Driving is a complicated task that requires the coordination of visual and sensory-motor skills. Unsafe driving behavior and accidents can happen regardless of the level of drivers’ experience. The main cause of the most of these accidents is human error. Emotions influence the way drivers process and react to internal or environmental factors. Specifically, anger elicited either from traffic or personal issues, is a serious threat on the road. Therefore, having an affective intelligent system in the car that can estimate drivers’ anger and respond to it appropriately can help drivers adapt to moment to-moment changes in driving situations. To this end, the present dissertation uses an integrated approach to monitoring drivers’ affective states in various driving contexts to address the question: “What types of music can mitigate the effects of anger on driving performance?” Three sources of information (behavioral, physiological, and subjective data) were considered in two experiments. In Experiment 1, three groups of participants were compared based on their emotional reactions and driving behaviors. Results showed that angry drivers who did not listen to music had riskier driving behavior than emotion neutral drivers. Results from heart rate, oxygenation level in prefrontal cortex, and self report questionnaires showed that music could help angry drivers react at the similar level to emotion-neutral drivers both internally and behaviorally. In Experiment 2, types of music emotion and familiarity of music were addressed to identify what kind of music an in-vehicle auditory system should play when it recognizes drivers’ anger. Results showed that different kinds of music did not effect driving performance. However, drivers experienced less frustration and effort when listening to music in general and less viii frustration when listening to self-selected music specifically. Regarding personality characteristics, drivers who had anger-expression out style had riskier driving behavior just as in Experiment 1. In conclusion, this research showed the benefits of music as a possible strategy to help angry drivers. In addition, important patterns were uncovered relating to assessing driver anger for possible affective intelligent systems in cars.
|Commitee:||Mueller, Shane, Steelman, Kelly, Wang, Dongyuan Debbie|
|School:||Michigan Technological University|
|Department:||Cognitive and Learning Sciences|
|School Location:||United States -- Michigan|
|Source:||DAI-B 80/06(E), Dissertation Abstracts International|
|Keywords:||Angry drivers, Distracted drivers, Fuel-efficient driving behavior, Heart rate variability, Music and emotion, fNIRS|
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