Title | ||
---|---|---|
In-Vehicle Affect Detection System: Identification of Emotional Arousal by Monitoring the Driver and Driving Style. |
Abstract | ||
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There have been clear needs to address the impact of driver emotions such as anger and happiness on aggressive or distracted driving behaviors. To tackle this issue, we have developed an affect detection system for identifying a driveru0027s emotional arousal, including the driveru0027s physiological data and vehicleu0027s kinematic data. Multimodal sensors are wirelessly connected to a smartphone and then, all the driver and driving data are displayed on our Android application in real-time. With the benefits of this multimodal, portable, non-intrusive, and cost-efficient system, subsequent experiments were designed to test and improve the system. After identifying significant features, various machine learning algorithms will be used to model a driveru0027s emotional states. Our final goal is to develop an optimized classifier of specific emotional states including arousal and valence. We hope that we can spark lively discussions on driver emotions at AutoUI and use the feedback to improve our system. |
Year | Venue | Field |
---|---|---|
2018 | AutomotiveUI (adjunct) | Arousal,Android (operating system),Spark (mathematics),Human–computer interaction,Happiness,Anger,Engineering,System identification,Classifier (linguistics),Distracted driving |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
3 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Eric Vasey | 1 | 6 | 2.97 |
Sangjin Ko | 2 | 0 | 1.35 |
Myounghoon Jeon | 3 | 113 | 36.51 |