dc.contributorFigueroa-Garcia J.C.
dc.contributorDuarte-Gonzalez M.
dc.contributorJaramillo-Isaza S.
dc.contributorOrjuela-Canon A.D.
dc.contributorDiaz-Gutierrez Y.
dc.creatorDomínguez Jiménez, Juan Antonio
dc.creatorCampo Landines, Kiara
dc.creatorContreras Ortiz, Sonia Helena
dc.date.accessioned2020-03-26T16:33:06Z
dc.date.available2020-03-26T16:33:06Z
dc.date.created2020-03-26T16:33:06Z
dc.date.issued2019
dc.identifierCommunications in Computer and Information Science; Vol. 1052, pp. 357-367
dc.identifier9783030310189
dc.identifier18650929
dc.identifierhttps://hdl.handle.net/20.500.12585/9167
dc.identifier10.1007/978-3-030-31019-6_31
dc.identifierUniversidad Tecnológica de Bolívar
dc.identifierRepositorio UTB
dc.identifier56682770100
dc.identifier57205565967
dc.identifier57210822856
dc.description.abstractThe recognition of aggressive driving patterns could aid to improve driving safety and potentially reduce traffic fatalities on the roads. Driving behavior is strongly shaped by emotions and can be divided into two main categories: calmed (non-aggressive) and aggressive. In this paper, we present a methodology to recognize driving behavior using driving performance features and biosignals. We used biosensors to measure heart rate and galvanic skin response of fifteen volunteers while driving in a simulated scenario. They were asked to drive in two different situations to elicit calmed and aggressive driving behaviors. The purpose of this study was to determine if driving behavior can be assessed from biosignals and acceleration/braking events. From two-tailed student t-tests, the results suggest that it is possible to differentiate between aggressive and calmed driving behavior from biosignals and also from longitudinal vehicle’s data. © 2019, Springer Nature Switzerland AG.
dc.languageeng
dc.publisherSpringer
dc.relation16 October 2019 through 18 October 2019
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rightsAtribución-NoComercial 4.0 Internacional
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075688427&doi=10.1007%2f978-3-030-31019-6_31&partnerID=40&md5=f7ed101058fc2d5b7a15c0fe2962c40c
dc.source6th Workshop on Engineering Applications, WEA 2019
dc.titleA Methodology for Driving Behavior Recognition in Simulated Scenarios Using Biosignals


Este ítem pertenece a la siguiente institución