A Methodology for Driving Behavior Recognition in Simulated Scenarios Using Biosignals
The 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...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9167
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9167
- Palabra clave:
- Acceleration patterns
Biosignals
Driving behavior
Feature extraction
Automobile drivers
Digital storage
Electrophysiology
Feature extraction
Traffic surveys
Acceleration pattern
Aggressive driving
Aggressive driving behaviors
Biosignals
Driving behavior
Driving performance
Galvanic skin response
Traffic fatalities
Behavioral research
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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A Methodology for Driving Behavior Recognition in Simulated Scenarios Using Biosignals |
| title |
A Methodology for Driving Behavior Recognition in Simulated Scenarios Using Biosignals |
| spellingShingle |
A Methodology for Driving Behavior Recognition in Simulated Scenarios Using Biosignals Acceleration patterns Biosignals Driving behavior Feature extraction Automobile drivers Digital storage Electrophysiology Feature extraction Traffic surveys Acceleration pattern Aggressive driving Aggressive driving behaviors Biosignals Driving behavior Driving performance Galvanic skin response Traffic fatalities Behavioral research |
| title_short |
A Methodology for Driving Behavior Recognition in Simulated Scenarios Using Biosignals |
| title_full |
A Methodology for Driving Behavior Recognition in Simulated Scenarios Using Biosignals |
| title_fullStr |
A Methodology for Driving Behavior Recognition in Simulated Scenarios Using Biosignals |
| title_full_unstemmed |
A Methodology for Driving Behavior Recognition in Simulated Scenarios Using Biosignals |
| title_sort |
A Methodology for Driving Behavior Recognition in Simulated Scenarios Using Biosignals |
| dc.contributor.editor.none.fl_str_mv |
Figueroa-Garcia J.C. Duarte-Gonzalez M. Jaramillo-Isaza S. Orjuela-Canon A.D. Diaz-Gutierrez Y. |
| dc.subject.keywords.none.fl_str_mv |
Acceleration patterns Biosignals Driving behavior Feature extraction Automobile drivers Digital storage Electrophysiology Feature extraction Traffic surveys Acceleration pattern Aggressive driving Aggressive driving behaviors Biosignals Driving behavior Driving performance Galvanic skin response Traffic fatalities Behavioral research |
| topic |
Acceleration patterns Biosignals Driving behavior Feature extraction Automobile drivers Digital storage Electrophysiology Feature extraction Traffic surveys Acceleration pattern Aggressive driving Aggressive driving behaviors Biosignals Driving behavior Driving performance Galvanic skin response Traffic fatalities Behavioral research |
| description |
The 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. |
| publishDate |
2019 |
| dc.date.issued.none.fl_str_mv |
2019 |
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2020-03-26T16:33:06Z |
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2020-03-26T16:33:06Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_c94f |
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Conferencia |
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Communications in Computer and Information Science; Vol. 1052, pp. 357-367 |
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9783030310189 |
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18650929 |
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https://hdl.handle.net/20.500.12585/9167 |
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10.1007/978-3-030-31019-6_31 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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56682770100 57205565967 57210822856 |
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Communications in Computer and Information Science; Vol. 1052, pp. 357-367 9783030310189 18650929 10.1007/978-3-030-31019-6_31 Universidad Tecnológica de Bolívar Repositorio UTB 56682770100 57205565967 57210822856 |
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https://hdl.handle.net/20.500.12585/9167 |
| dc.language.iso.none.fl_str_mv |
eng |
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eng |
| dc.relation.conferencedate.none.fl_str_mv |
16 October 2019 through 18 October 2019 |
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http://purl.org/coar/access_right/c_16ec |
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Atribución-NoComercial 4.0 Internacional |
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application/pdf |
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Springer |
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Springer |
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6th Workshop on Engineering Applications, WEA 2019 |
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Figueroa-Garcia J.C.Duarte-Gonzalez M.Jaramillo-Isaza S.Orjuela-Canon A.D.Diaz-Gutierrez Y.Domínguez Jiménez, Juan AntonioCampo Landines, KiaraContreras Ortiz, Sonia Helena2020-03-26T16:33:06Z2020-03-26T16:33:06Z2019Communications in Computer and Information Science; Vol. 1052, pp. 357-367978303031018918650929https://hdl.handle.net/20.500.12585/916710.1007/978-3-030-31019-6_31Universidad Tecnológica de BolívarRepositorio UTB566827701005720556596757210822856The 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.Recurso electrónicoapplication/pdfengSpringerhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075688427&doi=10.1007%2f978-3-030-31019-6_31&partnerID=40&md5=f7ed101058fc2d5b7a15c0fe2962c40c6th Workshop on Engineering Applications, WEA 2019A Methodology for Driving Behavior Recognition in Simulated Scenarios Using Biosignalsinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fAcceleration patternsBiosignalsDriving behaviorFeature extractionAutomobile driversDigital storageElectrophysiologyFeature extractionTraffic surveysAcceleration patternAggressive drivingAggressive driving behaviorsBiosignalsDriving behaviorDriving performanceGalvanic skin responseTraffic fatalitiesBehavioral research16 October 2019 through 18 October 2019Bradley, M.M., Lang, P.J., Measuring emotion: The self-assessment manikin and the semantic differential (1994) J. Behav. Ther. Exp. Psychiatry, 25 (1), pp. 49-59Cooper, C.L., Dewe, P.J., (2008) Stress: A Brief History, , Wiley, ChichesterDomínguez-Jiménez, J., Campo-Landines, K., Martínez-Santos, J., Contreras-Ortiz, S., Emotion detection through biomedical signals: A pilot study (2018) 14Th International Symposium on Medical Information Processing and Analysis, Vol. 10975, P. 1097506. International Society for Optics and PhotonicsEkman, P., An argument for basic emotions (1992) Cognit. Emot., 6 (3-4), pp. 169-200Hongyu, H., Zhou, X., Zhu, Z., Wang, Q., Xiao, H., A driving simulator study of young driver’s behavior under angry emotion (2019) Technical Report, SAE Technical PaperJames, L., Diane, N., (2000) Road Rage and Aggressive Driving: Steering Clear of Highway Warfare, , Prometheus, AmherstKnowles, M., Scott, H., Baglee, D., The effect of driving style on electric vehicle performance, economy and perception (2012) Int. J. Electr. Hybrid Veh., 4 (3), pp. 228-247Lanatà, A., How the autonomic nervous system and driving style change with incremental stressing conditions during simulated driving (2014) IEEE Trans. Intell. Transp. Syst., 16 (3), pp. 1505-1517Lang, P.J., The emotion probe: Studies of motivation and attention (1995) Am. Psychol., 50 (5), p. 372Lang, P.J., Bradley, M.M., Appetitive and defensive motivation: Goal-directed or goal-determined? (2013) Emot. Rev., 5 (3), pp. 230-234Mesken, J., (2006) Determinants and Consequences of drivers’ Emotions. Stichting Weten-Schappelijk Onderzoek Verkeersveiligheid SWOVMesken, J., Hagenzieker, M.P., Rothengatter, T., de Waard, D., Frequency, determinants, and consequences of different drivers’ emotions: An on-the-road study using self-reports,(observed) behaviour, and physiology (2007) Transp. Res. Part F Traffic Psychol. Behav., 10 (6), pp. 458-475Ooi, J.S.K., Ahmad, S.A., Chong, Y.Z., Ali, S.H.M., Ai, G., Wagatsuma, H., Driver emotion recognition framework based on electrodermal activity measurements during simulated driving conditions (2016) 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)(2018) Global Status Report on Road SafetyPeng, Z., Wang, Y., Chen, Q., The generation and development of road rage incidents caused by aberrant overtaking: An analysis of cases in China (2019) Transp. Res. Part F Traffic Psychol. Behav., 60, pp. 606-619Plutchik, R., A general psychoevolutionary theory of emotion (1980) Theories of Emotion, pp. 3-33. , pp., Elsevier, AmsterdamQu, W., Ge, Y., Jiang, C., Du, F., Zhang, K., The dula dangerous driving index in China: An investigation of reliability and validity (2014) Accid. Anal. Prev., 64, pp. 62-68Reeve, J., (2014) Understanding Motivation and Emotion, , Wiley, ChichesterRodgers, M.M., Pai, V.M., Conroy, R.S., Recent advances in wearable sensors for health monitoring (2015) IEEE Sens. J., 15 (6), pp. 3119-3126Russell, J.A., A circumplex model of affect (1980) J. Pers. Soc. Psychol., 39 (6), p. 1161Sacharin, V., Schlegel, K., Scherer, K.R., (2012) Geneva Emotion Wheel Rating StudyWang, W., Cheng, Q., Li, C., André, D., Jiang, X., A cross-cultural analysis of driving behavior under critical situations: A driving simulator study (2019) Transp. Res. Part F Traffic Psychol. Behav., 62, pp. 483-493Wu, X., Wang, Y., Peng, Z., Chen, Q., A questionnaire survey on road rage and anger-provoking situations in China (2018) Accid. Anal. Prev., 111, pp. 210-221Zhang, T., Chan, A.H., The association between driving anger and driving outcomes: A meta-analysis of evidence from the past twenty years (2016) Accid. Anal. Prev., 90, pp. 50-62http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9167/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9167oai:repositorio.utb.edu.co:20.500.12585/91672023-05-26 08:15:19.388Repositorio Institucional UTBrepositorioutb@utb.edu.co |