Intelligent Sensor Validation And Fusion For Vehicle Guidance Using Probabilistic And Fuzzy Methods

Alice Agogino, Kai Goebel and Sanam Alag

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

Abstract: This study reports on a method to accomplish sensor validation and fusion in Intelligent Transportation Systems (ITS). The method is based on probabilistic and fuzzy techniques that express a confidence in the sensor data and take into account environmental factors and the state of the system. Sensor data fusion uses the confidence assigned to each sensor reading and integrates them into one reading. Noise and failure are filtered from the data and lead to a safety improvement in ITS.

Keywords: Automobiles--Automatic control--Mathematical models; Multisensor data fusion; Fuzzy systems; Fault location (Engineering) (search for similar items in EconPapers)
Date: 1997-01-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.escholarship.org/uc/item/2nq1f8n9.pdf;origin=repeccitec (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cdl:itsrrp:qt2nq1f8n9

Access Statistics for this paper

More papers in Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley Contact information at EDIRC.
Bibliographic data for series maintained by Lisa Schiff ().