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dc.contributor.authorWu, Bing-Feien_US
dc.contributor.authorChen, Ying-Hanen_US
dc.contributor.authorYeh, Chung-Hsuanen_US
dc.contributor.authorLi, Yen-Fengen_US
dc.date.accessioned2014-12-08T15:32:26Z-
dc.date.available2014-12-08T15:32:26Z-
dc.date.issued2013-09-01en_US
dc.identifier.issn1524-9050en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TITS.2013.2257759en_US
dc.identifier.urihttp://hdl.handle.net/11536/22774-
dc.description.abstractWith the growing concern for driving safety, many driving-assistance systems have been developed. In this paper, we develop a reasoning-based framework for the monitoring of driving safety. The main objective is to present drivers with an intuitively understood green/yellow/red indicator of their danger level. Because the danger levelmay change owing to the interaction of the host vehicle and the environment, the proposed framework involves two stages of danger-level alerts. The first stage collects lane bias, the distance to the front car, longitudinal and lateral accelerations, and speed data from sensors installed in a real vehicle. All data were recorded in a normal driving environment for the training of hidden Markov models of driving events, including normal driving, acceleration, deceleration, changing to the left or right lanes, zigzag driving, and approaching the car in front. In addition to recognizing these driving events, the degree of each event is estimated according to its character. In the second stage, the danger-level indicator, which warns the driver of a dangerous situation, is inferred by fuzzy logic rules that address the recognized driving events and their degrees. A hierarchical decision strategy is also designed to reduce the number of rules that are triggered. The proposed framework was successfully implemented on a TI DM3730-based embedded platform and was fully evaluated in a real road environment. The experimental results achieved a detection ratio of 99% for event recognition, compared with that achieved by four conventional methods.en_US
dc.language.isoen_USen_US
dc.subjectDriving eventsen_US
dc.subjectdriving safetyen_US
dc.subjectfuzzy logicen_US
dc.subjecthidden Markov models (HMMs)en_US
dc.titleReasoning-Based Framework for Driving Safety Monitoring Using Driving Event Recognitionen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TITS.2013.2257759en_US
dc.identifier.journalIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMSen_US
dc.citation.volume14en_US
dc.citation.issue3en_US
dc.citation.spage1231en_US
dc.citation.epage1241en_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000324336100019-
dc.citation.woscount0-
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