Towards Real-Time Detection and Mitigation of Driver Frustration using SVM

Driving in stressful and frustrating situations remains a common issue in daily traffic scenarios and has been shown to increase the risk for hazardous and aggressive driving style. Aiming to improve road safety with intelligent systems, frustration has to be detected continuously as well as robust and mitigation strategies must be applied effectively. Since both, the modeling of frustration over time as well as the design and timing of applications for frustration mitigation, are complex tasks, we divided this work in two parts: (1) A driving simulator experiment was conducted to collect a dataset and to validate a driving context related frustration induction method. With this dataset we developed a bimodal frustration detection for the driving context using a temporal support vector machine. The detection combines drivers' visual facial features with heart rate measurements and yields an accuracy of 88.7% (AUC of ROC). (2) We applied the real-time frustration detection in a second simulator study to evaluate two application scenarios for frustration mitigation, which include a frustration sensitive ambient light and an autonomous driving assistant. Both applications were examined for their frustration mitigating effect as well as in terms of user experience. The results provide a helpful basis to develop future intelligent frustration mitigation systems.