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dc.contributor.authorGarcia Fernandez, Angel
dc.contributor.authorHammarstrand, L.
dc.contributor.authorFatemi, M.
dc.contributor.authorSvensson, L.
dc.identifier.citationGarcia Fernandez, A. and Hammarstrand, L. and Fatemi, M. and Svensson, L. 2014. Bayesian Road Estimation Using Onboard Sensors. IEEE Transactions on Intelligent Transportation Systems. 15 (4): pp. 1676-1689.

This paper describes an algorithm for estimating the road ahead of a host vehicle based on the measurements from several onboard sensors: a camera, a radar, wheel speed sensors,and an inertial measurement unit.We propose a novel road model that is able to describe the road ahead with higher accuracy than the usual polynomial model. We also develop a Bayesian fusionsystem that uses the following information from the surroundings: lane marking measurements obtained by the camera and leading vehicle and stationary object measurements obtained bya radar–camera fusion system. The performance of our fusion algorithm is evaluated in several drive tests. As expected, the more information we use, the better the performance is.Index Terms—Camera, information fusion, radar, road geometry,unscented Kalman filter (UKF).

dc.publisherIEEE Intelligent Transportation Systems Society
dc.subjectunscented Kalman filter (UKF)
dc.subjectroad geometry
dc.subjectinformation fusion
dc.titleBayesian Road Estimation Using Onboard Sensors
dc.typeJournal Article
dcterms.source.titleIEEE Transactions on Intelligent Transportation Systems

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curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusOpen access

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