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dc.contributor.authorMuhundan, Sushmethaaen
dc.date.accessioned2021-04-29T08:00:18Zen
dc.date.available2021-04-29T08:00:18Zen
dc.date.issued2021-04-28en
dc.identifier.othervt_gsexam:30005en
dc.identifier.urihttp://hdl.handle.net/10919/103165en
dc.description.abstractResearch shows that anger has a negative impact on cognition due to the rumination effect and in the context of driving, anger negatively impacts situation awareness, driving performance, and road safety. In-vehicle agents are capable of mitigating the effects of anger and subsequent effects on driving behavior. Language is another important aspect that influences information processing and human behavior during social interactions. This thesis aims to explore the effects of the language of in-vehicle agents on angry drivers' situation awareness, driving performance, and subjective perception. The three conditions explored are the native language agent condition (Hindi or Chinese), secondary language agent condition (English), and no agent condition. Results indicate that driving performance is better in the case of the native language agent condition when compared to the no agent condition. Higher levels of situational awareness were affected by the agent condition, favoring the native language condition over the secondary language condition. The participants preferred native language agents over the other conditions and the perceived workload was higher in the no-agent condition than the native agent condition. Drivers also expressed the need to control the state of the in-vehicle agent. The study results have practical design implications and the results are expected to help foster future work in this domain.en
dc.format.mediumETDen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAngry drivingen
dc.subjectIn-vehicle agentsen
dc.subjectNative language processingen
dc.subjectSituation awarenessen
dc.subjectDriving performanceen
dc.subjectPerceived workloaden
dc.titleExploring the Effects of Language on Angry Drivers' Situation Awareness, Driving Performance, and Subjective Perceptionen
dc.typeThesisen
dc.contributor.departmentComputer Scienceen
dc.description.degreeMaster of Scienceen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelmastersen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineComputer Science and Applicationsen
dc.contributor.committeechairJeon, Myounghoonen
dc.contributor.committeememberLuther, Kurten
dc.contributor.committeememberLee, Sang Wonen
dc.description.abstractgeneralPeople are deeply influenced by emotions. Anger while driving is shown to negatively impact people's perception and understanding of what is going on in the driving context and prediction about what will happen. As a result, this influences driving performance and road safety. Intelligent agents (such as Siri or Alexa) built into vehicles can help regulate the emotions of the drivers and can positively impact driving performance. Language is another important aspect that influences human behavior during social interactions. The current thesis aims to leverage the positive impacts of in-vehicle agents and language to design in-vehicle agent interactions capable of mitigating the negative effects of anger to ensure better driving performance and increased situation awareness. The three conditions explored are the native language agent condition (Hindi or Chinese), secondary language agent condition (English), and no agent condition. The effects on angry drivers' situation awareness, driving performance, and subjective perception are studied. Results indicate that the driving performance is better in the case of the native language agent condition when compared to the no agent condition. Participants preferred native language agents over the other conditions. People's understanding and prediction capability in the driving context was better in the native agent condition over the other conditions. The study results have practical design implications in designing in-vehicle agent interfaces and the results are expected to help foster future work.en


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