reasons. To overcome these shortcomings, sensor validation is needed to assess the integrity of the sensor information and adjust or correct as appropriate. In the presence of redundant information, sensor data must be fused, accommodating the findings from the validation process. In this report, we address the above issues. Key words: sensor validation, sensor fusion, data fusion, supervisory control, management of uncertainty, reliability, safety, Bayes networks, fault detection, Diagnosis, Influence Diagrams, risk analysis, decision making

Keywords: Automobiles--Automatic control--Mathematical models; Multisensor data fusion; Reliability (Engineering); Fault location (Engineering) (search for similar items in EconPapers)
Date: 1998-01-01
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